Information processing device and information processing program

Abstract

An information processing device according to one aspect of the present disclosure comprises a derivation unit that derives the cognitive resource of a user on the basis of variation in the time of user's reaction to a plurality of requests.

Description

[Technical Field]

[0001] This disclosure relates to an information processing device and an information processing program. [Background technology]

[0002] It is difficult to easily determine a person's objective cognitive capacity (cognitive resources) while they are performing a task. Therefore, traditionally, judgments about a person's state have been subjective, both by themselves and others, or determined without evaluating the validity of their physiological state. This is because, while people change their behavior in response to environmental stress, the relationship between environmental stress and brain function is not well understood. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2019-000457 [Overview of the project]

[0004] For example, in the invention described in Patent Document 1, when a series of answers are required, the task is determined by evaluating the difference between the actual reaction time and the correct reaction time only when there is a correct answer within the reaction time. Therefore, in the invention described in Patent Document 1, the task cannot be determined when there is no correct answer within the reaction time. Accordingly, it is desirable to provide an information processing device and information processing program that can determine the task regardless of whether there is a reaction time or not, or regardless of whether there is a correct answer within the reaction time.

[0005] The information processing device relating to the first aspect of this disclosure includes a derivation unit that derives the user's cognitive resource based on the variability of the user's response times to multiple requests.

[0006] In the information processing device relating to the first aspect of this disclosure, the user's cognitive capacity is derived based on the variability of the user's reaction times in response to multiple requests. This makes it possible to determine a task for the user using the derived cognitive capacity. Here, the Distributor has experimentally obtained the finding that the variability of reaction times changes depending on the task. Therefore, it is possible to determine a task for the user based on the cognitive capacity derived from the variability of reaction times.

[0007] The information processing device relating to the second aspect of this disclosure includes a derivation unit that derives the user's cognitive capacity based on the user's biosignals in response to a request.

[0008] In the information processing device relating to the second aspect of this disclosure, the user's cognitive capacity is derived based on the user's biosignals in response to a request. This makes it possible to determine a task for the user using the derived cognitive capacity. Here, the Distributor has experimentally obtained the finding that the user's biosignals change depending on the task. Therefore, it is possible to determine a task for the user based on the cognitive capacity derived from the user's biosignals.

[0009] The information processing device relating to the third aspect of this disclosure comprises a characteristic value generation unit, an evaluation value generation unit, and a derivation unit. The characteristic value generation unit generates characteristic values ​​for each observed waveform based on multiple partial observation data with observation periods shorter than the observation period of the observed data, which are included in each observation data obtained from a user's biological observation over a predetermined period. The evaluation value generation unit generates evaluation values ​​for the differences between the observed data relating to the observed waveform based on the characteristic values ​​for each observed data generated by the characteristic value generation unit. The derivation unit derives the user's cognitive capacity based on the evaluation values ​​generated by the evaluation value generation unit.

[0010] In the information processing device relating to the third aspect of this disclosure, characteristic values ​​are derived from each observation data obtained through biological observation of the user over a predetermined period, and evaluation values ​​for the differences between observation data regarding the observed waveform are generated based on the derived characteristic values ​​for each observation data. Then, the user's cognitive capacity is derived based on the generated evaluation values. Here, the Discloser has experimentally obtained the finding that the above evaluation values ​​change depending on the task. Therefore, it is possible to determine the task for the user based on the cognitive capacity derived from the above evaluation values.

[0011] The information processing program relating to the fourth aspect of this disclosure causes a computer to derive a user's cognitive capacity based on the variability of the user's response times to multiple requests.

[0012] In the information processing program relating to the fourth aspect of this disclosure, the user's cognitive capacity is derived based on the variability of the user's response times to multiple requests. Here, the Discloser has experimentally obtained the finding that the variability of response times changes depending on the task. Therefore, it is possible to determine the task for the user based on the cognitive capacity derived from the variability of response times.

[0013] The information processing program relating to the fifth aspect of this disclosure causes a computer to derive the user's cognitive capacity based on fluctuations in the user's biological signals in a specific frequency band in response to a request.

[0014] In the information processing device program relating to the fifth aspect of this disclosure, the user's cognitive capacity is derived based on fluctuations in the user's biological signals in a specific frequency band in response to a request. Here, the Distributor has experimentally obtained the finding that fluctuations in the user's biological signals in a specific frequency band change depending on the task. Therefore, it is possible to determine the task for the user based on the cognitive capacity derived from the fluctuations in the user's biological signals in a specific frequency band.

[0015] The information processing program according to the sixth aspect of the present disclosure causes a computer to execute the following three operations. (1) Generating, for each observation data, a characteristic value of an observation target waveform based on a plurality of partial observation data having an observation period shorter than the observation period of the observation data, which are included in each observation data obtained by observing a user's biological information for a predetermined period (2) Generating an evaluation value regarding the difference between observation data for an observation target waveform based on the characteristic values for each generated observation data (3) Deriving a user's cognitive capacity based on the generated evaluation value

[0016] In the information processing program according to the sixth aspect of the present disclosure, a characteristic value for each observation data is derived from each observation data obtained by observing a user's biological information for a predetermined period, and an evaluation value regarding the difference between observation data for an observation target waveform is generated based on the derived characteristic values for each observation data. Then, a user's cognitive capacity is derived based on the generated evaluation value. Here, the present inventor has obtained, through experiments, the finding that the above-described evaluation value changes depending on the task. Therefore, a user's cognitive capacity can be derived based on the above-described evaluation value, and a task for the user can be determined based on the derived cognitive capacity.

[0017] The information processing apparatus according to the seventh aspect of the present disclosure includes a changing unit that changes a task for a user based on the variation in the user's response time for a plurality of requests.

[0018] In the information processing apparatus according to the seventh aspect of the present disclosure, a task for a user is changed based on the variation in the user's response time corresponding to a plurality of requests. Here, the present inventor has obtained, through experiments, the finding that the variation in the response time changes depending on the task. Therefore, it becomes possible to change a task for a user based on the variation in the response time.

[0019] The information processing apparatus according to the eighth aspect of the present disclosure includes a changing unit that changes a task for a user based on the fluctuation of the user's biological signal.

[0020] In the information processing apparatus according to the eighth aspect of the present disclosure, the task for the user is changed based on the fluctuation of the user's biological signal with respect to the request. Here, the present inventor obtained the knowledge that the user's biological signal changes depending on the task through experiments. Therefore, it is possible to change the task for the user based on the fluctuation of the user's biological signal.

Brief Description of the Drawings

[0021] [Figure 1] It is a diagram showing an example of the response time (reaction time) for a large number of consecutive low-difficulty problems. [Figure 2] It is a diagram showing an example of the response time (reaction time) for a large number of consecutive high-difficulty problems. [Figure 3] It is a diagram showing an example of the power spectral density of the waveform in FIG. 1. [Figure 4] It is a diagram showing an example of the power spectral density of the waveform in FIG. 2. [Figure 5] It is a diagram showing an example of the relationship between the task difference in the variation of the reaction time and the task difference in the peak value of the power of the brain wave in the low-frequency band. [Figure 6] It is a diagram showing an example of the relationship between the task difference in the variation of the reaction time and the task difference in the correct answer rate. [Figure 7] It is a diagram showing an example of the relationship between the task difference in the peak value of the power of the brain wave in the low-frequency band and the task difference in the correct answer rate. [Figure 8] It is a diagram showing an example of the relationship between the task difference in arousal level and the task difference in the peak value of the power of the brain wave in the low-frequency band. [Figure 9] It is a diagram showing an example of the relationship between the task difference in arousal level and the task difference in the correct answer rate. [Figure 10] It is a diagram showing an example of the relationship between the variation of the reaction time and the task difference in the peak value of the power of the brain wave in the low-frequency band. [Figure 11] It is a diagram showing an example of the relationship between the variation of the reaction time and the correct answer rate. [Figure 12] It is a diagram showing an example of the relationship between arousal level and the correct answer rate. [Figure 13] This figure illustrates an example of the relationship between the variability in reaction time across different tasks and the accuracy rate. [Figure 14] This figure shows an example of the schematic configuration of an information processing device according to the first embodiment of this disclosure. [Figure 15] Figure 14 is a flowchart illustrating an example of the procedure for changing the difficulty level in an information processing device. [Figure 16] Figure 14 is a flowchart illustrating an example of the procedure for deriving a regression equation in an information processing device. [Figure 17] This figure shows a modified example of the schematic configuration of the information processing device shown in Figure 14. [Figure 18] Figure 14 is a conceptual diagram showing an example of a regression table. [Figure 19] This figure shows a modified example of the schematic configuration of the information processing device shown in Figure 14. [Figure 20] Figure 19 is a flowchart illustrating an example of the procedure for changing the difficulty level in an information processing device. [Figure 21] Figure 19 is a flowchart illustrating an example of the procedure for deriving a regression equation in an information processing device. [Figure 22] This figure shows a modified example of the schematic configuration of the information processing device shown in Figure 14. [Figure 23] Figure 22 is a flowchart illustrating an example of the procedure for changing the difficulty level in an information processing device. [Figure 24] Figure 22 is a flowchart illustrating an example of the procedure for deriving a regression equation in an information processing device. [Figure 25] This figure shows an example of a schematic configuration of an information processing device according to the second embodiment of this disclosure. [Figure 26] Figure 25 is a flowchart illustrating an example of the procedure for changing the difficulty level in an information processing device. [Figure 27] Figure 25 is a flowchart illustrating an example of the procedure for deriving a regression equation in an information processing device. [Figure 28] This figure shows an example of a schematic configuration of an information processing device according to the third embodiment of this disclosure. [Figure 29] Figure 28 is a flowchart illustrating an example of the procedure for changing the difficulty level in an information processing device. [Figure 30] This figure shows an example of a schematic configuration of an information processing device according to the fourth embodiment of this disclosure. [Figure 31] Figure 30 is a schematic diagram illustrating an example of the learning procedure for a learning model in an information processing device. [Figure 32] Figure 30 is a schematic diagram illustrating an example of a state estimation procedure using a learning model in an information processing device. [Figure 33] Figure 30 is a flowchart illustrating an example of the procedure for changing the difficulty level in an information processing device. [Figure 34] Figure 30 is a flowchart illustrating an example of the procedure for deriving a regression equation in an information processing device. [Figure 35] This figure shows a modified example of the schematic configuration of the information processing device shown in Figure 30. [Figure 36] Figure 30 is a flowchart illustrating an example of the procedure for changing the difficulty level in an information processing device. [Figure 37] Figure 30 is a flowchart illustrating an example of the procedure for deriving a regression equation in an information processing device. [Figure 38] This figure shows a modified example of the schematic configuration of the information processing device shown in Figure 14. [Figure 39] Figure 38 is a flowchart illustrating an example of the procedure for changing the difficulty level in an information processing device. [Figure 40] Figure 38 is a flowchart illustrating an example of the procedure for deriving a regression equation in an information processing device. [Figure 41] This diagram shows an example of a head-mounted display equipped with sensors. [Figure 42] This is a diagram illustrating an example of a headband equipped with sensors. [Figure 43] This is a diagram illustrating an example of headphones equipped with sensors. [Figure 44] This diagram shows an example of earphones equipped with a sensor. [Figure 45] This is a diagram illustrating an example of a watch equipped with a sensor. [Figure 46] This is a diagram illustrating an example of eyeglasses equipped with sensors. [Figure 47]This figure shows an example of a schematic configuration of an information processing device according to the fifth embodiment of this disclosure. [Figure 48] This figure illustrates an example of the relationship between the difference in PNN50 tasks for pulse wave analysis and the accuracy rate. [Figure 49] This figure illustrates an example of the relationship between the variability of the pulse wave pnn50 and the accuracy rate. [Figure 50] This figure illustrates an example of the relationship between the power difference of the PNN50 for low-frequency pulse waves and the accuracy rate. [Figure 51] This figure illustrates an example of the relationship between the task difference in pulse wave rmssd and the accuracy rate. [Figure 52] This figure illustrates an example of the relationship between the variability of pulse wave rmssd and accuracy. [Figure 53] This diagram illustrates an example of the relationship between the power difference of RMSSD in low-frequency pulse waves and the accuracy rate. [Figure 54] This figure illustrates an example of the relationship between the variability in the number of SCRs in psychogenic sweating and the accuracy rate. [Figure 55] This figure illustrates an example of the relationship between the number of SCRs in psychoactive sweating tasks and the accuracy rate. [Figure 56] This figure shows a modified example of the schematic configuration of the information processing device shown in Figure 25. [Figure 57] This figure shows a modified example of the schematic configuration of the information processing device shown in Figure 28. [Figure 58] This figure shows a modified example of the schematic configuration of the information processing device shown in Figure 30. [Figure 59] This figure shows a modified example of the schematic configuration of the information processing device shown in Figure 47. [Figure 60] This figure shows an example where some of the functions of the information processing device in Figure 14 are implemented in a server device. [Figure 61] This figure shows an example where some of the functions of the information processing device in Figure 25 are implemented in a server device. [Figure 62] This figure illustrates an example where some of the functions of the information processing device shown in Figure 28 are implemented in a server device. [Figure 63]It is a diagram showing an example in which a part of the functions of the information processing apparatus in FIG. 30 is provided in the server apparatus. [Figure 64] It is a diagram showing an example in which a part of the functions of the information processing apparatus in FIG. 56 is provided in the server apparatus. [Figure 65] It is a diagram showing a modified example of the schematic configuration of the information processing system in FIG. 57. [Figure 66] It is a diagram showing a modified example of the schematic configuration of the information processing system in FIG. 58. [Figure 67] It is a diagram showing game data that can be replaced with the problem data in FIGS. 14, 17, 19, 28, 30, 47, 56 to 66. [Figure 68] It is a diagram showing a modified example of the information processing apparatus in FIGS. 14, 17, 19, 22, and 38. [Figure 69] It is a diagram showing a modified example of the information processing apparatus in FIGS. 25, 28, 30, and 35. [Figure 70] It is a diagram showing a modified example of the information processing apparatus in FIGS. 56 to 59. [Figure 71] It is a diagram showing a modified example of the information processing apparatus in FIG. 47. [Figure 72] It is a diagram showing a modified example of the information processing apparatus in FIG. 47. [Figure 73] It is a diagram showing a modified example of the information processing apparatus in FIG. 60. [Figure 74] It is a diagram showing a modified example of the information processing apparatus in FIGS. 61 to 63. [Figure 75] It is a diagram showing a modified example of the information processing apparatus in FIGS. 64 to 66. [Figure 76] It is a diagram showing an example of the relationship between the problem difference of the median reaction time and the correct answer rate. [Figure 77] It is a diagram showing an example of the relationship between the arousal level and the correct answer rate.

Embodiments for Carrying Out the Invention

[0022] Hereinafter, embodiments for implementing the present disclosure will be described in detail with reference to the drawings. The description will be made in the following order. 1. Control of cognitive capacity in this disclosure (Figures 1-13) 2. First Embodiment (Figures 14-16) An example of using reaction time variability to derive a user's cognitive capacity. 3. Modified form of the first embodiment Modification A: An example using a regression table instead of a regression equation. (Figures 17 and 18) Modification B: An example using a different regression equation (Figures 19-21) Modification C: An example using a different regression equation (Figures 22-24) Modification D: An example of deriving the cognitive capacity of a group. 4. Second Embodiment (Figures 25-27) An example of using slow fluctuations in brainwaves to derive a user's cognitive capacity. 5. Modified form of the second embodiment An example using a regression table instead of a regression equation. An example of deriving the cognitive capacity of a group. 6. Third Embodiment (Figures 28 and 29) In deriving the user's cognitive capacity, the variability of reaction time and, An example utilizing slow fluctuations in brainwaves 7. Modified form of the third embodiment An example of deriving the cognitive capacity of a group. 8. Fourth Embodiment (Figures 30-34) To derive the user's cognitive capacity, a learning model that derives arousal level is used. Examples of use 9. Modified form of the fourth embodiment Modification E: An example using a different regression equation (Figures 35-37) Modification F: An example of deriving the cognitive capacity of a group. 10. Modified form of the first embodiment Modification G: An example using a different regression equation (Figures 38-40) 11. Regarding the biometric information that allows control of cognitive capacity as disclosed herein (Figures 41-46) 12. Fifth Embodiment The user's cognitive capacity is derived using pulse wave, electrocardiogram, blood flow, and psychogenic sweating. Examples using this method (Figures 47-55) 13. Modified form of the fifth embodiment An example of deriving the cognitive capacity of a group. 14. Modified Examples of the First to Fifth Embodiments Modification H: An example in which the electroencephalogram detection unit is provided as a separate component (Figures 56-59) Variations I-O: Examples of deriving cognitive capacity using a server device (Figures 60-66) Modified versions P and Q: Examples of applying this disclosure to game data (Figure 67) Modified example R: Example of recording user behavior (Figures 68-75) Examples using different regression equations (Figures 76 and 77)

[0023] <1. Regarding the control of cognitive capacity in this disclosure> Figures 1 and 2 are graphs showing the time it took a user to answer (reaction time) when solving a large number of problems consecutively. Figure 1 shows the graph when solving problems of relatively low difficulty, and Figure 2 shows the graph when solving problems of relatively high difficulty. Figure 3 is the power spectrum density obtained by performing an FFT (Fast Fourier Transform) on the observed brainwave (alpha wave) data of the user when solving a large number of low-difficulty problems consecutively. Figure 4 is the power spectrum density obtained by performing an FFT on the observed brainwave (alpha wave) data of the user when solving a large number of high-difficulty problems consecutively. Figures 3 and 4 show graphs obtained by measuring brainwaves (alpha waves) in segments of approximately 20 seconds and performing an FFT in an analysis window of approximately 200 seconds.

[0024] Figures 1 and 2 show that when solving high-difficulty problems, reaction times are not only longer, but the variability of reaction times is also greater compared to solving low-difficulty problems. Figures 3 and 4 show that when solving high-difficulty problems, the power of brainwaves (alpha waves) around 0.01 Hz is greater, and the power of brainwaves (alpha waves) around 0.02 to 0.04 Hz is smaller compared to solving low-difficulty problems. In this specification, the power of brainwaves (alpha waves) around 0.01 Hz will be appropriately referred to as "slow (low-frequency band) brainwave (alpha wave) fluctuations."

[0025] Figure 5 shows the task difference Δtv[s] in the variability of user reaction times (75th percentile - 25th percentile) when solving high-difficulty problems and low-difficulty problems, and the task difference ΔP[(mV)] in the peak power value of the user's slow brainwaves (alpha waves) when solving high-difficulty problems and low-difficulty problems. 2 (Hz) 2 This illustrates an example of the relationship with [ / Hz]. The task difference Δtv[s] is obtained by subtracting the variation in user reaction time when solving low-difficulty and high-difficulty problems from the variation in user reaction time when solving high-difficulty problems. The task difference ΔP is obtained by subtracting the peak value of the user's slow brainwave (alpha wave) power when solving low-difficulty and high-difficulty problems from the peak value of the user's slow brainwave (alpha wave) power when solving high-difficulty problems.

[0026] Figure 6 shows an example of the relationship between the task difference Δtv[s] of the variability in user reaction time (75th percentile - 25th percentile) when solving high-difficulty problems and low-difficulty problems, and the task difference ΔR[%] of the accuracy rate when solving high-difficulty problems and low-difficulty problems. The task difference ΔR is obtained by subtracting the accuracy rate when solving low- and high-difficulty problems from the accuracy rate when solving high-difficulty problems.

[0027] Figures 5 and 6 plot data for each user, and the overall characteristics of the users are represented by a regression equation (regression line). In Figure 5, the regression equation is given by ΔP = a1 × Δtv + b1, and in Figure 6, the regression equation is given by ΔR = a2 × Δtv + b2.

[0028] A small task difference Δtv for reaction time variability means that there is little difference in reaction time variability between solving high-difficulty problems and low-difficulty problems. Users who achieve this result tend to be able to solve problems within a certain range of reaction time, regardless of the difficulty level. On the other hand, a large task difference Δtv for reaction time variability means that there is a large difference in reaction time variability between solving high-difficulty problems and low-difficulty problems. Users who achieve this result tend to experience significant variability in the time it takes to solve problems as the difficulty level increases.

[0029] Figure 5 shows that when the task difference Δtv for reaction time variability is small, the task difference ΔP for the peak power value of slow EEG (alpha waves) increases in the positive direction, and when the task difference Δtv for reaction time variability is large, the task difference ΔP for the peak power value of slow EEG (alpha waves) decreases. From this, it can be seen that people who can answer difficult problems with the same reaction time as easy problems tend to have a task difference ΔP for the peak power value of slow EEG (alpha waves) that increases in the positive direction. Conversely, people whose reaction time variability increases with difficult problems tend to have a task difference ΔP for the peak power value of slow EEG (alpha waves) that does not change much regardless of the difficulty of the problem.

[0030] Figure 6 shows that when the task difference Δtv for reaction time variability is large, the task difference ΔR for the accuracy rate of the problem increases in the negative direction, and when the task difference Δtv for reaction time variability is small, the task difference ΔR for the accuracy rate of the problem decreases. From this, it can be seen that people who have a large variability in reaction time on difficult problems tend to have a large negative task difference ΔR for the accuracy rate (i.e., their accuracy rate on difficult problems decreases). Conversely, people who have a small variability in reaction time even on difficult problems tend to have a small task difference ΔR for the accuracy rate (i.e., they can answer difficult problems as accurately as easy problems).

[0031] From the above, it can be inferred that when the task difference Δtv of reaction time variability is large, the user's cognitive capacity (cognitive resource) is lower than a predetermined standard. Cognitive capacity refers to abilities that encompass executive function, execution efficiency, working memory, etc., and is academically known as cognitive resource. In this disclosure, cognitive capacity is also referred to as cognitive load tolerance.

[0032] Furthermore, when the task difference Δtv for reaction time variability is small, it can be inferred that the user's cognitive capacity is higher than a predetermined standard. If the user's cognitive capacity is lower than a predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than a predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0033] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δtv for reaction time variability is large, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δtv for reaction time variability is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0034] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference Δtv of reaction time variability and the regression equation in Figure 5 or Figure 6.

[0035] Figure 7 shows the task difference ΔP[(mV)] between solving high-difficulty problems and solving low-difficulty problems, specifically the peak power value of the user's slow brainwaves (alpha waves). 2 (Hz) 2 This shows an example of the relationship between [ / Hz] and the difference in problem-solving accuracy ΔR[%] between solving high-difficulty problems and solving low-difficulty problems. Figure 7 plots data for each user, and the characteristics of all users are represented by a regression equation (regression line). In Figure 7, the regression equation is expressed as ΔR=a3×ΔP+b3.

[0036] Figure 7 shows that when the task difference ΔP of the peak power value of slow brainwaves (alpha waves) is small (around 0), the task difference ΔR of the accuracy rate increases in the negative direction (i.e., the accuracy rate of difficult problems decreases). Conversely, when the task difference ΔP of the peak power value of slow brainwaves (alpha waves) is large in the positive direction (for example, around 0.4), the task difference ΔR of the accuracy rate decreases (i.e., difficult problems can be answered as accurately as easy problems).

[0037] From the above, it can be inferred that when the task difference ΔP of the peak power value of slow brainwaves (alpha waves) is small, the user's cognitive capacity is lower than a predetermined standard. Conversely, when the task difference ΔP of the peak power value of slow brainwaves (alpha waves) is large in the positive direction, it can be inferred that the user's cognitive capacity is higher than a predetermined standard.

[0038] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference ΔP of the peak power values ​​of slow brainwaves (alpha waves) is small, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference ΔP of the peak power values ​​of slow brainwaves (alpha waves) is large in the positive direction, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0039] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference ΔP of the peak power value of slow brainwaves (alpha waves) and the regression equation in Figure 7.

[0040] Figure 8 shows the task difference Δk[%] in user arousal level when solving high-difficulty problems versus low-difficulty problems, and the task difference ΔP[(mV)] in the peak power value of the user's slow brainwaves (alpha waves) when solving high-difficulty problems versus low-difficulty problems. 2 (Hz) 2This shows an example of the relationship with [ / Hz]. Figure 9 shows an example of the relationship between the task difference Δk[%] of user arousal level when solving high-difficulty problems and low-difficulty problems, and the task difference ΔR[%] of the problem accuracy when solving high-difficulty problems and low-difficulty problems. The task difference Δk[%] is obtained by subtracting the user arousal level when solving low- and high-difficulty problems from the user arousal level when solving high-difficulty problems. The arousal level is obtained by using the arousal level estimation model described later.

[0041] Figures 8 and 9 plot data for each user, and the overall characteristics of the users are represented by a regression equation (regression line). In Figure 8, the regression equation is given by ΔP = a₁₀ × Δk + b₁₀, and in Figure 9, the regression equation is given by ΔR = a₁₀ × Δk + b₁₀.

[0042] A small difference in arousal level Δk means that there is little difference in arousal level when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to be able to solve problems with a consistent level of arousal regardless of their difficulty. On the other hand, a large difference in arousal level Δk means that there is a large difference in arousal level when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to become more aroused as the difficulty of the problems increases.

[0043] Figure 8 shows that when the task difference Δk of arousal level is small, the task difference ΔP of the peak power value of slow brainwaves (alpha waves) increases in the positive direction, and when the task difference Δk of arousal level is large, the task difference ΔP of the peak power value of slow brainwaves (alpha waves) decreases. From this, it can be seen that people who can answer difficult problems with the same level of arousal as easy problems tend to have a task difference ΔP of the peak power value of slow brainwaves (alpha waves) that increases in the positive direction. Conversely, it can be seen that people who become highly aroused by difficult problems tend to have a task difference ΔP of the peak power value of slow brainwaves (alpha waves) that does not change much regardless of the difficulty of the problem.

[0044] Figure 9 shows that when the task difference Δk of arousal level is large, the task difference ΔR of the correct answer rate becomes larger in the negative direction, and when the task difference Δk of arousal level is small, the task difference ΔR of the correct answer rate becomes smaller. From this, it can be seen that people who are highly aroused by difficult problems tend to have a larger negative task difference ΔR of the correct answer rate (i.e., their correct answer rate for difficult problems decreases). Conversely, people who are less aroused even by difficult problems tend to have a smaller task difference ΔR of the correct answer rate (i.e., they can answer difficult problems as correctly as easy problems).

[0045] From the above, when the difference in arousal level between tasks Δk is large, it can be inferred that the user's cognitive capacity is lower than the predetermined standard. Conversely, when the difference in arousal level between tasks Δk is small, it can be inferred that the user's cognitive capacity is higher than the predetermined standard. If the user's cognitive capacity is lower than the predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the workload is high). On the other hand, if the user's cognitive capacity is higher than the predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the workload is low).

[0046] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δk of arousal is large, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δk of arousal is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0047] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference Δk in arousal level and the regression equation in Figure 8 or Figure 9.

[0048] Figure 10 shows an example of the relationship between the variability of user reaction times (75% percentile - 25% percentile) tv[s] when solving high-difficulty problems and the task difference ΔR[%] in the accuracy rate of solving high-difficulty problems versus low-difficulty problems. The task difference ΔR is obtained by subtracting the accuracy rate when solving low- and high-difficulty problems from the accuracy rate when solving high-difficulty problems. In Figure 10, data for each user is plotted, and the characteristics of all users are represented by a regression equation (regression line). In Figure 10, the regression equation is expressed as ΔR = a6 × tv + b6.

[0049] A small variation in reaction time (tv) means that high-difficulty problems were solved in roughly the same amount of time. For users who achieve this result, the difficulty of the problems can be said to be not very high. On the other hand, a large variation in reaction time (tv) means that there is a large variation in the time it takes to solve high-difficulty problems. For users who achieve this result, the difficulty of the problems can be said to be relatively high.

[0050] Figure 10 shows that when the reaction time variability tv is large, the task difference ΔR of the accuracy rate of the problem increases in the negative direction, and when the reaction time variability tv is small, the task difference ΔR of the accuracy rate of the problem decreases. From this, it can be seen that people who have a large variation in reaction time on difficult problems tend to have a large negative difference ΔR of the accuracy rate (i.e., their accuracy rate on difficult problems decreases). Conversely, people who have a small variation in reaction time even on difficult problems tend to have a small task difference ΔR of the accuracy rate (i.e., they can answer difficult problems as correctly as easy problems).

[0051] From the above, it can be inferred that when the variation in reaction time tv is large, the user's cognitive capacity is lower than a predetermined standard. Conversely, when the variation in reaction time tv is small, it can be inferred that the user's cognitive capacity is higher than a predetermined standard. If the user's cognitive capacity is lower than a predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than a predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0052] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the variability in reaction time (tv) is large, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the variability in reaction time (tv) is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0053] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the reaction time variability tv and the regression equation in Figure 10.

[0054] Figure 11 shows an example of the relationship between the variability of user reaction times (75% percentile - 25% percentile) tv[s] when solving high-difficulty problems and the accuracy rate R[%] when solving high-difficulty problems. In Figure 11, data for each user is plotted, and the characteristics of all users are represented by a regression equation (regression line). In Figure 11, the regression equation is expressed as R = a7 × tv + b7.

[0055] A small variation in reaction time (tv) means that high-difficulty problems were solved in roughly the same amount of time. For users who achieve this result, the difficulty of the problems can be said to be not very high. On the other hand, a large variation in reaction time (tv) means that there is a large variation in the time it takes to solve high-difficulty problems. For users who achieve this result, the difficulty of the problems can be said to be relatively high.

[0056] Figure 11 shows that when the variability of reaction time tv is large, the accuracy rate R for high-difficulty problems is low, and when the variability of reaction time tv is small, the accuracy rate R for high-difficulty problems is large. From this, it can be seen that people with large variability in reaction time for difficult problems tend to have a lower accuracy rate R for difficult problems. Conversely, people with small variability in reaction time even for difficult problems tend to have a higher accuracy rate R (that is, they can answer difficult problems as correctly as easy problems).

[0057] From the above, it can be inferred that when the variation in reaction time tv is large, the user's cognitive capacity is lower than a predetermined standard. Conversely, when the variation in reaction time tv is small, it can be inferred that the user's cognitive capacity is higher than a predetermined standard. If the user's cognitive capacity is lower than a predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than a predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0058] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the variability in reaction time (tv) is large, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the variability in reaction time (tv) is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0059] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the reaction time variability tv and the regression equation in Figure 11.

[0060] Figure 12 shows an example of the relationship between the user's level of arousal k[%] when solving high-difficulty problems and the accuracy rate R[%] of solving high-difficulty problems. In Figure 12, data for each user is plotted, and the characteristics of all users are represented by a regression equation (regression line). In Figure 12, the regression equation is expressed as R = a⁸ × k + b⁸.

[0061] A low arousal level k means that the user's arousal level is low when solving high-difficulty problems. For users who achieve this result, the difficulty level of the problems can be said to be not very high. On the other hand, a high arousal level k means that the user's arousal level is high when solving high-difficulty problems. For users who achieve this result, the difficulty level of the problems can be said to be relatively high.

[0062] Figure 12 shows that when the level of arousal k is high, the accuracy rate R of the problem is low, and when the level of arousal k is low, the accuracy rate of the problem is high. From this, it can be seen that people who are highly aroused by difficult problems tend to have a low accuracy rate R (i.e., their accuracy rate on difficult problems decreases). Conversely, people who are highly aroused even by difficult problems tend to have a high accuracy rate R (i.e., they can answer difficult problems as accurately as easy problems).

[0063] From the above, it can be inferred that when the level of arousal k is high, the user's cognitive capacity is lower than a predetermined standard. Conversely, when the level of arousal k is low, it can be inferred that the user's cognitive capacity is higher than a predetermined standard. If the user's cognitive capacity is lower than a predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than a predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0064] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the level of arousal k is high, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the level of arousal k is low, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0065] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the arousal level k and the regression equation in Figure 12.

[0066] Figure 13 shows an example of the relationship between the task difference Δtv[s] (75th percentile - 25th percentile) of user reaction time variability when solving high-difficulty problems and low-difficulty problems, and the problem accuracy R[%] when solving high-difficulty problems. In Figure 13, data for each user is plotted, and the characteristics of all users are represented by a regression equation (regression line). In Figure 13, the regression equation is expressed as R = a⁹ × Δtv + b⁹.

[0067] A small task difference Δtv for reaction time variability means that there is little difference in reaction time variability between solving high-difficulty problems and low-difficulty problems. Users who achieve this result tend to be able to solve problems within a certain range of reaction time, regardless of the difficulty level. On the other hand, a large task difference Δtv for reaction time variability means that there is a large difference in reaction time variability between solving high-difficulty problems and low-difficulty problems. Users who achieve this result tend to experience significant variability in the time it takes to solve problems as the difficulty level increases.

[0068] Figure 13 shows that when the difference in reaction time variability Δtv is large, the accuracy rate R is low, and when the difference in reaction time variability Δtv is small, the accuracy rate R is high. From this, it can be seen that people who have a large variation in reaction time on difficult problems tend to have a low accuracy rate R (i.e., their accuracy on difficult problems decreases). Conversely, people who have a small variation in reaction time even on difficult problems tend to have a high accuracy rate R (i.e., they can answer difficult problems as correctly as easy problems).

[0069] From the above, when the task difference Δtv for reaction time variability is large, it can be inferred that the user's cognitive capacity is lower than a predetermined standard. Conversely, when the task difference Δtv for reaction time variability is small, it can be inferred that the user's cognitive capacity is higher than a predetermined standard. If the user's cognitive capacity is lower than a predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than a predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0070] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δtv for reaction time variability is large, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δtv for reaction time variability is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0071] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference Δtv, which represents the variability of reaction times, and the regression equation in Figure 13.

[0072] <2. First Embodiment> [composition] An information processing device 1 according to the first embodiment of this disclosure will now be described. Figure 14 shows an example of the schematic configuration of the information processing device 1 according to this embodiment. The information processing device 1 includes an input receiving unit 10, a storage unit 20, a signal processing unit 30, a stimulus control unit 40, and a stimulus presentation unit 50. The signal processing unit 30 corresponds to a specific example of the "acquisition unit," "determination unit," and "derivation unit" of this disclosure. The stimulus presentation unit 50 corresponds to a specific example of the "presentation unit" of this disclosure.

[0073] The input receiving unit 10 receives input from the user and outputs it to the signal processing unit 30. User input can include, for example, the user's response to a stimulus presented by the stimulus presentation unit 50. For example, if the stimulus presented by the stimulus presentation unit 50 is a question presented using video (still image or video), sound, or light, the user's response can be to input an answer corresponding to the question data 22 (described later) to the input receiving unit 10. At this time, the input receiving unit 10 receives the user input as an answer corresponding to the question data 22 (described later) and outputs the received answer to the signal processing unit 30. The input receiving unit 10 is composed of, for example, an input interface such as a keyboard, mouse, or touch panel.

[0074] The memory unit 20 is, for example, a volatile memory such as DRAM (Dynamic Random Access Memory), or a non-volatile memory such as EEPROM (Electrically Erasable Programmable Read-Only Memory) or flash memory. The memory unit 20 stores an information processing program 21 that controls the user's cognitive capacity, problem data 22 used in the information processing program 21, a regression equation 23, and a difficulty level 24. The regression equation 23 corresponds to one specific example of the "regression data" in this disclosure. Furthermore, the memory unit 20 stores the reaction time 25 obtained by processing by the information processing program 21. The information processing program 21 will be described in detail later.

[0075] Problem data 22 includes multiple problem data sets of varying difficulty levels. Problem data are learning problems and correspond to specific examples of "requests" and "tasks" in this disclosure. Problem data 22 also includes data on the difficulty level of each problem data set included in problem data 22. Problem data 22 may further include correct answer data for each problem data set. Regression equation 23 is, for example, the regression equation shown in Figure 5 or Figure 6. Reaction time 25 is, for example, the time taken from the presentation of the question to the input of the answer. Reaction time 25 is, for example, the time taken from the presentation of a stimulus from the stimulus presentation unit 50 to the input receiving unit 10 receiving the user's response to the stimulus presented from the stimulus presentation unit 50 (for example, the timing of the user's answer input).

[0076] Difficulty level 24 includes, for example, setting data for the difficulty level of the questions presented to the user, and a table describing the correspondence between the difficulty level of the questions and the user's cognitive capacity. The setting data included in difficulty level 24 includes multiple difficulty levels as initial values, or multiple difficulty levels after being modified by the information processing program 21. The table included in difficulty level 24 sets difficulty levels according to the user's cognitive capacity. For example, if the user's cognitive capacity is α, the table included in difficulty level 24 sets multiple difficulty levels according to cognitive capacity α. Because multiple difficulty levels are set according to cognitive capacity α, the information processing program 21 is able to present the user with questions of multiple difficulty levels.

[0077] The signal processing unit 30 is composed of, for example, a processor. The signal processing unit 30 executes an information processing program 21 stored in the memory unit 20. The functions of the signal processing unit 30 are realized, for example, by the execution of the information processing program 21 by the signal processing unit 30. For example, the signal processing unit 30 reads out multiple problem data of different difficulty levels corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. For example, when the signal processing unit 30 obtains an answer corresponding to the problem data 22 from the input receiving unit 10, it derives a reaction time 25 based on the input timing of the obtained answer. For example, when the signal processing unit 30 has completed the presentation of a predetermined number of problems N, it calculates the task difference Δtv of the variation in reaction time 25. For example, the signal processing unit 30 derives cognitive capacity based on the calculated task difference Δtv and the regression equation 23 read from the memory unit 20. For example, the signal processing unit 30 determines the difficulty level of the problems to be presented in subsequent sessions based on the derived cognitive capacity. The signal processing unit 30 determines the difficulty level of the questions to be asked in subsequent sessions, for example, based on a table included in the difficulty level 24 that has been read from the memory unit 20. The signal processing unit 30 sets the difficulty level of the questions to be asked in subsequent sessions by, for example, writing the determined difficulty level to the setting data of the memory unit 20.

[0078] The stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 based on the problem data input from the signal processing unit 30. The stimulus control unit 40 outputs the generated control signal to the stimulus presentation unit 50. If the stimulus presentation unit 50 is a display panel, the stimulus control unit 40 generates a video signal as a control signal to display the problem data input from the signal processing unit 30. If the stimulus presentation unit 50 is an audio speaker, the stimulus control unit 40 generates an audio signal as a control signal to speak the problem data input from the signal processing unit 30. If the stimulus presentation unit 50 is a light-emitting device, the stimulus control unit 40 generates a light-emitting control signal as a control signal corresponding to the problem data input from the signal processing unit 30.

[0079] The stimulus presentation unit 50 presents a stimulus to the user based on a control signal input from the stimulus control unit 40. If the stimulus presentation unit 50 is a display panel, the stimulus presentation unit 50 presents the user with a video containing multiple problem data of varying difficulty levels based on a video signal input from the stimulus control unit 40. If the stimulus presentation unit 50 is an audio speaker, the stimulus presentation unit 50 presents the user with audio uttering multiple problem data of varying difficulty levels based on an audio signal input from the stimulus control unit 40. If the stimulus presentation unit 50 is a light-emitting device, the stimulus presentation unit 50 presents the user with light corresponding to multiple problem data of varying difficulty levels based on a light emission control signal input from the stimulus control unit 40.

[0080] [Operation] Next, the operation of the information processing device 1 will be explained. Figure 15 shows an example of the procedure for changing the difficulty level in the information processing device 1.

[0081] First, the signal processing unit 30 reads out multiple problem data of different difficulty levels from the problem data 22, corresponding to the setting data included in the difficulty level 24, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0082] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S101). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the received answer to the signal processing unit 30. When the signal processing unit 30 receives the answer from the input reception unit 10, it calculates (obtains) the reaction time 25 for the answer corresponding to the problem data (step S102).

[0083] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S103; N). Once the predetermined number of questions N have been presented (step S103; Y), the signal processing unit 30 calculates the task difference Δtv of the variability of reaction times 25 acquired so far (step S104). The signal processing unit 30 derives cognitive capacity using the calculated task difference Δtv and the regression equation 23 read from the memory unit 20 (step S105). The signal processing unit 30 determines the difficulty level of the questions to be presented in subsequent sessions using the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 changes the difficulty level of the questions to be presented in subsequent sessions by writing the determined difficulty level to the setting data of the memory unit 20 (step S106).

[0084] The signal processing unit 30 performs the above series of processes until a predetermined number of applications are completed (step S107;N). The signal processing unit 30 terminates the issuance of questions once the predetermined number of questions have been completed (step S107;Y).

[0085] Figure 16 shows an example of the procedure for deriving the regression equation 23 in the information processing device 1. First, the signal processing unit 30 reads out multiple problem data of different difficulty levels corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0086] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S111). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. The input reception unit 10 obtains the answer corresponding to the problem data from the user (step S112). The input reception unit 10 outputs the obtained answer to the signal processing unit 30. When the signal processing unit 30 obtains the answer from the input reception unit 10, it uses the correct answer data contained in the problem data 22 to determine whether the answer corresponding to the problem data is correct or incorrect (step S113). The signal processing unit 30 calculates (obtains) the reaction time 25 and the accuracy rate of the answer corresponding to the problem data (step S114).

[0087] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S105; N). Once the predetermined number of questions N have been presented (step S105; Y), the signal processing unit 30 calculates the task difference Δtv of the variation in reaction time 25 obtained so far, and the task difference ΔR of the accuracy rate of answers obtained so far (step S116). Based on the calculated task differences Δtv and ΔR, the signal processing unit 30 derives a regression equation 23 and stores the derived regression equation 23 in the storage unit 20 (step S117).

[0088] The information processing device 1 may perform the series of steps for deriving the regression equation 23 shown in Figure 16 separately from (i.e., in advance of) the series of steps for changing the difficulty level in the information processing device 1 shown in Figure 15. In this case, the user who answers the questions for deriving the regression equation 23 and the user who answers the questions in the series of steps shown in Figure 15 may be the same person or different people. The information processing device 1 may also perform the series of steps for deriving the regression equation 23 shown in Figure 16 within the series of steps S101 to S103 shown in Figure 15.

[0089] [effect] Next, we will explain the effects of the information processing device 1.

[0090] In the information processing device 1 and information processing program 21 according to this embodiment, multiple problem data to be presented to the user is determined based on the variability of the user's reaction time 25 corresponding to multiple problem data. Here, the Discloser has experimentally obtained the finding that the variability of reaction time 25 changes depending on the task. Therefore, it is possible to determine multiple problem data to be presented to the user based on the variability of reaction time 25. Consequently, cognitive capacity can be derived regardless of whether or not there is a correct answer in the reaction time.

[0091] In the information processing device 1 and information processing program 21 according to this embodiment, the user's cognitive capacity is derived based on the variability of the reaction time 25. This makes it possible to determine the difficulty level of multiple problem data to be presented to the user, or to determine the next set of problem data, based on the derived cognitive capacity. Therefore, cognitive capacity can be derived regardless of whether or not there is a correct answer within the reaction time.

[0092] In the information processing device 1 and information processing program 21 according to this embodiment, cognitive capacity is derived based on the task difference Δtv of the variability of reaction time 25 and the regression equation 23 for the task difference Δtv of the variability of reaction time 25. This makes it possible to derive cognitive capacity regardless of whether or not there is a correct answer in the reaction time.

[0093] In the information processing device 1 and information processing program 21 according to this embodiment, the difficulty level of multiple question data to be presented in subsequent sessions is determined using a table in which difficulty levels are set according to cognitive capacity. This makes it possible, for example, to set the difficulty level of multiple question data to be presented in subsequent sessions so that the user's cognitive capacity approaches a predetermined standard.

[0094] In the information processing device 1 and information processing program 21 according to this embodiment, reaction time 25 is derived based on the timing of the user's response input to multiple problem data. This allows cognitive capacity to be derived based on the task difference Δtv of the variability of reaction time 25 and the regression equation 23 for the task difference Δtv of the variability of reaction time 25. Therefore, cognitive capacity can be derived regardless of whether or not there is a correct answer in the reaction time 25.

[0095] In the information processing device 1 according to this embodiment, a stimulus presentation unit 50 is provided that presents multiple problem data. This allows control over the presentation timing of each problem data, enabling accurate deriving of the reaction time 25. As a result, cognitive capacity can be accurately derived regardless of whether or not there is a correct answer within the reaction time 25.

[0096] <3. Modified Examples of the First Embodiment> [Differentiation A] In the above embodiment, for example, as shown in Figure 17, a regression table 26 may be stored in the storage unit 20 instead of the regression equation 23. In the regression table 26, for example, as shown in Figure 18, the table sets up a correspondence between the task difference Δtv[s] of the variability of the user's reaction time (75% percentile - 25% percentile) when solving a high-difficulty problem and when solving a low-difficulty problem, and the task difference ΔR[%] of the correct answer rate for the problem when solving a high-difficulty problem and when solving a low-difficulty problem. In regression table 26, for example, when the task difference Δtv[s] is in the range of 0 to 0.1, the task difference ΔR[%] of the correct answer rate is -3%, when the task difference Δtv[s] is in the range of 0.1 to 0.2, the task difference ΔR[%] of the correct answer rate is -8%, and when the task difference Δtv[s] is in the range of 0.2 to 0.3, the task difference ΔR[%] of the correct answer rate is -10%.

[0097] In this modified version, regression table 26 is used instead of regression equation 23. Even in this case, cognitive capacity can be derived regardless of whether or not there is a correct answer in reaction time 25.

[0098] [Variation B] In the above embodiment, for example, as shown in Figure 19, information processing program 21a and regression equation 23a may be stored in the storage unit 20 instead of information processing program 21 and regression equation 23. Regression equation 23a is, for example, the regression equation shown in Figure 10.

[0099] The signal processing unit 30 executes an information processing program 21a stored in the memory unit 20. The functions of the signal processing unit 30 are realized, for example, by the execution of the information processing program 21a by the signal processing unit 30. For example, the signal processing unit 30 reads out multiple problem data of different difficulty levels corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. For example, when the signal processing unit 30 obtains an answer corresponding to the problem data 22 from the input receiving unit 10, it derives a reaction time 25 based on the input timing of the obtained answer. For example, when the signal processing unit 30 has completed the presentation of a predetermined number of questions N, it calculates the variability tv of the reaction time 25. For example, the signal processing unit 30 derives cognitive capacity based on the calculated variability tv and the regression equation 23a read from the memory unit 20. For example, the signal processing unit 30 determines the difficulty level of the questions to be presented in subsequent sessions based on the derived cognitive capacity. The signal processing unit 30 determines the difficulty level of the questions to be asked in subsequent sessions, for example, based on a table included in the difficulty level 24 that has been read from the memory unit 20. The signal processing unit 30 sets the difficulty level of the questions to be asked in subsequent sessions by, for example, writing the determined difficulty level to the setting data of the memory unit 20.

[0100] Next, the operation of the information processing device 1 will be explained. Figure 20 shows an example of the procedure for changing the difficulty level in the information processing device 1.

[0101] First, the signal processing unit 30 reads out multiple problem data of different difficulty levels from the problem data 22, corresponding to the setting data included in the difficulty level 24, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0102] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S121). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the received answer to the signal processing unit 30. When the signal processing unit 30 receives the answer from the input reception unit 10, it calculates (receives) the reaction time 25 for the answer corresponding to the problem data (step S122).

[0103] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S123; N). Once the predetermined number of questions N have been presented (step S123; Y), the signal processing unit 30 calculates the variability tv of the reaction times 25 acquired so far (step S124). The signal processing unit 30 derives cognitive capacity using the calculated variability tv and the regression equation 23a read from the memory unit 20 (step S125). The signal processing unit 30 determines the difficulty level of the questions to be presented in subsequent sessions using the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 changes the difficulty level of the questions to be presented in subsequent sessions by writing the determined difficulty level to the setting data of the memory unit 20 (step S126).

[0104] The signal processing unit 30 performs the above series of processes until a predetermined number of applications are completed (step S127; N). Once the predetermined number of questions have been submitted (step S127; Y), the signal processing unit 30 terminates the question submission process.

[0105] Figure 21 shows an example of the procedure for deriving the regression equation 23a in the information processing device 1. First, the signal processing unit 30 reads out multiple problem data of different difficulty levels corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0106] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S131). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. The input reception unit 10 obtains the answer corresponding to the problem data from the user (step S132). The input reception unit 10 outputs the obtained answer to the signal processing unit 30. When the signal processing unit 30 obtains the answer from the input reception unit 10, it uses the correct answer data contained in the problem data 22 to determine whether the answer corresponding to the problem data is correct or incorrect (step S133). The signal processing unit 30 calculates (obtains) the reaction time 25 and the accuracy rate of the answer corresponding to the problem data (step S134).

[0107] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S135; N). Once the predetermined number of questions N have been presented (step S135; Y), the signal processing unit 30 calculates the variation tv of reaction time 25 acquired so far and the task difference ΔR of the correct answer rate of the answers acquired so far (step S136). Based on the calculated variation tv and task difference ΔR, the signal processing unit 30 derives a regression equation 23a and stores the derived regression equation 23a in the storage unit 20 (step S137).

[0108] The information processing device 1 may perform the series of steps for deriving the regression equation 23a shown in Figure 21 separately from (i.e., in advance of) the series of steps for changing the difficulty level in the information processing device 1 shown in Figure 20. In this case, the user who answers the questions for deriving the regression equation 23a and the user who answers the questions in the series of steps shown in Figure 20 may be the same or different. The information processing device 1 may also perform the series of steps for deriving the regression equation 23a shown in Figure 21 within the series of steps S121 to S123 shown in Figure 20.

[0109] In this modified example, regression table 23a is used. Even in this case, cognitive capacity can be derived regardless of whether or not there is a correct answer in reaction time 25.

[0110] [Differentiation C] In the above embodiment, for example, as shown in Figure 22, instead of the information processing program 21 and the regression equation 23, the information processing program 21b and the regression equation 23b may be stored in the storage unit 20. The regression equation 23b is, for example, the regression equation shown in Figure 11.

[0111] The signal processing unit 30 executes the information processing program 21b stored in the memory unit 20. The functions of the signal processing unit 30 are realized, for example, by the execution of the information processing program 21b by the signal processing unit 30. For example, the signal processing unit 30 reads out multiple problem data of different difficulty levels corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. For example, when the signal processing unit 30 obtains an answer corresponding to the problem data 22 from the input receiving unit 10, it derives a reaction time 25 based on the input timing of the obtained answer. For example, when the signal processing unit 30 has completed the presentation of a predetermined number of questions N, it calculates the variability tv of the reaction time 25. For example, the signal processing unit 30 derives cognitive capacity based on the calculated variability tv and the regression equation 23b read from the memory unit 20. For example, the signal processing unit 30 determines the difficulty level of the questions to be presented in subsequent sessions based on the derived cognitive capacity. The signal processing unit 30 determines the difficulty level of the questions to be asked in subsequent sessions, for example, based on a table included in the difficulty level 24 that has been read from the memory unit 20. The signal processing unit 30 sets the difficulty level of the questions to be asked in subsequent sessions by, for example, writing the determined difficulty level to the setting data of the memory unit 20.

[0112] Next, the operation of the information processing device 1 will be explained. Figure 23 shows an example of the procedure for changing the difficulty level in the information processing device 1.

[0113] First, the signal processing unit 30 reads out multiple problem data of different difficulty levels from the problem data 22, corresponding to the setting data included in the difficulty level 24, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0114] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S141). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the received answer to the signal processing unit 30. When the signal processing unit 30 receives the answer from the input reception unit 10, it calculates (receives) the reaction time 25 for the answer corresponding to the problem data (step S142).

[0115] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S143; N). Once the predetermined number of questions N have been presented (step S143; Y), the signal processing unit 30 calculates the variability tv of the reaction times 25 acquired so far (step S144). The signal processing unit 30 derives cognitive capacity using the calculated variability tv and the regression equation 23b read from the memory unit 20 (step S145). The signal processing unit 30 determines the difficulty level of the questions to be presented in the next session using the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 changes the difficulty level of the questions to be presented in the next session by writing the determined difficulty level to the setting data of the memory unit 20 (step S146).

[0116] The signal processing unit 30 performs the above series of processes until a predetermined number of applications are completed (step S147; N). The signal processing unit 30 terminates the issuance of questions once the predetermined number of questions have been completed (step S147; Y).

[0117] Figure 24 shows an example of the procedure for deriving the regression equation 23b in the information processing device 1. First, the signal processing unit 30 reads out multiple problem data of a predetermined difficulty level corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read multiple problem data of the predetermined difficulty level to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0118] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S151). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. The input reception unit 10 obtains the answer corresponding to the problem data from the user (step S152). The input reception unit 10 outputs the obtained answer to the signal processing unit 30. When the signal processing unit 30 obtains the answer from the input reception unit 10, it uses the correct answer data contained in the problem data 22 to determine whether the answer corresponding to the problem data is correct or incorrect (step S153). The signal processing unit 30 calculates (obtains) the reaction time 25 and the accuracy rate of the answer corresponding to the problem data (step S154).

[0119] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S155; N). Once the predetermined number of questions N have been presented (step S155; Y), the signal processing unit 30 calculates the variation tv of reaction times 25 acquired so far and the accuracy rate R of the answers acquired so far (step S156). Based on the calculated variation tv and accuracy rate R, the signal processing unit 30 derives a regression equation 23b and stores the derived regression equation 23b in the memory unit 20 (step S157).

[0120] The information processing device 1 may perform the series of steps for deriving the regression equation 23b shown in Figure 24 separately from (i.e., in advance) the series of steps for changing the difficulty level in the information processing device 1 shown in Figure 23. In this case, the user who answers the question for deriving the regression equation 23b and the user who answers the question in the series of steps shown in Figure 23 may be the same person or different people. The information processing device 1 may also perform the series of steps for deriving the regression equation 23b shown in Figure 24 within the series of steps S141 to S143 shown in Figure 23.

[0121] In this modified example, regression equation 23b is used. Even in this case, cognitive capacity can be derived regardless of whether or not there is a correct answer within the reaction time 25.

[0122] [Differentiation D] In the above embodiment, the input receiving unit 10 may receive responses from multiple users. In this case, the signal processing unit 30 derives a reaction time 25 for each user based on the input timing of the responses received from each user. The signal processing unit 30 further calculates a task difference Δtv of the variation in reaction time 25 for each user, and derives a cognitive capacity for each user based on the calculated task difference Δtv and the regression equation 23 read from the memory unit 20. Based on the cognitive capacity derived for each user, the signal processing unit 30 derives a collective cognitive capacity when multiple users are viewed as a group. In this case, for example, it becomes possible to determine how burdensome the task is for the group, or how much leeway the group has for the task.

[0123] <4. Second Embodiment> Next, an information processing device 2 according to a second embodiment of this disclosure will be described. Note that descriptions of components that share the same reference numerals as those in the above embodiment will be omitted as appropriate to avoid redundant descriptions.

[0124] Figure 25 shows an example of the schematic configuration of the information processing device 2 according to this embodiment. The information processing device 2 comprises an input receiving unit 10, a storage unit 20, a signal processing unit 30, a stimulus control unit 40, a stimulus presentation unit 50, and a bio-information detection unit 60. The bio-information detection unit 60 functions as an electroencephalogram (EEG) detection unit that detects the user's EEG and outputs the detected EEG signal data to the signal processing unit 30. The signal processing unit 30 corresponds to a specific example of the "acquisition unit," "determination unit," and "derivation unit" of this disclosure. The stimulus presentation unit 50 corresponds to a specific example of the "presentation unit" of this disclosure. The bio-information detection unit 60 corresponds to a specific example of the "detection unit" of this disclosure.

[0125] In the information processing device 2, the memory unit 20 stores an information processing program 21c that controls the user's cognitive capacity, problem data 22 used in the information processing program 21c, a regression equation 23c, and a difficulty level 24. The regression equation 23c is, for example, the regression equation shown in Figure 7. The regression equation 23c corresponds to one specific example of the "regression data" in this disclosure. Furthermore, the memory unit 20 stores observation data 28 and peak values ​​29 obtained by processing by the information processing program 21c.

[0126] The signal processing unit 30 executes the information processing program 21c stored in the memory unit 20. The functions of the signal processing unit 30 are realized, for example, by the execution of the information processing program 21c by the signal processing unit 30. For example, the signal processing unit 30 reads out multiple problem data of different difficulty levels from the problem data 22, corresponding to the setting data included in the difficulty level 24, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. For example, the signal processing unit 30 acquires the user's electroencephalogram (EEG) signal data corresponding to multiple problem data of different difficulty levels from the bio-information detection unit 60. For example, the signal processing unit 30 extracts the signal data of the target waveform (α wave) included in the acquired EEG signal data. For example, the signal processing unit 30 derives the peak value 29 of the power of the slow EEG (α wave) in the signal data of the target waveform (α wave). For example, when a predetermined number of problems N have been presented, the signal processing unit 30 calculates the task difference ΔP of the peak value 29. The task difference ΔP with a peak value of 29 corresponds to one specific example of the "fluctuations in the user's specific frequency band of biosignals" as described in this disclosure. The signal processing unit 30 derives cognitive capacity based on the calculated task difference ΔP and the regression equation 23c read from the memory unit 20. The signal processing unit 30 determines the difficulty level of the questions to be asked in subsequent sessions based on the derived cognitive capacity. The signal processing unit 30 determines the difficulty level of the questions to be asked in subsequent sessions based on the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 sets the difficulty level of the questions to be asked in subsequent sessions by writing the determined difficulty level to the setting data of the memory unit 20.

[0127] [Operation] Next, we will explain the operation of the information processing device 2. Figure 26 shows an example of the procedure for changing the difficulty level in the information processing device 2.

[0128] First, the signal processing unit 30 reads out multiple problem data of different difficulty levels from the problem data 22, corresponding to the setting data included in the difficulty level 24, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0129] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S201). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the acquired answer to the signal processing unit 30. During this time, the bio-information detection unit 60 detects the user's brainwaves and outputs the detected brainwave signal data to the signal processing unit 30.

[0130] The signal processing unit 30 acquires the user's electroencephalogram (EEG) signal data (observation data 28) corresponding to multiple problem data of different difficulty levels from the bio-information detection unit 60 (step S202). The signal processing unit 30 extracts the signal data of the target waveform (alpha wave) included in the acquired EEG signal data. The signal processing unit 30 calculates the peak value 29 of the power of the slow EEG (alpha wave) in the signal data of the target waveform (alpha wave) (step S203).

[0131] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S204; N). Once the predetermined number of questions N have been presented (step S204; Y), the signal processing unit 30 calculates the task difference ΔP of the peak value 29 calculated so far (step S205). The signal processing unit 30 derives cognitive capacity using the calculated task difference ΔP and the regression equation 23c read from the memory unit 20 (step S206). The signal processing unit 30 determines the difficulty level of the questions to be presented in the next session using the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 changes the difficulty level of the questions to be presented in the next session by writing the determined difficulty level to the setting data of the memory unit 20 (step S207).

[0132] The signal processing unit 30 performs the above series of processes until a predetermined number of applications are completed (step S208;N). Once the predetermined number of questions have been submitted (step S208;Y), the signal processing unit 30 terminates the question submission process.

[0133] Figure 27 shows an example of the procedure for deriving the regression equation 23c in the information processing device 2. First, the signal processing unit 30 reads out multiple problem data of different difficulty levels corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0134] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S211). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the acquired answer to the signal processing unit 30. During this time, the bio-information detection unit 60 detects the user's brainwaves and outputs the detected brainwave signal data to the signal processing unit 30.

[0135] The signal processing unit 30 acquires the user's electroencephalogram (EEG) signal data (observation data 28) corresponding to multiple problem data of different difficulty levels from the bio-information detection unit 60 (step S212). The signal processing unit 30 further acquires the answers corresponding to multiple problem data of different difficulty levels (step S212). The signal processing unit 30 uses the correct answer data contained in the problem data 22 to determine the correctness of the answers corresponding to the problem data acquired from the input reception unit 10 (step S213). The signal processing unit 30 calculates (acquires) the accuracy rate of the answers corresponding to the problem data (step S214). The signal processing unit 30 further extracts the signal data of the target waveform (alpha wave) contained in the acquired EEG signal data (observation data 28). The signal processing unit 30 calculates the peak value 29 of the power of the slow EEG (alpha wave) in the signal data of the target waveform (alpha wave) (step S214).

[0136] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S215; N). Once the predetermined number of questions N have been presented (step S215; Y), the signal processing unit 30 calculates the task difference ΔR of the correct answer rate of the answers obtained so far and the task difference ΔP of the peak value 29 calculated so far (step S216). Based on the calculated task differences ΔR and ΔP, the signal processing unit 30 derives a regression equation 23c and stores the derived regression equation 23c in the storage unit 20 (step S217).

[0137] The information processing device 2 may perform the series of steps for deriving the regression equation 23c shown in Figure 27 separately from (i.e., in advance of) the series of steps for changing the difficulty level in the information processing device 2 shown in Figure 26. In this case, the user who answers the question for deriving the regression equation 23c and the user who answers the question in the series of steps shown in Figure 26 may be the same or different. The information processing device 2 may also perform the series of steps for deriving the regression equation 23c shown in Figure 27 within the series of steps S201 to S204 shown in Figure 26.

[0138] [effect] Next, we will explain the effects of the information processing device 2.

[0139] In the information processing device 2 and information processing program 21c according to this embodiment, multiple problem data to be presented to the user are determined based on fluctuations (peak value 29) of the user's biological signals in a specific frequency band in relation to the problem data. Here, the Discloser has experimentally obtained the finding that fluctuations (peak value 29) of the user's biological signals in a specific frequency band change depending on the task. Therefore, it is possible to determine the problem data to be presented to the user based on fluctuations (peak value 29) of the user's biological signals in a specific frequency band. Consequently, cognitive capacity can be derived regardless of whether or not there is a reaction time.

[0140] In the information processing device 2 and information processing program 21c according to this embodiment, the user's cognitive capacity is derived based on fluctuations (peak value 29) of the user's biological signals in a specific frequency band in response to problem data. This makes it possible to determine the difficulty level of the problem data to be presented to the user and to determine the problem data for subsequent sessions based on the derived cognitive capacity. Therefore, cognitive capacity can be derived regardless of whether or not there is a reaction time.

[0141] In the information processing device 2 and information processing program 21c according to this embodiment, cognitive capacity is derived based on the task difference ΔP of the user's biosignal fluctuations (peak value 29) in a specific frequency band and the regression equation 23c for the task difference ΔP. This makes it possible to derive cognitive capacity regardless of whether or not there is a reaction time.

[0142] In the information processing device 2 and information processing program 21c according to this embodiment, the difficulty level of multiple question data to be presented in subsequent sessions is determined using a table in which difficulty levels are set according to cognitive capacity. This makes it possible, for example, to set the difficulty level of multiple question data to be presented in subsequent sessions so that the user's cognitive capacity approaches a predetermined standard.

[0143] In the information processing device 2 according to this embodiment, the user's biosignals are detected by the biosignal detection unit 60 and output to the signal processing unit 30. As a result, cognitive capacity can be derived based on the task difference ΔP of fluctuations (peak value 29) of the user's biosignals in a specific frequency band and the regression equation 23c for the task difference ΔP. Consequently, cognitive capacity can be derived even without obtaining reaction time 25. In other words, cognitive capacity can be derived regardless of whether or not reaction time is available.

[0144] In the information processing apparatus 2 according to the present embodiment, a stimulus presentation unit 50 that presents a plurality of problem data with different levels of difficulty is provided. As a result, since the presentation timing of each problem data can be controlled, it is possible to accurately derive the fluctuations (peak value 29) of the biological signal in a specific frequency band of the user corresponding to a plurality of problem data with different levels of difficulty. As a result, the cognitive capacity can be derived without obtaining the reaction time 25. That is, the cognitive capacity can be derived regardless of whether there is a reaction time or not.

[0145] In the present embodiment, the regression equation 23c may be a regression equation that defines the relationship between the task difference ΔP and the correct answer rate R when solving a problem with a high level of difficulty, similar to the modified example of the first embodiment, or may be a regression equation that defines the relationship between the peak value of the power of slow brain waves (α waves) and the task difference ΔR. Also, in this modified example, the regression equation 23c may be a regression equation that defines the relationship between the peak value of the power of slow brain waves (α waves) and the correct answer rate R when solving a problem with a high level of difficulty, similar to the modified example of the first embodiment.

[0146] <5. Modified Example of the Second Embodiment> In the second embodiment described above, for example, instead of the regression equation 23c, a regression table similar to the regression table 26 shown in FIG. 18 may be stored in the storage unit 20. In the regression table according to this modified example, the task difference ΔP [(mV 2 / Hz) 2 / Hz] of the peak value of the power of the slow brain waves (α waves) of the user when solving a problem with a high level of difficulty and when solving a problem with a low level of difficulty, and the task difference ΔR [%] of the correct answer rate of the problem when solving a problem with a high level of difficulty and when solving a problem with a low level of difficulty are set in a table. In the regression table according to this modified example, for example, when the task difference ΔP [(mV 2 / Hz) 2 / Hz] is within the range of 0.4 to 0.2, the task difference ΔR [%] of the correct answer rate of the problem is -3%, and the task difference ΔP [(mV 2 / Hz) 2When [ / Hz] is within the range of 0.2 to 0, the task difference ΔR[%] of the correct answer rate for the problem is -8%, and the task difference ΔP[(mV 2 (Hz) 2 When [ / Hz] is within the range of 0 to -0.2, the task difference ΔR[%] in the correct answer rate of the problem is -10%.

[0147] In this modified example, instead of regression equation 23c, a regression table similar to the regression table 26 shown in Figure 18 is used. Even in this case, cognitive capacity can be derived without obtaining reaction time 25. In other words, cognitive capacity can be derived regardless of whether or not there is a correct answer for reaction time 25.

[0148] Furthermore, in the second embodiment and its modified form described above, the bio-information detection unit 60 may detect the brain waves of multiple users. In this case, the signal processing unit 30 extracts the signal data of the target waveform (alpha wave) included in the brain wave signal data obtained from each user. The signal processing unit 30 derives the peak value 29 of the power of the slow brain waves (alpha waves) in the signal data of the target waveform (alpha wave) for each user. The signal processing unit 30 further calculates the task difference ΔP of the peak value 29 for each user, and derives the cognitive capacity for each user based on the calculated task difference ΔP and the regression equation 23c read from the memory unit 20. Based on the cognitive capacity derived for each user, the signal processing unit 30 derives the collective cognitive capacity when multiple users are viewed as a group. In this case, for example, it becomes possible to determine how burdensome the task is for the group, or how much leeway the group has for the task.

[0149] <6. Third Embodiment> [composition] Next, an information processing device 3 according to a third embodiment of this disclosure will be described. Figure 28 shows an example of the schematic configuration of the information processing device 3 according to this embodiment. The information processing device 3 has a configuration that combines the information processing device 1 according to the first embodiment and the information processing device 2 according to the second embodiment. In other words, the information processing device 3 includes an input receiving unit 10, a storage unit 20, a signal processing unit 30, a stimulus control unit 40, a stimulus presentation unit 50, and a bio-information detection unit 60. The storage unit 20 stores an information processing program 21d, problem data 22 used in the information processing program 21d, regression equations 23, 23c, and difficulty level 24. Furthermore, the storage unit 20 stores reaction time 25, observation data 28, and peak value 29 obtained by processing by the information processing program 21d.

[0150] [Operation] Next, we will explain the operation of the information processing device 2. Figure 29 shows an example of the procedure for changing the difficulty level in the information processing device 3.

[0151] First, the signal processing unit 30 reads out multiple problem data of different difficulty levels from the problem data 22, corresponding to the setting data included in the difficulty level 24, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0152] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S301). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the acquired answer to the signal processing unit 30. During this time, the bio-information detection unit 60 detects the user's brainwaves and outputs the detected brainwave signal data to the signal processing unit 30.

[0153] The signal processing unit 30 acquires the user's electroencephalogram (EEG) signal data (observation data 28) corresponding to multiple problem data of different difficulty levels from the bio-information detection unit 60 (step S302). The signal processing unit 30 extracts the signal data of the target waveform (alpha wave) included in the acquired EEG signal data. The signal processing unit 30 calculates the peak power value 29 of the slow EEG (alpha wave) in the signal data of the target waveform (alpha wave) (step S303). The signal processing unit 30 further calculates (acquires) the reaction time 25 of the answer corresponding to the problem data (step S303).

[0154] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S304; N). Once the predetermined number of questions N have been presented (step S304; Y), the signal processing unit 30 calculates the task difference Δtv of the reaction time 25 variability and the task difference ΔP of the peak value 29 calculated so far (step S305). The signal processing unit 30 derives cognitive capacity using the calculated task differences Δtv, ΔP and the regression equations 23, 23c read from the memory unit 20 (step S306). The signal processing unit 30 determines the difficulty level of the questions to be presented in the next session using the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 changes the difficulty level of the questions to be presented in the next session by writing the determined difficulty level to the setting data of the memory unit 20 (step S307).

[0155] The signal processing unit 30 performs the above series of processes until a predetermined number of applications are completed (step S308; N). Once the predetermined number of questions have been submitted (step S308; Y), the signal processing unit 30 terminates the question submission process.

[0156] In the information processing device 3 and information processing program 21d according to this embodiment, the difficulty level of multiple problem data to be presented in subsequent sessions is determined based on the user's cognitive capacity, which is obtained based on the calculated task differences Δtv and ΔP. Here, the Discloser has experimentally obtained the finding that the variability of reaction time 25 and the fluctuations (peak value 29) of the user's biological signals in a specific frequency band change depending on the task. Therefore, the user's cognitive capacity can be derived based on the variability of reaction time 25 and the fluctuations (peak value 29) of the user's biological signals in a specific frequency band, and the difficulty level of multiple problem data to be presented in subsequent sessions can be determined based on the derived cognitive capacity. Thus, cognitive capacity can be derived regardless of whether or not there is a correct answer within the reaction time.

[0157] <8. Fourth Embodiment> Next, an information processing device 4 according to a fourth embodiment of the present disclosure will be described. Figure 30 shows an example of the schematic configuration of the information processing device 4 according to this embodiment. The information processing device 4 includes an input receiving unit 10, a storage unit 20, a signal processing unit 30, a stimulus control unit 40, a stimulus presentation unit 50, and a biological information detection unit 60. The signal processing unit 30 corresponds to a specific example of the "characteristic value generation unit," "evaluation value generation unit," "determination unit," and "derivation unit" of the present disclosure. The stimulus presentation unit 50 corresponds to a specific example of the "presentation unit" of the present disclosure. The biological information detection unit 60 corresponds to a specific example of the "detection unit" of the present disclosure.

[0158] The memory unit 20 stores an information processing program 21e that controls the user's cognitive capacity, problem data 22 used in the information processing program 21e, a regression equation 41, difficulty level 24, length of division period ΔT 44, length of overlap period Δd1 45, length of division period ΔW 46, and length of overlap period Δd2 47. The regression equation 41 is, for example, the regression equation shown in Figure 8 or Figure 9. The regression equation 41 corresponds to one specific example of the "regression data" in this disclosure. The information processing program 21e, for example, generates characteristic values ​​of the observed waveform for each observation data 43 from the acquired observation data 43, and performs processing to generate evaluation values ​​for the differences between the observation data 43 regarding the observed waveform based on the characteristic values ​​of each observation data 43. The division period ΔW is a value longer than the division period ΔT.

[0159] The memory unit 20 stores data input from the input receiving unit 10 to the signal processing unit 30. For example, the memory unit 20 stores the set value for the length of the division period ΔT input from the input receiving unit 10, and the set value for the length of the overlap period Δd1 input from the input receiving unit 10. The set value for the division period ΔT input from the input receiving unit 10 is included in the length 44 of the division period ΔT in the memory unit 20. The set value for the overlap period Δd1 input from the input receiving unit 10 is included in the length 45 of the overlap period Δd1 in the memory unit 20.

[0160] The memory unit 20 also stores data (observation data 43) input from the biological information detection unit 60 to the signal processing unit 30. For example, as shown in Figure 30, n observation data 43 are stored in the memory unit 20.

[0161] The memory unit 20 also stores a learning model 48. The learning model 48 performs, for example, the learning procedure shown in Figure 31 and the estimation procedure shown in Figure 32.

[0162] Here, for each of the h training observation data 43, the area of ​​the frequency band of the observed waveform included in the power spectrum PΔTa_b(t) (1≦a≦h,1≦b≦j) derived for each division period ΔT into j parts is defined as SΔTa_b(t) (characteristic value) (see Figure 31). Similarly, for each of the h training observation data 43, the area of ​​the frequency band of the observed waveform included in the power spectrum PΔWa_b(t) (1≦a≦h,1≦b≦j) derived for each division period ΔW into j parts is defined as SΔWa_b(t) (characteristic value) (see Figure 31). For each of the h training observation data 43, the emotional state within the observation period T is defined as E(t). Note that the number of training observation data 43 (h) is equal to the number of observation data 43 (n) during emotion estimation described later. Furthermore, the number of divisions (k) of the observed data 43 during training may be equal to or different from the number of divisions (j) of the observed data 43 during emotion estimation, as described later. Also, it is assumed that data exists at the same time in the area SΔTa_b(t) and the area SΔWa_b(t) during the observation period T.

[0163] The learning model 48 is a model that has undergone learning, including machine learning, with 2n data points common to b in areas SΔTa_b(t) and SΔWa_b(t) as explanatory variables, and the emotional state E(t) for the period corresponding to b as the dependent variable (see Figure 32). In other words, the learning model 48 is a model that estimates one emotional state from 2n data points.

[0164] The signal processing unit 30 executes the information processing program 21e stored in the memory unit 20. The function of the signal processing unit 30 is realized, for example, by the execution of the information processing program 21e by the signal processing unit 30. For each of the n measured observation data 43, the signal processing unit 30 derives a power spectrum PΔTa_b(t) (1≦a≦n, 1≦b≦k) for each of the k division periods ΔT, and derives the area RΔTa_b(t) (characteristic value) of the frequency band of the observed waveform included in the derived power spectrum PΔTa_b(t) (see Figure 32). The signal processing unit 30 further derives a power spectrum PΔWa_b(t) (1≦a≦n, 1≦b≦k) for each of the n measured observation data 43, divided into k division periods ΔW, and derives the area RΔWa_b(t) (characteristic value) of the frequency band of the observed waveform included in the derived power spectrum PΔWa_b(t) (see Figure 32).

[0165] When the learning model 48 receives 2n data points in areas RΔTa_b and RΔWa_b derived by the signal processing unit 30, where b is common to both, it generates an emotional state Out_b, which is an evaluation value of the difference between the observed data 43 regarding the observed waveform, based on these 2n data points, and outputs it to the signal processing unit 30 (see Figure 32). The emotional state Out_b corresponds to the user's level of arousal.

[0166] Here, if the actual emotional states corresponding to areas RΔTa_b and RΔWa_b are all the same, then assume that i (i ≤ k) of the emotional states Out_b match the actual emotional states. In this case, the estimation accuracy is i / k.

[0167] [Operation] Next, the operation of the information processing device 4 will be explained. Figure 33 shows an example of the procedure for changing the difficulty level in the information processing device 4.

[0168] First, the signal processing unit 30 reads out multiple problem data of different difficulty levels from the problem data 22, corresponding to the setting data included in the difficulty level 24, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0169] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S401). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the acquired answer to the signal processing unit 30. During this time, the bio-information detection unit 60 detects the user's brainwaves and outputs the detected brainwave signal data to the signal processing unit 30.

[0170] The signal processing unit 30 acquires signal data (n observation data 43) of the user's electroencephalogram (EEG) corresponding to multiple problem data of different difficulty levels from the bio-information detection unit 60 (step S402). For each of the acquired n observation data 43, the signal processing unit 30 derives a power spectrum PΔTa_b(t) (1≦a≦n, 1≦b≦k) for each division period ΔT into k parts, and derives the area RΔTa_b(t) (characteristic value) of the frequency band of the observed waveform included in the derived power spectrum PΔTa_b(t). The signal processing unit 30 further derives a power spectrum PΔWa_b(t) (1≦a≦n, 1≦b≦k) (1≦a≦n, 1≦b≦k) for each of the n observation data 43 for each division period ΔW into k parts, and derives the area RΔWa_b(t) (characteristic value) of the frequency band of the observed waveform included in the derived power spectrum PΔWa_b(t).

[0171] The signal processing unit 30 inputs 2n data points in the derived areas RΔTa_b and RΔWa_b where b is common to both into the learning model 48. The learning model 48 then outputs the emotional state Out_b (evaluation value) corresponding to these 2n data points to the signal processing unit 30. In other words, the signal processing unit 30 derives the emotional state Out_b (arousal level 42) using the n observation data points 43 and the learning model 48 (step S403).

[0172] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S404; N). Once the predetermined number of questions N have been presented (step S404; Y), the signal processing unit 30 calculates the task difference Δk of the arousal level 42 calculated so far (step S405). The signal processing unit 30 derives cognitive capacity based on the calculated task difference Δk and the regression equation 41 read from the memory unit 20 (step S206). For example, the signal processing unit 30 determines the difficulty level of the questions to be presented in subsequent sessions based on the derived cognitive capacity. The signal processing unit 30 determines the difficulty level of the questions to be presented in subsequent sessions based on the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 changes the difficulty level of the questions to be presented in subsequent sessions by writing the determined difficulty level to the setting data of the memory unit 20 (step S407).

[0173] The signal processing unit 30 performs the above series of processes until a predetermined number of applications are completed (step S408; N). The signal processing unit 30 terminates the issuance of questions once the predetermined number of questions have been completed (step S408; Y).

[0174] Figure 34 shows an example of the procedure for deriving the regression equation 41 in the information processing device 4. First, the signal processing unit 30 reads out multiple problem data of different difficulty levels corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0175] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S411). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the acquired answer to the signal processing unit 30. During this time, the bio-information detection unit 60 detects the user's brainwaves and outputs the detected brainwave signal data (n observation data 43) to the signal processing unit 30.

[0176] The signal processing unit 30 acquires the user's electroencephalogram (EEG) signal data (n observation data 43) corresponding to multiple problem data of different difficulty levels from the bio-information detection unit 60 (step S412). The signal processing unit 30 further acquires the answers corresponding to multiple problem data of different difficulty levels (step S412). The signal processing unit 30 uses the correct answer data contained in the problem data 22 to determine whether the answers corresponding to the problem data, acquired from the input reception unit 10, are correct or incorrect (step S413). The signal processing unit 30 calculates (acquires) the accuracy rate of the answers corresponding to the problem data (step S414). The signal processing unit 30 further uses the acquired EEG signal data (n observation data 43) and the learning model 48 to derive the emotional state Out_b (arousal level 42) (step S414).

[0177] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S415; N). Once the predetermined number of questions N have been presented (step S415; Y), the signal processing unit 30 calculates the task difference ΔR of the correct answer rate of the answers obtained so far and the task difference Δk of the arousal level 42 calculated so far (step S416). Based on the calculated task differences ΔR and Δk, the signal processing unit 30 derives a regression equation 41 and stores the derived regression equation 41 in the memory unit 20 (step S417).

[0178] [effect] Next, we will explain the effects of the information processing device 4.

[0179] In the information processing device 4 and information processing program 21e according to this embodiment, characteristic values ​​(area RΔTa_b(t), area RΔWa_b(t)) are derived for each observation data 43 obtained from the user's biological observation over a predetermined period of time. Based on the derived characteristic values ​​for each observation data 43, an evaluation value (emotional state Out_b) regarding the difference between observation data related to the observed waveform is generated as an arousal level 42. Then, a task for the user is determined based on the generated evaluation value (arousal level 42). Here, the Discloser has experimentally obtained the finding that the above evaluation value (arousal level 42) changes depending on the task. Therefore, it is possible to determine the problem data to be presented to the user based on the above evaluation value (arousal level 42). Consequently, cognitive capacity can be derived regardless of whether or not there is a reaction time.

[0180] In the information processing device 4 and information processing program 21e according to this embodiment, the user's cognitive capacity is derived based on the evaluation value (alertness level 42) described above. This makes it possible to determine the difficulty level of the problem data to be presented to the user and to determine the problem data for subsequent sessions based on the derived cognitive capacity. Therefore, cognitive capacity can be derived regardless of whether or not there is a reaction time.

[0181] In the information processing device 4 and information processing program 21e according to this embodiment, cognitive capacity is derived based on the task difference Δk of the evaluation value (alertness level 42) and the regression equation 41 for the task difference Δk of the evaluation value (alertness level 42). This makes it possible to derive cognitive capacity regardless of whether or not there is a reaction time.

[0182] In the information processing device 4 and information processing program 21e according to this embodiment, the difficulty level of multiple question data to be presented in subsequent sessions is determined using a table in which difficulty levels are set according to cognitive capacity. This makes it possible, for example, to set the difficulty level of multiple question data to be presented in subsequent sessions so that the user's cognitive capacity approaches a predetermined standard.

[0183] The information processing device 4 according to this embodiment is provided with a stimulus presentation unit 50 that presents multiple problem data of different difficulty levels. This allows for control over the presentation timing of each problem data, enabling accurate deriving of the evaluation value (arousal level 42). As a result, cognitive capacity can be accurately derived regardless of whether or not there is a reaction time.

[0184] <9. Modified Examples of the Fourth Embodiment> [Differentiation Example E] In the fourth embodiment described above, for example, as shown in Figure 35, information processing program 21f and regression equation 41a may be stored in the storage unit 20 instead of information processing program 21e and regression equation 41.

[0185] The information processing program 21f performs tasks such as generating characteristic values ​​for each observed waveform from the acquired observation data 43, and generating evaluation values ​​for the differences between the observed waveforms based on the characteristic values ​​for each observed data 43. The regression equation 41a is, for example, the regression equation shown in Figure 12.

[0186] The signal processing unit 30 executes the information processing program 21f stored in the memory unit 20. The function of the signal processing unit 30 is realized, for example, by the execution of the information processing program 21f by the signal processing unit 30.

[0187] Next, the operation of the information processing device 4 in this modified example will be described. Figure 36 shows an example of the procedure for changing the difficulty level in the information processing device 4.

[0188] First, the signal processing unit 30 reads out multiple problem data of a predetermined difficulty level corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read multiple problem data of the predetermined difficulty level to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0189] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S421). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the acquired answer to the signal processing unit 30. During this time, the bio-information detection unit 60 detects the user's brainwaves and outputs the detected brainwave signal data to the signal processing unit 30.

[0190] The signal processing unit 30 acquires signal data (n observation data 43) of the user's electroencephalogram (EEG) corresponding to multiple problem data of a predetermined difficulty level from the bio-information detection unit 60 (step S422). For each of the acquired n observation data 43, the signal processing unit 30 derives a power spectrum PΔTa_b(t) (1≦a≦n,1≦b≦k) for each division period ΔT into k parts, and derives the area RΔTa_b(t) (characteristic value) of the frequency band of the observed waveform included in the derived power spectrum PΔTa_b(t). The signal processing unit 30 further derives a power spectrum PΔWa_b(t) (1≦a≦n,1≦b≦k) (1≦a≦n,1≦b≦k) for each of the n observation data 43 for each division period ΔW into k parts, and derives the area RΔWa_b(t) (characteristic value) of the frequency band of the observed waveform included in the derived power spectrum PΔWa_b(t).

[0191] The signal processing unit 30 inputs 2n data points in the derived areas RΔTa_b and RΔWa_b where b is common to both into the learning model 48. The learning model 48 then outputs the emotional state Out_b (evaluation value) corresponding to these 2n data points to the signal processing unit 30. In other words, the signal processing unit 30 derives the emotional state Out_b (arousal level 42) using the n observation data points 43 and the learning model 48 (step S423).

[0192] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S424; N). Once the predetermined number of questions N have been presented (step S424; Y), the signal processing unit 30 derives cognitive capacity based on the arousal level 42 calculated so far and the regression equation 41a read from the memory unit 20 (step S425). For example, the signal processing unit 30 determines the difficulty level of the questions to be presented in subsequent sessions based on the derived cognitive capacity. The signal processing unit 30 determines the difficulty level of the questions to be presented in subsequent sessions based on the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 changes the difficulty level of the questions to be presented in subsequent sessions by writing the determined difficulty level to the setting data of the memory unit 20 (step S426).

[0193] The signal processing unit 30 performs the above series of processes until a predetermined number of applications are completed (step S427; N). Once the predetermined number of questions have been submitted (step S427; Y), the signal processing unit 30 terminates the question submission process.

[0194] Figure 37 shows an example of the procedure for deriving the regression equation 41a in the information processing device 4 in this modified example. First, the signal processing unit 30 reads out problem data of a predetermined difficulty level corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of the predetermined difficulty level to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0195] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S431). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the acquired answer to the signal processing unit 30. During this time, the bio-information detection unit 60 detects the user's brainwaves and outputs the detected brainwave signal data (n observation data 43) to the signal processing unit 30.

[0196] The signal processing unit 30 acquires the user's electroencephalogram (EEG) signal data (n observation data 43) corresponding to multiple problem data of a predetermined difficulty level from the bio-information detection unit 60 (step S432). The signal processing unit 30 further acquires the answers corresponding to multiple problem data of a predetermined difficulty level (step S432). The signal processing unit 30 uses the correct answer data contained in the problem data 22 to determine whether the answers corresponding to the problem data, acquired from the input reception unit 10, are correct or incorrect (step S433). The signal processing unit 30 calculates (acquires) the accuracy rate of the answers corresponding to the problem data (step S434). The signal processing unit 30 further uses the acquired EEG signal data (n observation data 43) and the learning model 48 to derive the emotional state Out_b (arousal level 42) (step S434).

[0197] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S435; N). Once the predetermined number of questions N have been presented (step S435; Y), the signal processing unit 30 calculates the accuracy rate R of the answers obtained so far and the alertness level 42 calculated so far (step S436). Based on the calculated accuracy rate R and alertness level 42, the signal processing unit 30 derives a regression equation 41a and stores the derived regression equation 41a in the memory unit 20 (step S437).

[0198] In this modified example, regression equation 41a is used. Even in this case, cognitive capacity can be derived regardless of whether or not there is a correct answer within reaction time 25.

[0199] In this modified example, regression equation 41a may be a regression equation that defines the relationship between the level of arousal k and the task difference ΔR of the accuracy rate, or it may be a regression equation that defines the relationship between the task difference Δk of the level of arousal and the accuracy rate R.

[0200] [Modification F] In the fourth embodiment and its modifications described above, the bio-information detection unit 60 may detect the brainwaves of multiple users. In this case, the signal processing unit 30 derives an emotional state Out_b (alertness level 42) for each user based on the brainwave signal data obtained from each user, and calculates a task difference Δk of the derived emotional state Out_b (alertness level 42) for each user. The signal processing unit 30 derives cognitive capacity for each user based on the calculated task difference Δk and the regression equation 41 read from the memory unit 20. The signal processing unit 30 derives the collective cognitive capacity when multiple users are viewed as a group, based on the cognitive capacity derived for each user. In this case, for example, it becomes possible to determine how burdensome the task is for the group, or how much leeway the group has for the task.

[0201] <10. Modifications of the First Embodiment> [Differentiation G] In the first embodiment described above, for example, as shown in Figure 38, an information processing program 21g and a regression equation 23g may be stored in the storage unit 20 instead of the information processing program 21 and the regression equation 23. The regression equation 23g is, for example, the regression equation shown in Figure 13.

[0202] The signal processing unit 30 executes an information processing program 21g stored in the memory unit 20. The function of the signal processing unit 30 is realized, for example, by the execution of the information processing program 21g by the signal processing unit 30. For example, the signal processing unit 30 reads out multiple problem data of different difficulty levels corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. For example, when the signal processing unit 30 obtains an answer corresponding to the problem data 22 from the input receiving unit 10, it derives a reaction time 25 based on the input timing of the obtained answer. For example, when the signal processing unit 30 has completed the presentation of a predetermined number of problems N, it calculates the task difference Δtv of the variability tv of the reaction time 25. For example, the signal processing unit 30 derives cognitive capacity based on the calculated task difference Δtv and the regression equation 23g read from the memory unit 20. For example, the signal processing unit 30 determines the difficulty level of the problems to be presented in subsequent sessions based on the derived cognitive capacity. The signal processing unit 30 determines the difficulty level of the questions to be asked in subsequent sessions, for example, based on a table included in the difficulty level 24 that has been read from the memory unit 20. The signal processing unit 30 sets the difficulty level of the questions to be asked in subsequent sessions by, for example, writing the determined difficulty level to the setting data of the memory unit 20.

[0203] Next, the operation of the information processing device 1 will be explained. Figure 39 shows an example of the procedure for changing the difficulty level in the information processing device 1.

[0204] First, the signal processing unit 30 reads out multiple problem data of different difficulty levels from the problem data 22, corresponding to the setting data included in the difficulty level 24, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0205] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S161). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. When the input reception unit 10 receives the answer corresponding to the problem data from the user, it outputs the received answer to the signal processing unit 30. When the signal processing unit 30 receives the answer from the input reception unit 10, it calculates (receives) the reaction time 25 for the answer corresponding to the problem data (step S162).

[0206] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S163; N). Once the predetermined number of questions N have been presented (step S163; Y), the signal processing unit 30 calculates the task difference Δtv of the reaction time variability tv acquired so far (step S164). The signal processing unit 30 derives cognitive capacity using the calculated task difference Δtv and the regression equation 23g read from the memory unit 20 (step S165). The signal processing unit 30 determines the difficulty level of the questions to be presented in subsequent sessions using the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 changes the difficulty level of the questions to be presented in subsequent sessions by writing the determined difficulty level to the setting data of the memory unit 20 (step S166).

[0207] The signal processing unit 30 performs the above series of processes until a predetermined number of applications are completed (step S167;N). The signal processing unit 30 terminates the issuance of questions once the predetermined number of questions have been completed (step S167;Y).

[0208] Figure 40 shows an example of the procedure for deriving the regression equation 23g in the information processing device 1. First, the signal processing unit 30 reads out multiple problem data of different difficulty levels corresponding to the setting data included in the difficulty level 24 from the problem data 22, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. Based on the problem data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0209] The stimulus presentation unit 50 presents the user with a stimulus based on problem data of a predetermined difficulty level, based on a control signal input from the stimulus control unit 40 (step S171). The stimulus presentation unit 50 presents the user with, for example, a video containing the problem data, an audio recording of the problem data, or light corresponding to the problem data. The stimulus presentation unit 50 may also present the user with, for example, a taste, a tactile sensation, or a smell corresponding to the problem data. The user then inputs the answer corresponding to the problem data into the input reception unit 10. The input reception unit 10 obtains the answer corresponding to the problem data from the user (step S172). The input reception unit 10 outputs the obtained answer to the signal processing unit 30. When the signal processing unit 30 obtains the answer from the input reception unit 10, it uses the correct answer data contained in the problem data 22 to determine whether the answer corresponding to the problem data is correct or incorrect (step S173). The signal processing unit 30 calculates (obtains) the reaction time 25 and the accuracy rate of the answer corresponding to the problem data (step S174).

[0210] The signal processing unit 30 performs the above series of processes until a predetermined number of questions N have been presented (step S175; N). Once the predetermined number of questions N have been presented (step S175; Y), the signal processing unit 30 calculates the task difference Δtv of the reaction time variation tv obtained so far, and the accuracy rate R of the answers obtained so far (step S176). Based on the calculated task difference Δtv and accuracy rate R, the signal processing unit 30 derives a regression equation 23g and stores the derived regression equation 23g in the memory unit 20 (step S177).

[0211] The information processing apparatus 1 may perform a series of procedures for deriving the regression formula 23g shown in FIG. 40 separately (i.e., in advance) from a series of procedures for changing the difficulty level in the information processing apparatus 1 shown in FIG. 39. At this time, the user who answers the problem for deriving the regression formula 23g and the user who answers the problem in the series of procedures shown in FIG. 39 may be the same as each other or may be different from each other. Note that the information processing apparatus 1 may perform a series of procedures for deriving the regression formula 23g shown in FIG. 40 by mixing them in the series of procedures of steps S161 to S163 shown in FIG. 39.

[0212] In this modification, the regression table 23g is used. Even in such a case, the cognitive capacity can be derived regardless of whether there is a correct answer in the reaction time 25.

[0213] <11. Regarding the biological information capable of controlling the cognitive capacity of the present disclosure> In each of the above embodiments and their modifications, reaction time and brain waves have been cited as information capable of controlling the cognitive capacity of the present disclosure. However, as information capable of controlling the cognitive capacity of the present disclosure, in addition to reaction time and brain waves, for example, pulse wave, electrocardiogram, blood flow, psychogenic sweating, etc. can also be cited. Pulse wave, electrocardiogram, blood flow, and psychogenic sweating can be measured, for example, by installing sensors (hereinafter referred to as "sensor S") on fingers, ears, heads, arms, chests, etc. Therefore, a large-scale sensor such as a headset used when measuring brain waves is not required as the sensor S.

[0214] The sensor S can be mounted, for example, on a head-mounted display (HMD) 200 as shown in FIG. 41. In the head-mounted display 200, for example, the detection electrodes 203 of the sensor S can be provided on the inner surfaces of the pad portion 201 and the band portion 202.

[0215] Also, the sensor S can be mounted on, for example, a headband 300 as shown in FIG. 42. In the headband 300, for example, the detection electrode 303 of the sensor S can be provided on the inner surface of the band portions 301 and 302 that contact the head.

[0216] Also, the sensor S can be mounted on, for example, headphones 400 as shown in FIG. 43. In the headphones 400, for example, the detection electrode 403 of the sensor S can be provided on the inner surface of the band portion 401 that contacts the head or on the ear pads 402.

[0217] Also, the sensor S can be mounted on, for example, earphones 500 as shown in FIG. 44. In the earphones 500, for example, the detection electrode 502 of the sensor S can be provided on the earpiece 501 that is inserted into the ear.

[0218] Also, the sensor S can be mounted on, for example, a watch 600 as shown in FIG. 45. In the watch 600, for example, the detection electrode 604 of the sensor S can be provided on the inner surface of the display portion 601 that displays the time or the inner surface of the band portion 602 (for example, the inner surface of the buckle portion 603).

[0219] Also, the sensor S can be mounted on, for example, glasses 700 as shown in FIG. 46. In the glasses 700, for example, the detection electrode 702 of the sensor S can be provided on the inner surface of the frame 701.

[0220] Also, the sensor S can be mounted on, for example, gloves, rings, pencils, pens, game console controllers, etc.

[0221] (Pulse wave, electrocardiogram, blood flow) Based on the electrical signals of the pulse wave, electrocardiogram, and blood flow obtained by the sensor S, for example, by using the following characteristic quantities shown below, the control of the cognitive capacity of the present disclosure is possible. ·Heart rate per second ·Average value of the heart rate per second within a predetermined period (window) • rmssd (root mean square successive difference): Root mean square of consecutive heart rate intervals • pnn50 (percentage of adjacent normal-to-normal intervals): The percentage of consecutive heartbeats with intervals exceeding 50ms. • LF: Area of ​​the PSD of heart rate intervals between 0.04 and 0.15 Hz • HF: Area of ​​the PSD of heart rate intervals between 0.15 and 0.4 Hz · LF / (LF+HF) ·HF / (LF+HF) LF / HF • Entropy of heart rate • SD1: Standard deviation in the direction of the y=x axis of a Poincaré plot (a scatter plot with the t-th heart rate interval as the x-axis and the (t+1)-th heart rate interval as the y-axis). • SD2: Standard deviation in the direction perpendicular to the y=x axis of a Poincaré plot. • SD1 / SD2 • SDRR (standard deviation of RR interval): standard deviation of heart rate interval

[0222] (Psychogenic sweating) The cognitive capacity of this disclosure can be controlled by using features obtained based on the electrical signals (EDA: electrodermal activity) of psychogenic sweating obtained by sensor S, such as those shown below. • Number of SCRs (skin conductance responses) generated per minute • SCR amplitude • SCL (skin conductance level) value • Rate of change of SCL

[0223] For example, by using the method described in the following document, it is possible to separate SCR and SCL from EDA. Benedek, M., & Kaernbach, C. (2010). A continuous measure of phasic electrodermal activity. Journal of neuroscience methods, 190(1), 80-91.

[0224] Furthermore, in controlling cognitive capacity as described in this disclosure, either a single modal (one physiological indicator) or a combination of multiple modal (multiple physiological indicators) may be used.

[0225] <12. Fifth Embodiment> Next, an information processing device 700 according to the fifth embodiment of this disclosure will be described. Note that descriptions of components that share the same reference numerals as those in the above embodiments will be omitted as appropriate to avoid redundant descriptions.

[0226] [composition] Figure 47 shows an example of the schematic configuration of the information processing device 700 according to this embodiment. The information processing device 700 comprises an input receiving unit 10, a storage unit 20, a signal processing unit 30, a stimulus control unit 40, a stimulus presentation unit 50, and a biosignal detection unit 710. The biosignal detection unit 710 is configured to include the sensor S described above and detects pulse waves, electrocardiograms, blood flow, or psychogenic sweating. When the sensor S detects a pulse wave, the biosignal detection unit 710 functions as a pulse wave detection unit that detects the pulse wave using the sensor S and outputs the pulse wave obtained by the detection. When the sensor S detects an electrocardiogram, the biosignal detection unit 710 functions as an electrocardiogram detection unit that detects the electrocardiogram using the sensor S and outputs the electrocardiogram obtained by the detection. When the sensor S detects blood flow, the biosignal detection unit 710 functions as a blood flow detection unit that detects the blood flow using the sensor S and outputs the blood flow obtained by the detection. When sensor S detects psychogenic sweating, the biosignal detection unit 710 functions as a sweat detection unit that uses sensor S to detect psychogenic sweating and outputs the psychogenic sweating obtained through detection. The biosignal detection unit 710 outputs the signal data obtained through detection to the signal processing unit 30.

[0227] In the information processing device 2, the memory unit 20 stores an information processing program 721 that controls the user's cognitive capacity, problem data 22 used in the information processing program 721, a regression equation 722, and a difficulty level 24. The regression equation 722 is, for example, the regression equation shown in Figures 48 to 55 described later. Furthermore, the memory unit 20 stores feature data 723 obtained by processing by the information processing program 721.

[0228] The signal processing unit 30 executes the information processing program 721 stored in the memory unit 20. The function of the signal processing unit 30 is realized, for example, by the execution of the information processing program 721 by the signal processing unit 30. The signal processing unit 30 reads out multiple problem data of different difficulty levels from the problem data 22, corresponding to the setting data included in the difficulty level 24, and sequentially outputs the read problem data of different difficulty levels to the stimulus control unit 40. The signal processing unit 30 obtains, for example, user signal data corresponding to multiple problem data of different difficulty levels from the biosignal detection unit 710. The signal processing unit 30 derives the above-mentioned feature quantities (feature quantity data 723) based on the acquired signal data. The signal processing unit 30 derives the feature quantity data 723 when, for example, a predetermined number of problems N have been presented. The signal processing unit 30 derives cognitive capacity based on the derived feature quantity data 723 and the regression equation 722 read from the memory unit 20. The signal processing unit 30 determines the difficulty level of the questions to be asked in subsequent sessions, for example, based on the derived cognitive capacity. The signal processing unit 30 determines the difficulty level of the questions to be asked in subsequent sessions, for example, based on the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 sets the difficulty level of the questions to be asked in subsequent sessions, for example, by writing the determined difficulty level to the setting data of the memory unit 20.

[0229] FIG. 48 shows an example of the relationship between the task difference Δha [%] of pnn50 of the pulse wave when solving a high-difficulty problem and the correct answer rate R [%] when solving a low-difficulty problem. The task difference Δha is obtained by subtracting the pnn50 of the pulse wave when solving a low-high-difficulty problem from the pnn50 of the pulse wave when solving a high-difficulty problem. In FIG. 48, data for each user are plotted, and the characteristics of all users are represented by a regression equation (regression line). In FIG. 48, the regression equation is represented as R = a10 × Δha + b10.

[0230] A small task difference Δha in the pnn50 of the pulse wave means that the difference in the pnn50 of the pulse wave when solving a high-difficulty problem and when solving a low-difficulty problem is small. It can be said that users who obtained such results tend to be able to solve problems with a certain range of pnn50 of the pulse wave regardless of the difficulty of the problem. On the other hand, a large task difference Δha in the pnn50 of the pulse wave means that the difference in the pnn50 of the pulse wave when solving a high-difficulty problem and when solving a low-difficulty problem is large. It can be said that users who obtained such results tend to have a larger pnn50 of the pulse wave as the difficulty of the problem increases.

[0231] From FIG. 48, it can be seen that when the task difference Δha of the pnn50 of the pulse wave is large, the correct answer rate R of the problem becomes high, and when the task difference Δha of the pnn50 of the pulse wave is small, the correct answer rate R of the problem becomes low. From this, it can be seen that people with a large pnn50 of the pulse wave for difficult problems tend to have a high correct answer rate R (that is, they can answer difficult problems as well as easy problems). Conversely, it can be seen that people with a small pnn50 of the pulse wave even for difficult problems tend to have a low correct answer rate R (that is, the correct answer rate for difficult problems decreases).

[0232] From the above, when the task difference Δha of the pulse wave pnn50 is large, it can be inferred that the user's cognitive capacity is higher than the predetermined standard. Conversely, when the task difference Δha of the pulse wave pnn50 is small, it can be inferred that the user's cognitive capacity is lower than the predetermined standard. If the user's cognitive capacity is lower than the predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than the predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0233] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δha of the pulse wave pnn50 is small, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δha of the pulse wave pnn50 is large, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0234] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference Δha of the pulse wave pnn50 and the regression equation in Figure 48.

[0235] Figure 49 shows an example of the relationship between the task difference Δhb[%] of the variability of the pulse wave pnn50 when solving high-difficulty problems and the accuracy rate R[%] when solving high-difficulty problems. The task difference Δhb is obtained by subtracting the variability of the pulse wave pnn50 when solving low- and high-difficulty problems from the variability of the pulse wave pnn50 when solving high-difficulty problems. In Figure 49, data for each user is plotted, and the characteristics of all users are represented by a regression equation (regression line). In Figure 49, the regression equation is expressed as R = a11 × Δhb + b11.

[0236] A small task-based difference Δhb in the variability of the pulse wave's pnn50 means that there is little difference in the variability of the pulse wave's pnn50 when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to be able to solve problems with a certain range of pulse wave pnn50 variability, regardless of the problem's difficulty. On the other hand, a large task-based difference Δhb in the variability of the pulse wave's pnn50 means that there is a large difference in the variability of the pulse wave's pnn50 when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to experience greater variability in their pulse wave pnn50 as the difficulty of the problem increases.

[0237] Figure 49 shows that when the task difference Δhb for the variability of the pulse wave pnn50 is large, the accuracy rate R of the problem is high, and when the task difference Δhb for the variability of the pulse wave pnn50 is small, the accuracy rate R of the problem is small. From this, it can be seen that people whose pulse wave pnn50 variability is large when solving difficult problems tend to have a high accuracy rate R (i.e., they can solve difficult problems as well as easy problems). Conversely, people whose pulse wave pnn50 variability is small even when solving difficult problems tend to have a low accuracy rate R (i.e., their accuracy rate on difficult problems decreases).

[0238] From the above, when the task difference Δhb for the variability of the pulse wave pnn50 is large, it can be inferred that the user's cognitive capacity is higher than the predetermined standard. Conversely, when the task difference Δha for the variability of the pulse wave pnn50 is small, it can be inferred that the user's cognitive capacity is lower than the predetermined standard. If the user's cognitive capacity is lower than the predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than the predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0239] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhb of the variability of the pulse wave pnn50 is small, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhb of the variability of the pulse wave pnn50 is large, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0240] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference Δhb of the variability of the pulse wave pnn50 and the regression equation in Figure 49.

[0241] Figure 50 shows the difference in power in the low-frequency band (around 0.01 Hz) of the power spectrum obtained by performing an FFT on the pulse wave pnn50 when solving a high-difficulty problem and when solving an easy-difficulty problem, Δhc[ms]. -2This shows an example of the relationship between [Hz] and the accuracy rate R[%] when solving high-difficulty problems. Below, "the power in the low-frequency band (around 0.01Hz) of the power spectrum obtained by performing an FFT on the pnn50 of the pulse wave" will be referred to as "the power in the low-frequency band of the pnn50 of the pulse wave." The task difference Δhc is obtained by subtracting the power in the low-frequency band of the pnn50 of the pulse wave when solving low- and high-difficulty problems from the power in the low-frequency band of the pnn50 of the pulse wave when solving high-difficulty problems. Figure 50 plots the data for each user, and the characteristics of all users are represented by a regression equation (regression line). In Figure 50, the regression equation is represented as R = a12 × Δhc + b12.

[0242] A small difference in the power of the low-frequency band of the pulse wave's pnn50 (PNN50) between tasks (Δhc) means that there is little difference in the power of the low-frequency band of the pulse wave's pnn50 when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to be able to solve problems with a certain range of power in the low-frequency band of the pulse wave's pnn50, regardless of the difficulty of the problem. On the other hand, a large negative difference in the power of the low-frequency band of the pulse wave's pnn50 (Δhc) means that there is a large difference in the power of the low-frequency band of the pulse wave's pnn50 when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to have a decrease in the power of the low-frequency band of the pulse wave's pnn50 as the difficulty of the problem increases.

[0243] Figure 50 shows that when the difference Δhc in the low-frequency power of the pulse wave pnn50 is small, the accuracy rate R of the problem increases, and when the difference Δhc in the low-frequency power of the pulse wave pnn50 is large in the negative direction, the accuracy rate R of the problem decreases. From this, it can be seen that people with high low-frequency power of the pulse wave pnn50 tend to have a high accuracy rate R even for difficult problems (i.e., they can answer difficult problems as correctly as easy problems). Conversely, people with low low-frequency power of the pulse wave pnn50 tend to have a low accuracy rate R even for difficult problems (i.e., their accuracy rate for difficult problems decreases).

[0244] From the above, when the task difference Δhc of the low-frequency power of the pulse wave pnn50 is small, it can be inferred that the user's cognitive capacity is higher than a predetermined standard. Conversely, when the task difference Δhc of the low-frequency power of the pulse wave pnn50 is large in the negative direction, it can be inferred that the user's cognitive capacity is lower than a predetermined standard. If the user's cognitive capacity is lower than a predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than a predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0245] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhc of the low-frequency power of the pulse wave pnn50 is large in the negative direction, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhc of the low-frequency power of the pulse wave pnn50 is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0246] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the power difference Δhc in the low-frequency band of the pulse wave pnn50 and the regression equation in Figure 50.

[0247] Figure 51 shows an example of the relationship between the task difference Δhd [ms] of pulse wave rmssd when solving high-difficulty problems and the accuracy rate R [%] when solving high-difficulty problems. The task difference Δhd is obtained by subtracting the rmssd of the pulse wave when solving low-difficulty problems from the rmssd of the pulse wave when solving high-difficulty problems. In Figure 51, data for each user is plotted, and the characteristics of all users are represented by a regression equation (regression line). In Figure 51, the regression equation is expressed as R = a13 × Δhd + b13.

[0248] A small task-based difference Δhd for pulse wave rmssd means that the difference in pulse wave rmssd between solving high-difficulty problems and low-difficulty problems is small. Users who achieve this result tend to be able to solve problems with a certain range of pulse wave rmssd regardless of the difficulty level. On the other hand, a large negative task-based difference Δhd for pulse wave rmssd means that the difference in pulse wave rmssd between solving high-difficulty problems and low-difficulty problems is large. Users who achieve this result tend to have a smaller pulse wave rmssd as the difficulty level of the problem increases.

[0249] Figure 51 shows that when the task difference Δhd of the pulse wave rmssd is small, the accuracy rate R of the problem is high, and when the task difference Δhd of the pulse wave rmssd is large in the negative direction, the accuracy rate R of the problem is small. From this, it can be seen that people with a large pulse wave rmssd tend to have a high accuracy rate R even for difficult problems (i.e., they can answer difficult problems as correctly as easy problems). Conversely, people with a small pulse wave rmssd for difficult problems tend to have a low accuracy rate R (i.e., their accuracy rate for difficult problems decreases).

[0250] From the above, when the task difference Δhd of the pulse wave rmssd is small, it can be inferred that the user's cognitive capacity is higher than the given standard. Conversely, when the task difference Δhd of the pulse wave rmssd is large in the negative direction, it can be inferred that the user's cognitive capacity is lower than the given standard. If the user's cognitive capacity is lower than the given standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than the given standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0251] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhd of the pulse wave rmssd is large in the negative direction, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhd of the pulse wave rmssd is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0252] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference Δhd of the pulse wave rmssd and the regression equation in Figure 51.

[0253] Figure 52 shows an example of the relationship between the task difference Δhe [ms] of pulse wave rmssd variability when solving high-difficulty problems and low-difficulty problems, and the accuracy rate R [%] when solving high-difficulty problems. The task difference Δhe is obtained by subtracting the pulse wave rmssd variability when solving low- and high-difficulty problems from the pulse wave rmssd variability when solving high-difficulty problems. In Figure 52, data for each user is plotted, and the characteristics of all users are represented by a regression equation (regression line). In Figure 52, the regression equation is expressed as R = a14 × Δhe + b14.

[0254] A small task-based difference Δhe in the variability of pulse wave rmssd means that there is little difference in the variability of pulse wave rmssd between solving high-difficulty problems and low-difficulty problems. Users who achieve this result tend to be able to solve problems with a certain range of pulse wave rmssd variability, regardless of the difficulty of the problem. On the other hand, a large negative task-based difference Δhe in the variability of pulse wave rmssd means that there is a large difference in the variability of pulse wave rmssd between solving high-difficulty problems and low-difficulty problems. Users who achieve this result tend to have a smaller variability in pulse wave rmssd as the difficulty of the problem increases.

[0255] Figure 52 shows that when the task difference Δhe for the variability of pulse wave rmssd is small, the accuracy rate R of the problem increases, and when the task difference Δhe for the variability of pulse wave rmssd is large in the negative direction, the accuracy rate R of the problem decreases. From this, it can be seen that people with a large variability of pulse wave rmssd tend to have a high accuracy rate R even on difficult problems (i.e., they can answer difficult problems as correctly as easy problems). Conversely, people with a small variability of pulse wave rmssd on difficult problems tend to have a low accuracy rate R (i.e., their accuracy on difficult problems decreases).

[0256] From the above, when the task difference Δhe for the variability of pulse wave rmssd is small, it can be inferred that the user's cognitive capacity is higher than the given standard. Conversely, when the task difference Δhe for the variability of pulse wave rmssd is large in the negative direction, it can be inferred that the user's cognitive capacity is lower than the given standard. If the user's cognitive capacity is lower than the given standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than the given standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0257] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhe of the variability of pulse wave rmssd is large in the negative direction, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhe of the variability of pulse wave rmssd is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0258] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference Δhe of the variability of pulse wave rmssd and the regression equation in Figure 52.

[0259] Figure 53 shows the difference in power in the low-frequency band (around 0.01 Hz) of the power spectrum obtained by performing an FFT on the pulse wave rmssd when solving a high-difficulty problem and when solving an easy-difficulty problem, Δhf[ms 2This shows an example of the relationship between [ / Hz] and the accuracy rate R[%] when solving high-difficulty problems. Hereafter, "the power in the low-frequency band (around 0.01Hz) of the power spectrum obtained by performing an FFT on the pulse wave rmssd" will be referred to as "the power in the low-frequency band of the pulse wave rmssd". The task difference Δhf is obtained by subtracting the power in the low-frequency band of the pulse wave rmssd when solving low- and high-difficulty problems from the power in the low-frequency band of the pulse wave rmssd when solving high-difficulty problems. Figure 53 plots the data for each user, and the characteristics of all users are represented by a regression equation (regression line). In Figure 53, the regression equation is represented as R=a15×Δhf+b15.

[0260] A small difference in the power of the low-frequency band of the pulse wave's RMSSD (Resonant Mass Dynamics) indicates that there is little difference in the power of the low-frequency band of the pulse wave's RMSSD when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to be able to solve problems using a certain range of RMSSD power in the low-frequency band, regardless of the problem's difficulty. On the other hand, a large negative difference in the power of the low-frequency band of the pulse wave's RMSSD indicates that there is a large difference in the power of the low-frequency band of the pulse wave's RMSSD when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to have a decrease in the power of the low-frequency band of their RMSSD as the difficulty of the problem increases.

[0261] Figure 53 shows that when the difference Δhf in the low-frequency power of the pulse wave's rmssd is small, the accuracy rate R of the problem increases, and when the difference Δhf in the low-frequency power of the pulse wave's rmssd is large in the negative direction, the accuracy rate R of the problem decreases. From this, it can be seen that people with high low-frequency power of their pulse wave's rmssd tend to have a high accuracy rate R (i.e., they can answer difficult problems as correctly as easy problems). Conversely, people with low low-frequency power of their pulse wave's rmssd tend to have a low accuracy rate R (i.e., their accuracy rate on difficult problems decreases).

[0262] From the above, when the task difference Δhf of the low-frequency power of the pulse wave RMSSD is small, it can be inferred that the user's cognitive capacity is higher than the predetermined standard. Conversely, when the task difference Δhf of the low-frequency power of the pulse wave RMSSD is large in the negative direction, it can be inferred that the user's cognitive capacity is lower than the predetermined standard. If the user's cognitive capacity is lower than the predetermined standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than the predetermined standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0263] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhf of the low-frequency power of the pulse wave RMSSD is large in the negative direction, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhf of the low-frequency power of the pulse wave RMSSD is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0264] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the power difference Δhf in the low-frequency band of the pulse wave RMSSD and the regression equation in Figure 53.

[0265] Figure 54 shows an example of the relationship between the task difference Δhg [min] of the variability in the number of SCRs for psychoactive sweating when solving high-difficulty problems and when solving low-difficulty problems, and the accuracy rate R [%] when solving high-difficulty problems. The task difference Δhg is obtained by subtracting the variability in the number of SCRs for psychoactive sweating when solving low-difficulty and high-difficulty problems from the variability in the number of SCRs for psychoactive sweating when solving high-difficulty problems. In Figure 54, data for each user is plotted, and the characteristics of all users are represented by a regression equation (regression line). In Figure 54, the regression equation is expressed as R = a¹⁶ × Δhg + b¹⁶.

[0266] A small task difference Δhg for the variability in the number of SCRs for psychogenic sweating means that there is little difference in the variability in the number of SCRs for psychogenic sweating when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to be able to solve problems with a certain range of variability in the number of SCRs for psychogenic sweating, regardless of the difficulty of the problem. On the other hand, a large negative task difference Δhg for the variability in the number of SCRs for psychogenic sweating means that there is a large difference in the variability in the number of SCRs for psychogenic sweating when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to show a decrease in the variability in the number of SCRs for psychogenic sweating as the difficulty of the problem increases.

[0267] Figure 54 shows that when the task difference Δhg for the variability in the number of SCRs in psychogenic sweating is small, the accuracy rate R of the problem increases, and when the task difference Δhg for the variability in the number of SCRs in psychogenic sweating is large in the negative direction, the accuracy rate R of the problem decreases. From this, it can be seen that people with a large variability in the number of SCRs in psychogenic sweating tend to have a high accuracy rate R even on difficult problems (i.e., they can answer difficult problems as correctly as easy problems). Conversely, people with a small variability in the number of SCRs in psychogenic sweating tend to have a low accuracy rate R (i.e., their accuracy rate on difficult problems decreases).

[0268] From the above, when the task difference Δhg for the variability in the number of SCRs in psychogenic sweating is small, it can be inferred that the user's cognitive capacity is higher than the given standard. Conversely, when the task difference Δhg for the variability in the number of SCRs in psychogenic sweating is large in the negative direction, it can be inferred that the user's cognitive capacity is lower than the given standard. If the user's cognitive capacity is lower than the given standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than the given standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0269] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhg for the variability in the number of SCRs in psychogenic sweating is large in the negative direction, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhg for the variability in the number of SCRs in psychogenic sweating is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0270] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference Δhgf of the variability in the number of SCRs in psychogenic sweating and the regression equation in Figure 54.

[0271] Figure 55 shows the task difference Δhh[ms] in the number of SCRs (Stress Criteria for Psychoactive Sweating) when solving high-difficulty problems versus low-difficulty problems. 2This shows an example of the relationship between [Hz] and the accuracy rate R[%] when solving high-difficulty problems. The task difference Δhh is obtained by subtracting the number of SCRs for psychogenic sweating when solving low- and high-difficulty problems from the number of SCRs for psychogenic sweating when solving high-difficulty problems. In Figure 55, data for each user is plotted, and the characteristics of all users are represented by a regression equation (regression line). In Figure 55, the regression equation is expressed as R=a17×Δhh+b17.

[0272] A small task difference Δhh in the number of SCRs for psychogenic sweating means that there is little difference in the number of SCRs for psychogenic sweating when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to be able to solve problems with a certain range of SCRs for psychogenic sweating, regardless of the difficulty level. On the other hand, a large negative task difference Δhh in the number of SCRs for psychogenic sweating means that there is a large difference in the number of SCRs for psychogenic sweating when solving high-difficulty problems compared to low-difficulty problems. Users who achieve this result tend to have fewer SCRs for psychogenic sweating as the difficulty level of the problem increases.

[0273] Figure 55 shows that when the task difference Δhh in the number of SCRs for psychogenic sweating is small, the accuracy rate R of the problem is high, and when the task difference Δhh in the number of SCRs for psychogenic sweating is large in the negative direction, the accuracy rate R of the problem is low. From this, it can be seen that even in difficult problems, people with a large number of SCRs for psychogenic sweating tend to have a high accuracy rate R (i.e., they can answer difficult problems as correctly as easy problems). Conversely, people with a small number of SCRs for psychogenic sweating in difficult problems tend to have a low accuracy rate R (i.e., their accuracy rate on difficult problems decreases).

[0274] From the above, when the task difference Δhh in the number of SCRs for psychogenic sweating is small, it can be inferred that the user's cognitive capacity is higher than the given standard. Conversely, when the task difference Δhh in the number of SCRs for psychogenic sweating is large in the negative direction, it can be inferred that the user's cognitive capacity is lower than the given standard. If the user's cognitive capacity is lower than the given standard, the difficulty level of the problem may be too high for the user (i.e., the load is high). On the other hand, if the user's cognitive capacity is higher than the given standard, the difficulty level of the problem may be too low for the user (i.e., the load is low).

[0275] If a user's cognitive capacity is below a predetermined standard, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhh in the number of SCRs for psychogenic sweating is large in the negative direction, lowering the difficulty of the problem may bring the user's cognitive capacity closer to the standard. Conversely, if a user's cognitive capacity is above a predetermined standard, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard. In other words, if the task difference Δhh in the number of SCRs for psychogenic sweating is small, increasing the difficulty of the problem may bring the user's cognitive capacity closer to the standard.

[0276] From the above, it can be seen that it is possible to control the user's cognitive capacity by using the task difference Δhh in the number of SCRs for psychogenic sweating and the regression equation in Figure 55.

[0277] [effect] Next, the effects of the information processing device 700 and the information processing program 721 according to this embodiment will be described.

[0278] In the information processing device 700 and information processing program 721 according to this embodiment, multiple problem data to be presented to the user are determined based on the user's feature data 723 for the problem data. Here, the Discloser has experimentally obtained the finding that the user's feature data 723 changes depending on the task. Therefore, it is possible to determine the problem data to be presented to the user based on the user's feature data 723. Consequently, cognitive capacity can be derived regardless of whether or not there is a reaction time.

[0279] In the information processing device 700 and information processing program 721 according to this embodiment, the user's cognitive capacity is derived based on the user's feature data 723 for the problem data. This makes it possible to determine the difficulty level of the problem data to be presented to the user and to determine the problem data for subsequent attempts based on the derived cognitive capacity. Therefore, cognitive capacity can be derived regardless of whether or not there is a reaction time.

[0280] <13. Modified Examples of the Fifth Embodiment> In the fifth embodiment described above, the biometric information detection unit 710 may detect biometric information (pulse wave, electrocardiogram, blood flow, or psychogenic sweating) of multiple users. In this case, the signal processing unit 30 derives feature data 723 for each user based on the signal data of the biometric information (pulse wave, electrocardiogram, blood flow, or psychogenic sweating) obtained from each user. The signal processing unit 30 derives cognitive capacity for each user based on the derived feature data 723 and the regression equation 722 read from the memory unit 20. Based on the cognitive capacity derived for each user, the signal processing unit 30 derives the collective cognitive capacity when multiple users are viewed as a group. In this case, for example, it becomes possible to determine how burdensome the task is for the group, or how much leeway the group has for the task.

[0281] <14. Modified Examples of the First to Fifth Embodiments> Next, we will describe modified versions of the information processing devices 1 to 4,700 according to the first to fifth embodiments.

[0282] [Modification H] In the information processing device 2 according to the second embodiment, for example, as shown in Figure 56, the biological information detection unit 60 may be provided separately from the information processing device 2. In this case, the signal processing unit 30 may communicate with the biological information detection unit 60, for example, via the communication unit 70.

[0283] Furthermore, in the information processing device 3 according to the third embodiment, for example, as shown in Figure 57, the biological information detection unit 60 may be provided separately from the information processing device 3. In this case, the signal processing unit 30 may communicate with the biological information detection unit 60, for example, via the communication unit 70.

[0284] Furthermore, in the information processing device 4 according to the fourth embodiment, for example, as shown in Figure 58, the biological information detection unit 60 may be provided separately from the information processing device 4. In this case, the signal processing unit 30 may communicate with the biological information detection unit 60, for example, via the communication unit 70.

[0285] Furthermore, in the information processing device 700 according to the fifth embodiment, for example, as shown in Figure 59, the biosignal detection unit 710 may be provided separately from the information processing device 700. In this case, the signal processing unit 30 may communicate with the biosignal detection unit 710, for example, via the communication unit 70.

[0286] [Modification I] In the information processing device 1 according to the first embodiment, some of the functions of the information processing programs 21, 21a, 21b, and 21g may be performed by an external device configured to communicate with the information processing device 1. In this case, for example, as shown in Figure 60, the information processing system 5 is configured by the information processing device 1 and the server device 6, which are configured to communicate with each other.

[0287] In this modified example, the information processing device 1 includes a storage unit 20 in which the information processing program 21A is stored. The information processing program 21A includes a series of procedures for causing the signal processing unit 30 to execute some of the functions of the information processing programs 21, 21a, 21b, and 21g, for example, in Figure 11, it includes a series of procedures up to the calculation (acquisition) of the reaction time 25. When the information processing program 21A is loaded, the signal processing unit 30 calculates (acquires) the reaction time 25 by executing steps S101, S102, S121, S122, S141, S142, S161, and S162 in Figures 11, 15, 20, 23, and 39. The signal processing unit 30 transmits the calculated (acquired) reaction time 25 to the server device 6 via the communication unit 80. The communication unit 80 is configured to communicate with the server device 6.

[0288] In this modified example, the server device 6 includes, for example, a control unit 61, a communication unit 62, and a storage unit 63. The communication unit 62 is configured to communicate with the information processing device 1 (communication unit 80). The storage unit 63 is, for example, a volatile memory such as DRAM, or a non-volatile memory such as EEPROM or flash memory. The storage unit 63 stores an information processing program 63A that controls the user's cognitive capacity, as well as problem data 22, a regression equation 23, and a difficulty level 24 used in the information processing program 63A. The information processing program 63A includes, for example, a series of procedures that the signal processing unit 30 is instructed to execute in the information processing programs 21, 21a, 21b, and 21g, excluding the series of procedures up to the calculation (acquisition) of the reaction time 25. When the information processing program 63A is loaded, the control unit 61 executes the series of procedures that the signal processing unit 30 is instructed to execute in the information processing programs 21, 21a, 21b, and 21g, excluding the series of procedures up to the calculation (acquisition) of the reaction time 25. As a result, the control unit 61 derives the user's cognitive capacity, determines the difficulty level of the problem data based on the derived cognitive capacity, and writes the determined difficulty level to the setting data in the difficulty level 24 of the storage unit 63, thereby setting the difficulty level of the problems to be presented in subsequent sessions.

[0289] In this modified version, some of the functions of the information processing programs 21, 21a, 21b, and 21g are performed by an external device configured to communicate with the information processing device 1. Even in this case, cognitive capacity can be derived regardless of whether or not there is a correct answer in the reaction time, similar to the information processing device 1 in the first embodiment.

[0290] [Modification J] In the information processing device 2 according to the second embodiment, some of the functions of the information processing program 21c may be performed by an external device configured to communicate with the information processing device 2. In this case, for example, as shown in Figure 61, the information processing system 7 is configured by the information processing device 2 and the server device 6, which are configured to communicate with each other.

[0291] In this modified example, the information processing device 2 includes a storage unit 20 in which the information processing program 21B is stored. The information processing program 21B includes a series of procedures for causing the signal processing unit 30 to execute some of the functions of the information processing program 21c, for example, a series of procedures up to acquiring observation data 28 in Figure 26. When the information processing program 21B is loaded, the signal processing unit 30 acquires the observation data 28 by executing steps S201 and S202 in Figure 26. The signal processing unit 30 transmits the acquired observation data 28 to the server device 6 via the communication unit 80.

[0292] In this modified example, the server device 6 includes, for example, a control unit 61, a communication unit 62, and a storage unit 63. The communication unit 62 is configured to communicate with the information processing device 2 (communication unit 80). The storage unit 63 stores an information processing program 63B that controls the user's cognitive capacity, as well as problem data 22, a regression equation 27, and a difficulty level 24 used in the information processing program 63B. The information processing program 63B includes, for example, a series of procedures that the information processing program 21c has the signal processing unit 30 execute, excluding the series of procedures up to the acquisition of observation data 28. When the information processing program 63B is loaded, the control unit 61 executes the series of procedures that the information processing program 21c has the signal processing unit 30 execute, excluding the series of procedures up to the acquisition of observation data 28. As a result, the control unit 61 derives the user's cognitive capacity, determines the difficulty level of the problem data based on the derived cognitive capacity, and writes the determined difficulty level to the setting data in the difficulty level 24 of the storage unit 63, thereby setting the difficulty level of the problems to be presented in subsequent sessions.

[0293] In this modified example, some of the functions of the information processing program 21c are performed by an external device configured to communicate with the information processing device 2. Even in this case, cognitive capacity can be derived regardless of whether or not there is a reaction time, similar to the information processing device 2 in the second embodiment.

[0294] [Differentiation K] In the information processing device 3 according to the third embodiment, some of the functions of the information processing program 21 may be performed by an external device configured to communicate with the information processing device 3. In this case, for example, as shown in Figure 62, the information processing system 8 is configured by the information processing device 3 and the server device 6, which are configured to communicate with each other.

[0295] In this modified example, the information processing device 3 includes a storage unit 20 in which the information processing program 21C is stored. The information processing program 21C includes a series of procedures for causing the signal processing unit 30 to execute a part of the functions of the information processing program 21d, for example, a series of procedures for acquiring the reaction time 25 and observation data 28 in Figure 29. When the information processing program 21C is loaded, the signal processing unit 30 acquires the reaction time 25 and observation data 28. The signal processing unit 30 transmits the acquired reaction time 25 and observation data 28 to the server device 6 via the communication unit 80.

[0296] In this modified example, the server device 6 includes, for example, a control unit 61, a communication unit 62, and a storage unit 63. The communication unit 62 is configured to communicate with the information processing device 3 (communication unit 80). The storage unit 63 stores an information processing program 63C that controls the user's cognitive capacity, as well as problem data 22, regression equations 23 and 27, and difficulty level 24 used in the information processing program 63C. The information processing program 63C includes, for example, a series of procedures that the information processing program 21d has the signal processing unit 30 execute, excluding the procedures up to acquiring reaction time 25 and observation data 28. When the information processing program 63C is loaded, the control unit 61 executes the series of procedures that the information processing program 21d has the signal processing unit 30 execute, excluding the procedures up to acquiring reaction time 25 and observation data 28. As a result, the control unit 61 derives the user's cognitive capacity, determines the difficulty level of the problem data based on the derived cognitive capacity, and writes the determined difficulty level to the setting data in the difficulty level 24 of the storage unit 63, thereby setting the difficulty level of the problems to be presented in subsequent sessions.

[0297] In this modified example, some of the functions of the information processing program 21d are performed by an external device configured to communicate with the information processing device 3. Even in this case, cognitive capacity can be derived regardless of whether or not there is a reaction time, similar to the information processing device 3 in the third embodiment.

[0298] [Modified version L] In the information processing device 4 according to the fourth embodiment, some of the functions of the information processing program 21e may be performed by an external device configured to communicate with the information processing device 4. In this case, for example, as shown in Figure 63, the information processing system 9 is configured by the information processing device 4 and the server device 6, which are configured to communicate with each other.

[0299] In this modified example, the information processing device 4 includes a storage unit 20 in which the information processing program 21D is stored. The information processing program 21D includes a series of procedures for causing the signal processing unit 30 to execute a part of the functions of the information processing program 21e, for example, a series of procedures for acquiring observation data 43 in Figure 33. When the information processing program 21D is loaded, the signal processing unit 30 acquires the observation data 43 by executing steps S401 and S402 in Figure 33. The signal processing unit 30 transmits the acquired observation data 43 to the server device 6 via the communication unit 80.

[0300] In this modified example, the server device 6 includes, for example, a control unit 61, a communication unit 62, and a storage unit 63. The communication unit 62 is configured to communicate with the information processing device 4 (communication unit 80). The storage unit 63 stores an information processing program 63D that controls the user's cognitive capacity, as well as problem data 22 used in the information processing program 63D, a regression equation 41, difficulty level 24, length of division period ΔT 44, length of overlap period Δd1 45, length of division period ΔW 46, and length of overlap period Δd2 47. The information processing program 63D includes, for example, a series of procedures that the information processing program 21e has the signal processing unit 30 execute, excluding the series of procedures up to acquiring observation data 43. When the information processing program 63D is loaded, the control unit 61 executes the series of procedures that the information processing program 21e has the signal processing unit 30 execute, excluding the series of procedures up to acquiring observation data 43. As a result, the control unit 61 derives the user's cognitive capacity, determines the difficulty level of the problem data based on the derived cognitive capacity, and writes the determined difficulty level to the setting data in the difficulty level 24 of the storage unit 63, thereby setting the difficulty level of the problems to be presented in subsequent sessions.

[0301] In this modified example, some of the functions of the information processing program 21e are performed by an external device configured to communicate with the information processing device 4. Even in this case, cognitive capacity can be derived regardless of whether or not there is a reaction time, similar to the information processing device 4 in the fourth embodiment.

[0302] [Differentiation M] In the modified example J described above, for example, as shown in Figure 64, the biological information detection unit 60 may be provided separately from the information processing device 2. In this case, the signal processing unit 30 may communicate with the biological information detection unit 60, for example, via the communication unit 90.

[0303] [Differentiation N] In the modified example K described above, for example, as shown in Figure 65, the biological information detection unit 60 may be provided separately from the information processing device 3. In this case, the signal processing unit 30 may communicate with the biological information detection unit 60, for example, via the communication unit 90.

[0304] [Modification O] In the above modified example L, for example, as shown in Figure 66, the biological information detection unit 60 may be provided separately from the information processing device 4. In this case, the signal processing unit 30 may communicate with the biological information detection unit 60, for example, via the communication unit 90.

[0305] [Modified Version P] In the first embodiment described above, for example, game data 49 shown in Figure 67 may be provided instead of problem data 22. In this case, the user's response may be to input a response corresponding to the game data 49 to the input receiving unit 10. At this time, the input receiving unit 10 receives the input from the user as a response corresponding to the game data 49 and outputs the received response to the signal processing unit 30.

[0306] Game data 49 includes multiple game data sets with different difficulty levels. The game data corresponds to one specific example of the "request" and "problem" in this disclosure. Game data 49 also includes data on the difficulty level of each game data set included in game data 49. Game data 49 may further include correct answer data for each game data set.

[0307] Difficulty level 24 includes, for example, setting data for the difficulty level of the game provided to the user, and a table describing the correspondence between the game difficulty level and the user's cognitive capacity. The setting data included in difficulty level 24 includes multiple difficulty levels as initial values, or multiple difficulty levels after being modified by the information processing program 21. The table included in difficulty level 24 sets difficulty levels according to the user's cognitive capacity. For example, if the user's cognitive capacity is α, the table included in difficulty level 24 sets multiple difficulty levels according to cognitive capacity α. Because multiple difficulty levels are set according to cognitive capacity α, the information processing program 21 is able to provide the user with games of multiple difficulty levels.

[0308] The signal processing unit 30 reads game data of multiple difficulty levels corresponding to the setting data included in difficulty level 24 from the game data 49, and sequentially outputs the read game data of multiple difficulty levels to the stimulus control unit 40. When the signal processing unit 30 obtains a response corresponding to the game data 49 from the input receiving unit 10, it derives the reaction time 25 based on the input timing of the obtained response. When the signal processing unit 30 has completed providing a predetermined number of response N game data, it calculates the task difference Δtv of the variation in reaction time 25. The signal processing unit 30 derives cognitive capacity using the calculated task difference Δtv and the regression equation 23 read from the memory unit 20. The signal processing unit 30 determines the difficulty level of the game to be provided next time and beyond using the table included in difficulty level 24 read from the memory unit 20. The signal processing unit 30 sets the difficulty level of the game to be provided next time and beyond by writing the determined difficulty level to the setting data in the memory unit 20.

[0309] The stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 based on the game data input from the signal processing unit 30. The stimulus control unit 40 outputs the generated control signal to the stimulus presentation unit 50. If the stimulus presentation unit 50 is a display panel, the stimulus control unit 40 generates a video signal as a control signal that displays the game data input from the signal processing unit 30. The stimulus presentation unit 50 presents a stimulus to the user based on the control signal input from the stimulus control unit 40. If the stimulus presentation unit 50 is a display panel, the stimulus presentation unit 50 presents the user with a video containing multiple game data of different difficulty levels based on the video signal input from the stimulus control unit 40.

[0310] Next, the operation of the information processing device 1 according to this modified example will be described.

[0311] First, the signal processing unit 30 reads game data for multiple difficulty levels corresponding to the setting data included in difficulty level 24 from the game data 49, and sequentially outputs the read game data for multiple difficulty levels to the stimulus control unit 40. Based on the game data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0312] The stimulus presentation unit 50 presents the user with stimuli based on game data of a predetermined difficulty level, based on control signals input from the stimulus control unit 40. For example, the stimulus presentation unit 50 presents the user with a video containing game data. The user then inputs a response corresponding to the game data into the input receiving unit 10. When the input receiving unit 10 receives a response corresponding to the game data from the user, it outputs the acquired response to the signal processing unit 30. When the signal processing unit 30 receives a response from the input receiving unit 10, it calculates (acquires) the reaction time 25 of the response corresponding to the game data.

[0313] The signal processing unit 30 performs the above series of processes until it has finished providing a game with a predetermined number of responses N. Once it has finished providing a game with a predetermined number of responses N, the signal processing unit 30 calculates the task difference Δtv of the variation in reaction times 25 acquired so far. The signal processing unit 30 derives cognitive capacity using the calculated task difference Δtv and the regression equation 23 read from the memory unit 20. The signal processing unit 30 determines the difficulty level of the game to be provided next time and beyond using the table included in the difficulty level 24 read from the memory unit 20. The signal processing unit 30 changes the difficulty level of the game to be provided next time and beyond by writing the determined difficulty level to the setting data of the memory unit 20.

[0314] The signal processing unit 30 performs the above series of processes until it has finished providing a predetermined number of games. Once it has finished providing the predetermined number of games, the signal processing unit 30 terminates the provision of games.

[0315] Next, the procedure for deriving the regression equation 23 in the information processing device 1 according to this modified example will be explained. First, the signal processing unit 30 reads out game data of multiple difficulty levels corresponding to the setting data included in the difficulty level 24 from the game data 49, and sequentially outputs the read game data of multiple difficulty levels to the stimulus control unit 40. Based on the game data input from the signal processing unit 30, the stimulus control unit 40 generates a control signal to control the stimulus presentation unit 50 and outputs it to the stimulus presentation unit 50.

[0316] The stimulus presentation unit 50 presents the user with stimuli based on game data of a predetermined difficulty level, based on control signals input from the stimulus control unit 40. For example, the stimulus presentation unit 50 presents the user with a video containing game data. The user then inputs a response corresponding to the game data into the input reception unit 10. The input reception unit 10 receives the response corresponding to the game data from the user. The input reception unit 10 outputs the received response to the signal processing unit 30. When the signal processing unit 30 receives the response from the input reception unit 10, it uses the correct answer data contained in the game data 49 to determine whether the response corresponding to the game data is correct or incorrect. The signal processing unit 30 calculates (obtains) the reaction time 25 and the accuracy rate of the response corresponding to the game data.

[0317] The signal processing unit 30 performs the above series of processes until it has finished providing a game with a predetermined number of responses N. Once it has finished providing a game with a predetermined number of responses N, the signal processing unit 30 calculates the task difference Δtv of the variation in reaction time 25 acquired so far, and the task difference ΔR of the accuracy rate of the responses acquired so far. Based on the calculated task differences Δtv and ΔR, the signal processing unit 30 derives a regression equation 23 and stores the derived regression equation 23 in the storage unit 20.

[0318] The information processing device 1 may perform a series of steps for deriving the regression equation 23 separately from (i.e., in advance) the series of steps for changing the difficulty level in the information processing device 1. In this case, the user who responds to the game for deriving the regression equation 23 and the user who responds to the game in the series of steps for changing the difficulty level in the information processing device 1 may be the same or different. The information processing device 1 may also perform the series of steps for deriving the regression equation 23 within the series of steps for changing the difficulty level in the information processing device 1.

[0319] In the information processing device 1 and information processing program 21 relating to this modified example, the difficulty level of the multiple game data to be provided in subsequent sessions is determined based on the user's cognitive capacity, which is obtained based on the variability of reaction times 25 corresponding to multiple game data of different difficulty levels. Here, the Discloser has experimentally obtained the finding that the variability of reaction times 25 changes depending on the task. Therefore, it is possible to derive the user's cognitive capacity based on the variability of reaction times 25, and to determine the difficulty level of the multiple game data to be presented in subsequent sessions based on the derived cognitive capacity. Thus, cognitive capacity can be derived regardless of whether or not there is a correct answer within the reaction time.

[0320] [Differentiation Example Q] In each of the above embodiments and their variations, for example, the game data 49 shown in Figure 67 may be provided instead of the problem data 22. Even in this case, cognitive capacity can be derived regardless of whether or not there is a correct answer in the reaction time, or regardless of whether or not there is a reaction time.

[0321] [Modification R] In the first embodiment and its modified form described above, for example, as shown in Figure 68, an action recording unit 100 may be provided instead of the input receiving unit 10. Also, in the second, third, fourth, and fifth embodiments and their modified forms described above, for example, as shown in Figures 69, 70, 71, and 72, an action recording unit 100 may be provided instead of the input receiving unit 10.

[0322] Furthermore, in the above modified example I, for example, as shown in Figure 73, an action recording unit 100 may be provided instead of the input receiving unit 10. Also, in the above modified examples J, K, and L, for example, as shown in Figure 74, an action recording unit 100 may be provided instead of the input receiving unit 10. Also, in the above modified examples M, N, and O, for example, as shown in Figure 75, an action recording unit 100 may be provided instead of the input receiving unit 10.

[0323] The behavior recording unit 100 acquires the user's behavior log. The behavior recording unit 100 is composed of, for example, a camera, and outputs video (sequential still images or video) obtained by, for example, photographing the user and the question paper provided to the user with the camera to the signal processing unit 30. The signal processing unit 30 derives the reaction time 25 based on the acquired behavior log. The signal processing unit 30 derives the reaction time 25 based on video (sequential still images or video) input from the behavior recording unit 100.

[0324] The behavior recording unit 100 may, for example, provide information corresponding to the stop of a stopwatch when measuring the reaction time 25, and may detect the user's response actions and input actions. The behavior recording unit 100 may, for example, detect button clicks, game controller operations, or head movements using a head-mounted display. The behavior recording unit 100 may, for example, track eye movements using an image sensor or the like.

[0325] In this modified example, an action recording unit 100 is provided instead of the input receiving unit 10. Even in this case, it is possible to derive the reaction time 25 based on the video (sequential still images or video) input from the action recording unit 100. Therefore, as with the above embodiments and their modified examples, cognitive capacity can be derived regardless of whether there is a correct reaction time or not, or regardless of whether there is a reaction time or not.

[0326] The effects described herein are for illustrative purposes only. The effects of this disclosure are not limited to those described herein. This disclosure may have effects other than those described herein.

[0327] For example, the series of processes described above can be executed by software, but they can also be executed by hardware.

[0328] Furthermore, in the above-described embodiments and their variations, the signal processing unit 30 determines the difficulty level of a problem using the difficulty level 24, and this determination also includes determining (selecting) the problems to be presented in subsequent sessions. Therefore, in the above-described embodiments and their variations, the signal processing unit 30 determines the difficulty level of a problem using the difficulty level 24, and also determines (selects) a problem from the problem data 22 that corresponds to the determined difficulty level.

[0329] Furthermore, in the above-described embodiments and their variations, the questions to which the user responds (answers) and the questions to which the user makes a decision (selects) using difficulty level 24 may be data belonging to a common field. Also, in variation N, the game to which the user responds (answers) and the game to which the user makes a decision (selects) using difficulty level 24 may be data belonging to a common field. For example, the questions to which the user responds (answers) and the questions to which the user makes a decision (selects) using difficulty level 24 may correspond to question data in the learning of a specific subject.

[0330] In addition, in the above-described embodiments and their variations, the data that the user responds to (answers) and the data that the user determines (selects) using difficulty level 24 may belong to different fields. Furthermore, in variation N, the data that the user responds to (answers) and the data that the user determines (selects) using difficulty level 24 may belong to different fields. For example, the problem the user responds to (answers) may be a puzzle problem, and the problem the user determines (selects) using difficulty level 24 may be a game of a difficulty level determined (selected) using difficulty level 24.

[0331] Furthermore, each of the above embodiments and their variations can be applied to applications that require objective cognitive capacity, such as games, healthcare, learning, training for sports competitions, and training for human resource development. In this case, in each of the above embodiments and their variations, at least one of the following may be used instead of problem data 22 (problem data in learning): game data, item data in healthcare, item data in training for sports competitions, and item data in training for human resource development. In other words, this disclosure is applicable to a variety of fields. At least one of the following may be considered a specific example of the "request" or "problem" of this disclosure.

[0332] Furthermore, in the above-described embodiments and their modifications, a detection unit for detecting biological signals other than electroencephalograms may be provided instead of the biological information detection unit 60. Also, in the above-described embodiments and their modifications, the measurement target may be a living organism other than a human (for example, an animal).

[0333] Furthermore, in the regression equations relating to the above-described embodiments and their modifications, for example, as shown in Figure 76, the task difference Δtv of the median reaction time may be used instead of the task difference Δtv of the variability of reaction time.

[0334] Furthermore, in the above-described embodiments and their variations, the regression equation is not limited to a straight line (regression line), but may also be a curve (regression curve), for example. The curve (regression curve) may be a quadratic function, for example. The regression equation defining the relationship between the level of alertness k[%] and the accuracy rate R[%] may be a quadratic function (R=a×k) as shown in Figure 77, for example. 2 It may also be defined as +bk+c).

[0335] Furthermore, for example, this disclosure can take the following configuration. (1-1) The system includes a decision unit that determines the task to the user based on the user's cognitive capacity (cognitive resource), which is obtained based on the variability of the user's response time to multiple requests. Information processing device. (1-2) The system includes a decision unit that determines the task for the user based on the variability in the user's response time to multiple requests. Information processing device. (1-3) The system includes a modification unit that changes the tasks assigned to the user based on the variability in the user's response time to multiple requests. Information processing device. (2) The system further includes an acquisition unit for acquiring the aforementioned reaction time. An information processing device as described in any one of (1-1), (1-2), and (1-3). (3) The acquisition unit acquires the reaction time based on information from the sensor. (2) The information processing device described above. (4) The system further includes a derivation unit that derives the user's cognitive capacity based on the aforementioned variability in reaction time. An information processing device as described in any one of (1-1), (1-2), (1-3), (2), and (3). (5) The derivation unit derives the cognitive capacity based on the difference in the variability of reaction time and regression data on the difference in the variability of reaction time. (3) The information processing device described above. (6) The derivation unit derives the cognitive capacity based on the variability of the reaction time and regression data on the variability of the reaction time. The information processing device described in (1-1). (7) The system further comprises a decision unit that determines the task to the user based on the aforementioned cognitive capacity. The information processing device described in (1-3). (8) The decision unit modifies the task for the user based on the cognitive capacity. (7) The information processing device described above. (9) The acquisition unit derives the response time based on the timing of the user's response input or the action log for the multiple requests. (2) The information processing device described above. (10) The presenting unit further comprises a display unit that presents the aforementioned multiple requests. An information processing device as described in any one of (1-1), (1-2), (1-3), (2), (3), (4), (5), (6), (7), (8), and (9). (11) The decision unit determines a number of subsequent requests based on the variation in response times. (10) The information processing device described above. (12) The determination unit determines the difficulty level of the task based on the variation in reaction time. The information processing device described in (1-1). (13) The determination unit changes the difficulty level of the task based on the variation in reaction time. (12) The information processing device described above. (14) The aforementioned requests correspond to game data, item data in healthcare, problem data in learning, item data in training for sports competitions, and item data in training for human resource development. An information processing device as described in any one of (1-1), (1-2), (1-3), (2), (3), (4), (5), (6), (7), (8), (9), (10), (11), (12), and (13). (15-1) The system includes a derivation unit that derives the user's cognitive capacity based on the user's biosignals in response to a request. Information processing device. (15-2) The system includes a decision unit that determines the task to be given to the user based on the user's biometric signals in response to the request. Information processing device. (16) The aforementioned biological signal is time-series data. An information processing device as described in either (15-1) or (15-2). (17) The derivation unit derives the user's cognitive capacity based on fluctuations in components of a specific frequency band included in the biosignal. (16) The information processing device described above. (18) The derivation unit derives the cognitive capacity based on the fluctuation task difference and regression data regarding the fluctuation task difference. The information processing device described in (15-1). (19) The derivation unit derives the cognitive capacity based on the fluctuations and regression data for the fluctuations. The information processing device described in (15-1). (20) The system further comprises a decision unit that determines the task to the user based on the aforementioned cognitive capacity. The information processing device described in (15-1). (twenty one) The decision unit modifies the task for the user based on the cognitive capacity. (20) The information processing device described above. (twenty two) The system further includes an acquisition unit for acquiring the aforementioned biological signals. An information processing device as described in any one of (15-1), (15-2), (16), (17), (18), (19), (20), and (21). (twenty three) The system further includes a detection unit that detects the user's biosignals and outputs them to the acquisition unit. (22) The information processing device described above. (twenty four) The system further includes a presentation unit that presents the aforementioned request. An information processing device as described in any one of (15-1), (15-2), (16), (17), (18), (19), (20), (21), (22), and (23). (twenty five) The aforementioned determination unit determines the next request based on the fluctuations in the biological signal. (24) The information processing device described above. (26) The determination unit determines the difficulty level of the task based on the fluctuations of the biological signal. (20) The information processing device described above. (27) The aforementioned biosignals correspond to the user's electroencephalogram, pulse wave, electrocardiogram, blood flow, or psychoactive sweating. An information processing device as described in any one of (15-1), (15-2), (16), (17), (18), (19), (20), (21), (22), (23), (24), (25), and (26). (28) The aforementioned request corresponds to at least one of the following: game data, item data in healthcare, problem data in learning, item data in training for sports competition, and item data in training for human resource development. An information processing device as described in any one of (15-1), (15-2), (16), (17), (18), (19), (20), (21), (22), (23), (24), (25), (26), and (27). (29-1) A characteristic value generation unit generates characteristic values ​​for each observation data of the target waveform based on multiple partial observation data with observation periods shorter than the observation period of the observation data, which are included in each observation data obtained from the user's biological observation over a predetermined period. An evaluation value generation unit generates an evaluation value for the difference between the observed data with respect to the observed waveform, based on the characteristic value for each of the observed data generated by the characteristic value generation unit, Based on the evaluation values ​​generated by the evaluation value generation unit, a decision unit determines the task for the user based on the user's cognitive capacity. Equipped with Information processing device. (29-2) A characteristic value generation unit generates characteristic values ​​for each observation data of the target waveform based on multiple partial observation data with observation periods shorter than the observation period of the observation data, which are included in each observation data obtained from the user's biological observation over a predetermined period. An evaluation value generation unit generates an evaluation value for the difference between the observed data with respect to the observed waveform, based on the characteristic value for each of the observed data generated by the characteristic value generation unit, Based on the evaluation values ​​generated by the evaluation value generation unit, a determination unit determines the task for the user. Equipped with Information processing device. (30) The system further includes a derivation unit that derives the user's cognitive capacity based on the aforementioned evaluation value. An information processing device as described in either (29-1) or (29-2). (31) The derivation unit derives the cognitive capacity based on the difference in the evaluation values ​​and regression data regarding the difference in the evaluation values. (24) The information processing device described above. (32) It further includes a presentation section for presenting requests. An information processing device as described in any one of (29-1), (29-2), (30), and (31). (33) The decision unit determines the next request based on the evaluation value. (32) The information processing device described above. (34) The determination unit determines the difficulty level of the task based on the evaluation value. An information processing device as described in any one of (29-1), (29-2), (30), (31), (32), (33), and (34). (35) The determination unit changes the task for the user based on the fluctuations in the biological signal. (34) The information processing device described above. (36) Each of the aforementioned observational data corresponds to the user's electroencephalogram, pulse wave, electrocardiogram, blood flow, or psychogenic sweating. An information processing device as described in any one of (29-1), (29-2), (30), (31), (32), (33), (34), and (35). (37) The system further includes a detection unit for detecting each of the aforementioned observation data. An information processing apparatus as described in any one of (29-1), (29-2), (30), (31), (32), (33), (34), (35), and (36). (38) The aforementioned request corresponds to at least one of the following: game data, item data in healthcare, problem data in learning, item data in training for sports competition, and item data in training for human resource development. An information processing apparatus as described in any one of (29-1), (29-2), (30), (31), (32), (33), (34), (35), (36), and (37). (39-1) The computer is instructed to determine a task for the user based on the user's cognitive capacity, which is determined based on the variability in the user's response times to multiple requests. Information processing program. (39-2) The computer is instructed to determine a task for the user based on the variability in the user's response time to multiple requests. Information processing program. (39-3) The computer is instructed to change the tasks assigned to the user based on the variability in the user's response time to multiple requests. Information processing program. (39-4) The computer is instructed to determine a task for the user based on the user's cognitive capacity, which is derived from the user's biosignals in response to the request. Information processing program. (39-5) The computer is instructed to derive the user's cognitive capacity based on the user's biometric signals in response to a request. Information processing program. (39-6) The computer is made to determine a task for the user based on the user's biometric signals in response to a request. Information processing program. (39-7) Based on multiple partial observation data with shorter observation periods than the observation period of the aforementioned observation data, which are included in each observation data obtained from the user's biological observation over a predetermined period, characteristic values ​​of the observed waveform are generated for each observation data. Based on the characteristic values ​​for each of the generated observation data, an evaluation value is generated regarding the difference between the observation data with respect to the observed waveform. Based on the cognitive capacity of the user obtained from the generated evaluation values, the task to be given to the user is determined. Make the computer execute it. Information processing program. (39-8) Based on multiple partial observation data with shorter observation periods than the observation period of the aforementioned observation data, which are included in each observation data obtained from the user's biological observation over a predetermined period, characteristic values ​​of the observed waveform are generated for each observation data. Based on the characteristic values ​​for each of the generated observation data, an evaluation value is generated regarding the difference between the observation data with respect to the observed waveform. Based on the generated evaluation values, the tasks for the user are determined. Make the computer execute it. Information processing program.

[0336] In the information processing device relating to the first aspect of this disclosure, a task is determined for the user based on the variability of the user's reaction times in response to multiple requests. Here, the Distributor has experimentally obtained the finding that the variability of reaction times changes depending on the task. Therefore, it is possible to determine the task for the user based on the variability of reaction times. Consequently, cognitive capacity can be detected regardless of whether or not there is a correct answer in the reaction time.

[0337] In the information processing device relating to the second aspect of this disclosure, a task is determined for the user based on fluctuations in the user's biological signals in a specific frequency band in response to a request. Here, the Distributor has experimentally obtained the finding that fluctuations in the user's biological signals in a specific frequency band change depending on the task. Therefore, it is possible to determine a task for the user based on fluctuations in the user's biological signals in a specific frequency band. Consequently, cognitive capacity can be detected regardless of whether or not there is a reaction time.

[0338] In the information processing device relating to the third aspect of this disclosure, characteristic values ​​are derived from each observation data obtained through the observation of the user's biological data over a predetermined period, and evaluation values ​​for the differences between observation data relating to the observed waveform are generated based on the derived characteristic values ​​for each observation data. Then, a task for the user is determined based on the generated evaluation values. Here, the Distributor has experimentally obtained the finding that the above evaluation values ​​change depending on the task. Therefore, it is possible to determine a task for the user based on the above evaluation values. Consequently, cognitive capacity can be detected regardless of whether or not there is a reaction time.

[0339] In the information processing program relating to the fourth aspect of this disclosure, the task to the user is determined based on the variability of the user's response times to multiple requests. Here, the Distributor has experimentally obtained the finding that the variability of response times changes depending on the task. Therefore, it is possible to determine the task to the user based on the variability of response times. Consequently, cognitive capacity can be detected regardless of whether or not there is a correct response time.

[0340] In the information processing device program relating to the fifth aspect of this disclosure, a task is determined for the user based on the user's biosignals in response to a request. Here, the Distributor has experimentally obtained the finding that the user's biosignals change depending on the task. Therefore, it is possible to determine a task for the user based on the user's biosignals. Consequently, cognitive capacity can be detected regardless of whether or not there is a reaction time.

[0341] In the information processing program relating to the sixth aspect of this disclosure, characteristic values ​​are derived from each observation data obtained through biological observation of the user over a predetermined period, and evaluation values ​​for the differences between observation data regarding the observed waveform are generated based on the derived characteristic values ​​for each observation data. Then, a task for the user is determined based on the generated evaluation values. Here, the Distributor has experimentally obtained the finding that the above evaluation values ​​change depending on the task. Therefore, it is possible to derive the user's cognitive capacity based on the above evaluation values, and to determine a task for the user based on the derived cognitive capacity. Thus, cognitive capacity can be detected regardless of whether or not there is a reaction time.

[0342] In the information processing device relating to the seventh aspect of this disclosure, the task presented to the user is changed based on the variability of the user's reaction times in response to multiple requests. Here, the Distributor has experimentally obtained the finding that the variability of reaction times changes depending on the task. Therefore, it is possible to change the task presented to the user based on the variability of reaction times. Consequently, cognitive capacity can be detected regardless of whether or not there is a correct answer in the reaction time.

[0343] In the information processing device relating to the eighth aspect of this disclosure, the task presented to the user is changed based on fluctuations in the user's biosignals in response to a request. Here, the Distributor has experimentally obtained the finding that the user's biosignals change depending on the task. Therefore, it is possible to change the task presented to the user based on fluctuations in the user's biosignals. Consequently, cognitive capacity can be detected regardless of whether or not there is a correct answer in the reaction time.

[0344] This application claims priority based on Japanese Patent Application No. 2020-072585, filed with the Japan Patent Office on 14 April 2020, and Japanese Patent Application No. 2020-203058, filed on 7 December 2020, and all contents of these applications are incorporated herein by reference.

[0345] Those skilled in the art will understand that various modifications, combinations, subcombinations, and changes can be conceived depending on design requirements and other factors, and that these fall within the scope of the attached claims and their equivalents.

Claims

1. A derivation unit that derives the user's cognitive capacity (cognitive resource) based on the variability of the user's reaction times to multiple tasks given to the user, A decision unit sets a difficulty level based on the cognitive capacity, and determines multiple tasks of varying difficulty levels corresponding to the set difficulty level as multiple new tasks to be given to the user in subsequent visits. Equipped with Information processing device.

2. The derivation unit derives the cognitive capacity based on the task difference, which is the difference in the variability of reaction time between high-difficulty tasks and low-difficulty tasks, and regression data on the task difference. The information processing apparatus according to claim 1.

3. The derivation unit derives the cognitive capacity based on the variability of the reaction time and regression data on the variability of the reaction time. The information processing apparatus according to claim 1.

4. The determination unit, each time the setting difficulty level is set, determines a plurality of tasks of difficulty corresponding to the set difficulty level as the plurality of new tasks. The information processing apparatus according to any one of claims 1 to 3.

5. The system further includes an acquisition unit for acquiring the aforementioned reaction time. The information processing apparatus according to any one of claims 1 to 4.

6. The acquisition unit derives the reaction time based on the timing of the user's response input or the action log for multiple tasks given to the user. The information processing apparatus according to claim 5.

7. The presenting unit further includes a unit that presents the aforementioned multiple problems. The information processing apparatus according to any one of claims 1 to 6.

8. It further includes a memory unit that stores a table describing the correspondence between difficulty level and cognitive capacity. The determination unit reads the difficulty level for the cognitive capacity derived by the derivation unit from the table, and sets the read difficulty level as the set difficulty level. The information processing apparatus according to any one of claims 1 to 7.

9. The memory unit further stores multiple tasks of varying difficulty levels. The determination unit reads from the storage unit multiple tasks of varying difficulty levels corresponding to the set difficulty level, as multiple new tasks to be given to the user on subsequent occasions. The information processing apparatus according to claim 8.

10. The cognitive capacity (cognitive resource) of the user is derived based on the variability of the user's reaction times to multiple tasks given to the user, Based on the cognitive capacity, a difficulty level is set, and multiple tasks of varying difficulty levels are determined to be given to the user in subsequent sessions. Make the computer execute it. Information processing program.

11. The cognitive capacity is derived based on the task difference, which is the difference in reaction time variability between high-difficulty tasks and low-difficulty tasks, and regression data on the task difference. Make the computer execute it. The information processing program according to claim 10.

12. The cognitive capacity is derived based on the variability of the reaction time and regression data on the variability of the reaction time. Make the computer execute it. The information processing program according to claim 10.

13. Each time the aforementioned difficulty level is set, multiple tasks of a difficulty level corresponding to the aforementioned difficulty level are determined as the multiple new tasks. Make the computer execute it. An information processing program according to any one of claims 10 to 12.

14. To obtain the aforementioned reaction time. Make the computer execute it. An information processing program according to any one of claims 10 to 13.

15. The reaction time is derived based on the timing of the user's response input or their behavioral logs to multiple tasks given to the user. Make the computer execute it. The information processing program according to claim 14.

16. To present the aforementioned multiple challenges. Make the computer execute it. An information processing program according to any one of claims 10 to 15.

Images