Method and device for realizing real monitoring picture coding task normal form under eye movement tracker, processor and computer readable storage medium

Abstract

The invention relates to a method for realizing a real monitoring picture coding task normal form under an eye movement tracker. The method comprises the following steps: constructing a real monitoring picture task normal form material library; designing a real monitoring picture task normal form; collecting eye movement tracking data; extracting behavioristics and eye movement data results; behavior indexes related to real monitoring are calculated; calculating eye movement indexes and establishing an eye movement index data set; constructing a comprehensive feature data set; and calculating and analyzing characteristic eye movement indexes reflecting the real monitoring ability. The method, the device, the processor and the computer readable storage medium for realizing the real monitoring picture coding task paradigm under the eye movement tracker have the remarkable advantages of high adaptability, high objectivity and multi-dimensional fusion, the limitation of a single data dimension is made up by combining high-resolution eye movement data and behavioral indexes, and the real monitoring picture coding task paradigm is realized. The method realizes refined evaluation of individualized differences, and has wide application prospects and important practical values.

Description

Technical Field

[0001] The present invention relates to the field of eye trackers, and in particular to the field of reality monitoring ability detection for schizophrenia patients, and specifically refers to a method, device, processor and computer-readable storage medium thereof for implementing a reality monitoring picture encoding task paradigm under an eye tracker. Background Art

[0002] Reality monitoring is the cognitive process of distinguishing endogenous from exogenous information. It is essential to maintaining the normal operation of our daily lives. It helps us identify our own thoughts and feelings and distinguish them from real experiences or events told by others. Schizophrenia patients generally have impaired reality monitoring ability (Simons et al., 2017). They tend to attribute internally generated "imaginary" information to real events in the outside world. Reality monitoring deficits are considered to be the cognitive basis of hallucination symptoms in mental illness ( et al., 2023), has a well-recognized neural mechanism involving medial prefrontal dysfunction and paracingulate sulcus structural abnormalities ( et al., 2023; Simons et al., 2017). Real-time neurofeedback training can significantly improve the medial prefrontal function related to reality monitoring, and may also improve reality monitoring ability (Garrison et al. 2021). Therefore, if the reality monitoring ability of patients with mental illness can be effectively detected, targeted neural regulation can be performed accordingly, which is expected to improve the patient's reality monitoring function and improve hallucination symptoms. However, the traditional paradigm for detecting reality monitoring requires subjects to memorize and perform semantic encoding, which has certain requirements for cultural level and cognitive function (Chen Ying et al., 2021). Schizophrenia patients have significantly impaired cognitive function, and their cultural level is significantly lower than that of normal controls of the same age. It is more difficult for them to complete tasks such as vocabulary recognition, word pair completion, or semantic evaluation. Therefore, it is difficult to promote the detection of reality monitoring ability in patient groups.

[0003] Eye movement is an important part of visual perception in daily life and has high social value. Because its nervous system has many simplified properties, it provides a good model system for exploring neuroscience problems, and also provides an effective behavioral measurement indicator for exploring the high-level cognitive processes related to the human cerebral cortex and subcortex (Lisberger, 2021). Eye movement defects are considered to be an independent phenotypic feature of schizophrenia (Holzman, 1992) and can effectively identify early individuals with schizophrenia (Zhang et al., 2022). The eye movement process includes three basic movement modes, namely fixation, saccade and smooth pursuit movement. Fixation is the period of time that the eyeball stays in one position, which is usually used to measure the concentration of attention; saccade is the process of the eyeball moving quickly from one fixation point to another, reflecting the way of obtaining information; smooth pursuit movement refers to the continuous and smooth movement of the eyeball to follow the target of smooth movement in the visual field. Van Tricht et al. found that the smooth pursuit movement of prodromal patients was different from that of normal people, mainly manifested by an increase in the number of saccades during the pursuit process (2010); Nieman et al. found that prodromal patients showed an increased error rate of reverse saccades, which was significantly correlated with working memory defects (2007); Zhang Dan et al. found that the saccade amplitudes at different time periods in the free view were also significantly different from those of normal subjects, and the saccade amplitude had a sensitive predictive validity for the severity of symptoms in clinical high-risk groups (2023). Some studies have divided high-risk groups into early (mainly cognitive defects or functional impairment) and late (weakened positive symptoms or transient intermittent psychosis). The results showed that the latency of reverse saccades in late-stage high-risk groups was significantly prolonged, and the latency of the three groups of early high-risk, late high-risk, and first-episode schizophrenia showed a significant increasing trend (Kleineidam, 2019). As a quick, simple, and non-invasive examination tool, eye movement examination can more intuitively reflect the high-level cognitive function defects of patients with schizophrenia and its prodromal stage. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, device, processor and computer-readable storage medium thereof that meet the requirements of strong adaptability, multi-dimensional fusion and a wide range of applications for realizing a real-life monitoring picture encoding task paradigm under an eye tracker.

[0005] In order to achieve the above-mentioned purpose, the method, device, processor and computer-readable storage medium for implementing the reality monitoring picture encoding task paradigm under the eye tracker of the present invention are as follows:

[0006] The method for implementing the reality monitoring picture encoding task paradigm under the eye tracker is mainly characterized in that the method comprises the following steps:

[0007] (1) Select 48 different pairs of related objects;

[0008] (2) Produce 48 pairs of pictures of related objects and build a material library of the real-life monitoring picture task paradigm;

[0009] (3) Design a reality monitoring picture task paradigm, and divide the experiment into two groups, each group includes an encoding phase and a test phase, and each phase presents 24 picture stimulus materials;

[0010] (4) A pre-experiment was conducted on the subjects using an eye tracker to test the reality monitoring function and collect eye tracking data;

[0011] (5) Extract behavioral and eye movement data results;

[0012] (6) Calculate behavioral indicators related to reality monitoring, including perceptual misattribution and imaginary misattribution;

[0013] (7) Calculate eye movement indicators and establish an eye movement indicator data set;

[0014] (8) Combine behavioral indicators and eye movement indicators to construct a comprehensive feature dataset;

[0015] (9) Calculate and analyze characteristic eye movement indicators that reflect real-world monitoring capabilities.

[0016] Preferably, the step (2) specifically comprises the following steps:

[0017] (2.1) Select 24 pairs of related objects and present them completely in the pictures, that is, each of the 24 pictures contains two related objects;

[0018] (2.2) partially presenting paired objects, i.e., the other 24 images contain only one object;

[0019] (2.3) Set location conditions;

[0020] (2.4) Set up stimulus materials for the encoding phase and present them in different combinations;

[0021] (2.5) Set up the task materials for the test phase, presenting an object under the perception condition or an object under the imagination condition in a picture.

[0022] Preferably, the step (3) specifically comprises the following steps:

[0023] (3.1) In the encoding phase, the four condition combination pictures were randomly presented;

[0024] (3.2) During the testing phase, each image corresponds to only one task requirement, which is used for real-world monitoring detection or spatial position judgment;

[0025] (3.3) Set the region of interest;

[0026] (3.4) Add markers to the triggers that mark the start and end of stimulus presentation during the encoding and test phases.

[0027] Preferably, the step (4) specifically comprises the following steps:

[0028] (4.1) During the encoding phase, the subject's associations and words are matched with the actual objects presented;

[0029] (4.2) During the test phase, determine whether the object is actually seen or imagined, or determine the location of the object.

[0030] Preferably, the step (6) specifically comprises the following steps:

[0031] (6.1) Calculate perceptual misattribution;

[0032] (6.2) Computational imagination misattribution.

[0033] Preferably, the step (7) specifically comprises the following steps:

[0034] (7.1) Calculate the total number of fixations;

[0035] (7.2) Calculate the total fixation duration;

[0036] (7.3) Calculate the discreteness of the gaze point distribution;

[0037] (7.4) Calculate gaze entropy;

[0038] (7.5) Calculate the gaze point skewness;

[0039] (7.6) Calculate the scan amplitude;

[0040] (7.7) Calculate scanning speed;

[0041] (7.8) Calculate the scanning path length;

[0042] (7.9) Calculate the ratio of glance time to fixation time;

[0043] (7.10) Calculate the number of lookbacks;

[0044] (7.11) Calculate the playback frequency;

[0045] (7.12) Calculate the average time interval between look-backs.

[0046] Preferably, the step (9) specifically comprises the following steps:

[0047] (9.1) Calculate the correlation coefficient and conduct correlation analysis;

[0048] (9.2) Perform principal component analysis on the original related eye movement indicators;

[0049] (9.3) Construct a characteristic comprehensive eye movement index to explain reality monitoring ability;

[0050] (9.4) Use the random forest model to calculate the importance of characteristic indicators and select indicators with higher characteristic importance;

[0051] (9.5) Calculate the Shapley value and obtain the contribution of characteristic eye movement indicators in the random forest model to the prediction of real-world monitoring ability.

[0052] The device for implementing the reality monitoring picture encoding task paradigm under the eye tracker has the following main features: the device comprises:

[0053] a processor configured to execute computer-executable instructions;

[0054] A memory stores one or more computer executable instructions, and when the computer executable instructions are executed by the processor, the various steps of the method for realizing the reality monitoring picture encoding task paradigm under the above-mentioned eye tracker are implemented.

[0055] The processor for implementing the reality monitoring picture encoding task paradigm under the eye tracker has the main feature that the processor is configured to execute computer executable instructions. When the computer executable instructions are executed by the processor, the various steps of the method for implementing the reality monitoring picture encoding task paradigm under the above-mentioned eye tracker are implemented.

[0056] The main feature of the computer-readable storage medium is that a computer program is stored thereon, and the computer program can be executed by a processor to implement the various steps of the method for realizing the reality monitoring picture encoding task paradigm under the above-mentioned eye tracker.

[0057] The method, device, processor and computer-readable storage medium for implementing the reality monitoring picture coding task paradigm under the eye tracker of the present invention have the significant advantages of strong adaptability, high objectivity and multi-dimensional integration. By combining high-resolution eye movement data with behavioral indicators, this paradigm is not only suitable for schizophrenia patients with cognitive decline, but also can fully reveal the individual's cognitive strategy and decision-making process, make up for the limitations of a single data dimension, and realize the refined evaluation of individual differences, providing a scientific evaluation tool for cognitive research and clinical application, with broad application prospects and important practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a schematic diagram of an example of stimulus material involved in the method of implementing a reality monitoring picture encoding task paradigm under the eye tracker of the present invention.

[0059] Figure 2 The present invention is a flow chart of an experiment of an embodiment of a method for implementing a reality monitoring picture encoding task paradigm under an eye tracker of the present invention.

[0060] Figure 3 The present invention is a flowchart of a method for implementing a reality monitoring picture encoding task paradigm under the eye tracker of the present invention. DETAILED DESCRIPTION

[0061] In order to more clearly describe the technical content of the present invention, further description is given below in conjunction with specific embodiments.

[0062] The method, device, processor and computer-readable storage medium for implementing the reality monitoring picture encoding task paradigm under the eye tracker of the present invention include the following steps:

[0063] (1) Select 48 different pairs of related objects;

[0064] (2) Produce 48 pairs of pictures of related objects and build a material library of the real-life monitoring picture task paradigm;

[0065] (3) Design a reality monitoring picture task paradigm, and divide the experiment into two groups, each group includes an encoding phase and a test phase, and each phase presents 24 picture stimulus materials;

[0066] (4) A pre-experiment was conducted on the subjects using an eye tracker to test the reality monitoring function and collect eye tracking data;

[0067] (5) Extract behavioral and eye movement data results;

[0068] (6) Calculate behavioral indicators related to reality monitoring, including perceptual misattribution and imaginary misattribution;

[0069] (7) Calculate eye movement indicators and establish an eye movement indicator data set;

[0070] (8) Combine behavioral indicators and eye movement indicators to construct a comprehensive feature dataset;

[0071] (9) Calculate and analyze characteristic eye movement indicators that reflect real-world monitoring capabilities.

[0072] As a preferred embodiment of the present invention, the step (2) specifically comprises the following steps:

[0073] (2.1) Select 24 pairs of related objects and present them completely in the pictures, that is, each of the 24 pictures contains two related objects;

[0074] (2.2) partially presenting paired objects, i.e., the other 24 images contain only one object;

[0075] (2.3) Set location conditions;

[0076] (2.4) Set up stimulus materials for the encoding phase and present them in different combinations;

[0077] (2.5) Set up the task materials for the test phase, presenting an object under the perception condition or an object under the imagination condition in a picture.

[0078] As a preferred embodiment of the present invention, the step (3) specifically comprises the following steps:

[0079] (3.1) In the encoding phase, the four condition combination pictures were randomly presented;

[0080] (3.2) During the testing phase, each image corresponds to only one task requirement, which is used for real-world monitoring detection or spatial position judgment;

[0081] (3.3) Set the region of interest;

[0082] (3.4) Add markers to the triggers that mark the start and end of stimulus presentation during the encoding and test phases.

[0083] As a preferred embodiment of the present invention, the step (4) specifically comprises the following steps:

[0084] (4.1) During the encoding phase, the subject's associations and words are matched with the actual objects presented;

[0085] (4.2) During the test phase, determine whether the object is actually seen or imagined, or determine the location of the object.

[0086] As a preferred embodiment of the present invention, the step (6) specifically comprises the following steps:

[0087] (6.1) Calculate perceptual misattribution;

[0088] (6.2) Computational imagination misattribution.

[0089] As a preferred embodiment of the present invention, the step (7) specifically comprises the following steps:

[0090] (7.1) Calculate the total number of fixations;

[0091] (7.2) Calculate the total fixation duration;

[0092] (7.3) Calculate the discreteness of the gaze point distribution;

[0093] (7.4) Calculate gaze entropy;

[0094] (7.5) Calculate the gaze point skewness;

[0095] (7.6) Calculate the scan amplitude;

[0096] (7.7) Calculate scanning speed;

[0097] (7.8) Calculate the scanning path length;

[0098] (7.9) Calculate the ratio of glance time to fixation time;

[0099] (7.10) Calculate the number of lookbacks;

[0100] (7.11) Calculate the playback frequency;

[0101] (7.12) Calculate the average time interval between look-backs.

[0102] As a preferred embodiment of the present invention, the step (9) specifically comprises the following steps:

[0103] (9.1) Calculate the correlation coefficient and conduct correlation analysis;

[0104] (9.2) Perform principal component analysis on the original related eye movement indicators;

[0105] (9.3) Construct a characteristic comprehensive eye movement index to explain reality monitoring ability;

[0106] (9.4) Use the random forest model to calculate the importance of characteristic indicators and select indicators with higher characteristic importance;

[0107] (9.5) Calculate the Shapley value and obtain the contribution of characteristic eye movement indicators in the random forest model to the prediction of real-world monitoring ability.

[0108] The device for implementing the reality monitoring picture encoding task paradigm under the eye tracker of the present invention, wherein the device comprises:

[0109] a processor configured to execute computer-executable instructions;

[0110] A memory stores one or more computer executable instructions, and when the computer executable instructions are executed by the processor, the various steps of the method for realizing the reality monitoring picture encoding task paradigm under the above-mentioned eye tracker are implemented.

[0111] The processor of the eye tracker of the present invention implements the reality monitoring picture encoding task paradigm, wherein the processor is configured to execute computer executable instructions, and when the computer executable instructions are executed by the processor, the various steps of the method for implementing the reality monitoring picture encoding task paradigm under the above-mentioned eye tracker are implemented.

[0112] The computer-readable storage medium of the present invention stores a computer program thereon, and the computer program can be executed by a processor to implement the various steps of the method for implementing the reality monitoring picture encoding task paradigm under the above-mentioned eye tracker.

[0113] In a specific embodiment of the present invention, in view of the reality monitoring function defects of schizophrenia, the advantages of fast, simple and intuitive detection of eye movement trackers are utilized, and combined with the abnormal eye movement patterns of patients with mental illness, a reality monitoring picture encoding task paradigm under eye trackers that is suitable for a large number of schizophrenia patients is designed.

[0114] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0115] (1) Stimulus design: Extract well-known pairable objects from life scenes, construct a set of stimuli to distinguish between “seen” and “imagined”, and determine the effectiveness and difficulty of the stimuli through standardized pre-experiments.

[0116] (2) Task setting: We used ExperimentBuilder (EB), an experimental design software developed by SR Research of Canada, to design a simple and intuitive real-life monitoring picture encoding task, including paired or independent object presentation in the encoding phase and a response task in the test phase, to reduce reliance on language and cognitive abilities.

[0117] (3) Eye movement data collection: The Eyelink1000plus eye tracking device from SR Research was used to record in real time data such as the subject’s gaze position, gaze duration, scanning path, and gaze sequence while completing the task.

[0118] (4) Index extraction and analysis: Indicators that reflect attention allocation and reality monitoring strategies are extracted based on eye movement data, such as fixation time, fixation sequence, and scanning path in key areas, to quantify the subject’s performance in the task.

[0119] (5) Data integration and evaluation: Combining behavioral data and eye movement indicators, a comprehensive assessment of the subject’s reality monitoring ability is conducted and an individualized assessment report is generated.

[0120] The simulation experiment conducted by the present invention is implemented by programming on a PC test platform with a CPU of 4.0 GHz and a memory of 16 GB. The implementation of the present invention is further described in detail below in conjunction with the accompanying drawings of the specification.

[0121] (1) Objects from life scenes that are familiar to the public, including animals and plants, daily necessities, culture, education, and sports, were selected to form 48 different pairs of related objects.

[0122] (2) Create 48 pairs of images of related objects (1024×768 pixels, 300 dpi) and build a material library for the real-life surveillance image task paradigm.

[0123] (2.1) Paired objects presented completely (perceptual condition): 24 pairs of related objects were selected and presented completely in the pictures, that is, the 24 pictures contained two related objects;

[0124] (2.2) Partial presentation of paired objects (imagination condition): the other 24 pictures contained only one object;

[0125] (2.3) Position condition (left or right): Paired objects or partially presented objects are placed on the left or right side of the picture.

[0126] (2.4) Encoding stage stimulus materials: presented in different condition combinations (four combinations of "perception / imagination × left / right" conditions), namely perception-left, perception-right, imagination-left, imagination-right, with 12 pictures in each condition;

[0127] (2.5) Test phase Task materials: 48 pictures, with an object in the perception condition or an object in the imagination condition in the center of the picture. Two options are presented below the object in the picture: "perceived / imagined" or "left / right".

[0128] (3) The experimental design software Experiment Builder (EB) developed by SR Research, Canada, was used to design the reality monitoring picture task paradigm. In order to avoid eye fatigue of the subjects, the experiment was conducted in two rounds, each of which included an encoding phase and a test phase, with 24 picture stimuli presented in each phase.

[0129] (3.1) Encoding stage: The four condition combination pictures were randomly presented, and each condition was presented less than 3 times in a row. Each picture was presented for 3000ms, and the interval between pictures was 500ms;

[0130] (3.2) Test phase: Each picture corresponds to only one task requirement, which is used for reality monitoring detection or spatial position judgment. The two tasks are evenly distributed and randomly presented, and each task is presented less than 3 times in a row. The picture presentation time is determined by the subject's reaction time, and the interval between pictures is 500ms. The spatial position judgment task is mainly used for internal control. In addition, it can also examine the subject's spatial memory ability;

[0131] (3.3) Setting the area of ​​interest (AOI): During the encoding phase, a rectangular AOI (AOI) is set that contains the object and extends outward by 1°. code); During the test phase, three AOIs are set, each containing a central object (AOI test1 ) and two options (AOI test2 , AOI test3 ) and each extending outwards a rectangular AOI of 1° viewing angle;

[0132] (3.4) Marking timestamps: Adding marks to the triggers of the start and end of stimulus material presentation during encoding and testing phases;

[0133] (4) A pilot experiment was conducted using the Eyelink1000plus eye tracker from SR Research to test the reality monitoring function and collect eye tracking data on subjects (including chronic schizophrenia, first-episode schizophrenia, clinical high-risk psychosis syndrome, and normal controls).

[0134] (4.1) Encoding stage: In this stage, the subjects were asked to carefully watch the objects presented on the computer and name all the objects. In the imagination condition, the subjects were also asked to associate and name another related object to pair with the presented object.

[0135] (4.2) Test phase: When the option “perceived / imagined” is displayed below the object, the subjects are asked to judge whether another object associated with the presented object is what they saw or thought of during the encoding phase (reality monitoring task); when the option “left / right” is displayed below the object, the subjects are asked to judge whether the object was presented on the left or right side of the computer screen during the encoding phase (spatial position judgment task).

[0136] (5) The behavioral and eye movement data were extracted using SR Research's eye movement data analysis software Data Viewer (DV). The eye movement data of this test is generated based on the eye's fixation and saccadic movements.

[0137] (6) Calculate behavioral indicators related to reality monitoring, including perceptual misattribution and imaginary misattribution. The core indicator of reality monitoring ability in schizophrenia is imaginary misattribution, which reflects the patient's ability to distinguish between reality and fictional information.

[0138] (6.1) The ratio at which the subjects judged perceived information as imagined information reflects the deficiency in the ability to "accurately" detect the perceptual conditions.

[0139] (6.2) The subjects judged the ratio of imagined information to perceived information, reflecting the defect of the ability to "accurately" imagine conditions.

[0140] (7) Calculate eye movement indicators and establish an eye movement indicator data set: including the number and distribution of fixation points in each AOI, fixation time, scan amplitude, scan speed, scan path length, return gaze and pupil diameter, etc.

[0141] (7.1) The total number of fixation points (FN) is calculated as follows:

[0142]

[0143] Where N is the total number of fixation points in AOI; Fixation i represents the coordinates of the i-th fixation point (x i ,y i );I(Fixation i ∈AOI) is the indicator function, if the i-th fixation point falls within the AOI, then I = 1, otherwise I = 0.

[0144] (7.2) Total Fixation Duration (TFD) refers to the sum of the durations of all fixation points within the AOI. The calculation formula is:

[0145]

[0146] in, is the duration of the i-th fixation in the AOI (unit: ms); N is the number of fixations in the AOI.

[0147] (7.3) Fixation Dispersion (FD): It is used to quantify the dispersion of the fixation point within the fixation area and can reflect the distribution characteristics of the subject's attention within the AOI.

[0148]

[0149] Among them, (x i ,y i ) represents the coordinates of the i-th fixation point, is the mean coordinate of all fixation points in the area, and N is the number of fixation points in the AOI.

[0150] (7.4) Fixation Entropy (FE): measures the randomness and uniformity of the distribution of fixation points. A high entropy value indicates that the fixation points are evenly distributed, while a low entropy value indicates that the fixation points are concentrated in a specific area. The calculation formula is:

[0151]

[0152] Among them, p iis the probability that the i-th region of interest is being looked at, and N is the total number of regions of interest.

[0153] (7.5) Fixation Skewness (FS): reflects whether there is a certain bias in the spatial distribution of the fixation point within the AOI. The calculation formula is:

[0154]

[0155] Among them, x i is the coordinate of the i-th fixation point; is the average position of the fixation point; N is the total number of fixation points.

[0156] (7.6) Saccadic Amplitude (SA): It is the distance between two gaze points, usually expressed as a value in degrees. The calculation formula is:

[0157]

[0158] Where (x1, y1) is the coordinate of the starting point of the saccade; (x2, y2) is the coordinate of the ending point of the saccade; D is the distance from the eye to the screen (unit: cm); arctan calculates the inverse tangent function of the viewing angle and converts the distance into an angle.

[0159] The calculation formula for the average scan amplitude within the AOI is:

[0160]

[0161] Where M is the number of scans within the AOI; SA i is the amplitude of the i-th scan.

[0162] (7.7) Saccadic Velocity (SV): refers to the speed of the eye in a saccadic movement, usually in degrees per second (° / s). The calculation formula for the average saccadic velocity within the AOI is:

[0163]

[0164] Where M is the number of scans within the AOI; SA i is the amplitude of the i-th scan; t i is the time taken for the i-th scan (unit: s).

[0165] (7.8) Scan Path Length (SPL): refers to the length of the continuous path between fixation points during eye movement, usually used to measure the movement distance of fixation points within a certain period of time. This indicator reflects the intensity of individual eye movement activity, exploratory behavior or fixation stability. The calculation formula is:

[0166]

[0167] Among them, (x i ,y i ) represents the coordinates of the i-th fixation point; N is the total number of fixations within the AOI.

[0168] (7.9) Average Saccade-to-Fixation Ratio (S / F): refers to the ratio of saccade time to fixation time, which is used to describe the relationship between saccade and fixation movements and reflects the processing mode of an individual when collecting information during eye movement. The calculation formula is:

[0169]

[0170] Where M is the number of glances within the AOI; N is the total number of fixations within the AOI; is the duration of the i-th saccade within the AOI (unit: ms); is the duration of the ith fixation (unit: ms);

[0171] (7.10) Gaze return: refers to the return of the gaze point or line of sight to the AOI, which is usually related to cognitive processing, memory retrieval, attention control, etc. During the test phase, when the individual switches gaze from the central object to the options, it indicates that he or she is conducting further cognitive processing and confirmation.

[0172] The calculation formula of Revisit Count (RC) is:

[0173]

[0174] In the formula, i and j represent that after the i-th fixation point leaves the AOI, the j-th fixation point returns to the AOI again, which is a return event; 1 is an indicator function, which means that when the two fixations are in the same area of ​​interest, it returns 1, otherwise it returns 0; AOI i , AOI j They represent the areas of interest where the i-th and j-th fixation points are located respectively.

[0175] (7.11) The calculation formula of revisit frequency (RF) is:

[0176] RF=RC / FN

[0177] (7.12) Mean Revisit Interval, ) is calculated as:

[0178]

[0179] Where n is the total number of lookbacks; represents the time point of the i-th look back; Indicates the time point when the i-th leaves the AOI.

[0180] (8) Combine behavioral indicators and eye movement indicators to construct a comprehensive feature dataset.

[0181] (9) Calculate and analyze characteristic eye movement indicators that reflect real-world monitoring capabilities.

[0182] (9.1) Correlation analysis: Through the correlation between behavioral indicators and eye movement indicators, the relationship between behavior and cognitive process is explored. The correlation coefficient (CC) formula is as follows:

[0183]

[0184] Among them, X i With Y i represent the observed values ​​of behavioral and eye movement indicators respectively, and N is the number of samples.

[0185] (9.2) Principal component analysis: Principal component analysis is performed on the original related eye movement indicators. The specific formula is as follows:

[0186]

[0187] Among them, Z j represents the jth principal component; w ij Indicates that the i-th variable X in the j-th principal component i The contribution of X i is the eye movement index; n is the number of eye movement indicators.

[0188] (9.3) Multiple regression analysis: Extract the principal components with cumulative variance contribution rate of 85-90% and construct a characteristic comprehensive eye movement index to explain the real monitoring ability. The specific formula is as follows:

[0189]

[0190] Among them, Y is the core indicator of schizophrenia reality monitoring, the rate of imaginary misattribution; Z i is the principal component of the eye movement index combination; β0 is the intercept; βi is the regression coefficient, which indicates the influence of each principal component on the behavioral index; ∈ is the residual term, which indicates the error not explained by the principal component of the eye movement index.

[0191] (9.4) Machine learning method: Use the random forest model to calculate the importance of characteristic indicators and select indicators with higher characteristic importance. The specific formula is:

[0192]

[0193] Where T is the number of decision trees; Indicates the number of trees in the tth tree due to X j The amount of reduction in mean squared error (MSE) caused by the split.

[0194] (9.5) Shapley contribution explanation (SHAP analysis): Based on the theory of Shapley value, it is used to explain the contribution of characteristic eye movement indicators in the random forest model to the prediction of real monitoring ability. The specific formula is:

[0195]

[0196] Where S is a subset of variables; f(S) is the model prediction value based only on subset S; φ j is the SHAP value of the jth feature index. Compared with the existing technology, the real monitoring picture task paradigm under the eye tracker of the present invention has the following advantages:

[0197] 1) Strong adaptability: The invention selects objects in popular life scenes as stimulus materials and designs a real-life monitoring picture task paradigm that can significantly reduce the requirements for language ability and is suitable for schizophrenia patients with decreased cognitive function;

[0198] 2) High objectivity: This invention makes full use of eye tracking technology to collect high-resolution attention allocation data in real time. By introducing rich eye movement data indicators, it can not only reveal micro-attention patterns that traditional behavioral data cannot capture, but also quantify individual cognitive strategies and decision-making processes, providing a more comprehensive and objective evaluation;

[0199] 3) Multi-dimensional fusion: Combine eye movement indicators with behavioral data to explore the individual's response patterns under different task conditions, and make up for the one-sidedness and bias that may be caused by single-dimensional data. In addition, the refined analysis of eye movement data makes it possible to evaluate individual differences, providing researchers and clinicians with more targeted evaluation tools.

[0200] The technical solution of the present invention provides a real-life monitoring picture coding task paradigm specifically for schizophrenia patients with low cognitive function. The stimulus materials of this paradigm are closer to daily real-life situations and are intended to simulate situations that patients may encounter in real life, thereby improving ecological validity.

[0201] The technical solution of the present invention proposes for the first time to quantify the reality monitoring ability through eye tracking indicators. It not only focuses on group classification, but also analyzes the high-order neural and cognitive patterns of reality monitoring through specific eye movement characteristics, and deeply understands the cognitive defect mechanism of schizophrenia patients.

[0202] The technical solution of the present invention focuses on the objective quantification of reality monitoring capabilities, using more refined eye movement data analysis to reveal the potential cognitive mechanism of reality monitoring. Through eye movement indicators, not only can patient groups be identified, but also the differences between different individuals in the reality monitoring process can be analyzed, providing possibilities for personalized clinical evaluation and intervention.

[0203] The specific implementation scheme of this embodiment can refer to the relevant description in the above embodiment, which will not be repeated here.

[0204] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0205] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0206] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0207] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0208] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the corresponding program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0209] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0210] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0211] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0212] The method, device, processor and computer-readable storage medium for implementing the reality monitoring picture coding task paradigm under the eye tracker of the present invention have the significant advantages of strong adaptability, high objectivity and multi-dimensional integration. By combining high-resolution eye movement data with behavioral indicators, this paradigm is not only suitable for schizophrenia patients with cognitive decline, but also can fully reveal the individual's cognitive strategy and decision-making process, make up for the limitations of a single data dimension, and realize the refined evaluation of individual differences, providing a scientific evaluation tool for cognitive research and clinical application, with broad application prospects and important practical value.

[0213] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it is apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.

Claims

1. A method for implementing a real-life monitoring picture encoding task paradigm under an eye tracker, characterized in that: The method comprises the following steps: (1) Select 48 different pairs of related objects; (2) Produce 48 pairs of pictures of related objects and build a material library of the real-life monitoring picture task paradigm; (3) Design a reality monitoring picture task paradigm, and divide the experiment into two groups, each group includes an encoding phase and a test phase, and each phase presents 24 picture stimulus materials; (4) A pre-experiment was conducted on the subjects using an eye tracker to test the reality monitoring function and collect eye tracking data; (5) Extract behavioral and eye movement data results; (6) Calculate behavioral indicators related to reality monitoring, including perceptual misattribution and imaginary misattribution; (7) Calculate eye movement indicators and establish an eye movement indicator data set; (8) Combine behavioral indicators and eye movement indicators to construct a comprehensive feature dataset; (9) Calculate and analyze characteristic eye movement indicators that reflect real-world monitoring capabilities.

2. The method for implementing the reality monitoring picture encoding task paradigm under the eye tracker according to claim 1, characterized in that: The step (2) specifically comprises the following steps: (2.1) Select 24 pairs of related objects and present them completely in the pictures, that is, each of the 24 pictures contains two related objects; (2.2) partially presenting paired objects, i.e., the other 24 images contain only one object; (2.3) Set location conditions; (2.4) Set up stimulus materials for the encoding phase and present them in different combinations; (2.5) Set up the task materials for the test phase, presenting an object under the perception condition or an object under the imagination condition in a picture.

3. The method for implementing the reality monitoring picture encoding task paradigm under the eye tracker according to claim 1, characterized in that: The step (3) specifically comprises the following steps: (3.1) In the encoding phase, the four condition combination pictures were randomly presented; (3.2) During the testing phase, each image corresponds to only one task requirement, which is used for real-world monitoring detection or spatial position judgment; (3.3) Set the region of interest; (3.4) Add markers to the triggers that mark the start and end of stimulus presentation during the encoding and test phases.

4. The method for implementing the reality monitoring picture encoding task paradigm under the eye tracker according to claim 1, characterized in that: The step (4) specifically comprises the following steps: (4.1) During the encoding phase, the subject's associations and words are matched with the actual objects presented; (4.2) During the test phase, determine whether the object is actually seen or imagined, or determine the location of the object.

5. The method for implementing the reality monitoring picture encoding task paradigm under the eye tracker according to claim 1, characterized in that: The step (6) specifically comprises the following steps: (6.1) Calculate perceptual misattribution; (6.2) Computational imagination misattribution.

6. The method for implementing the reality monitoring picture encoding task paradigm under the eye tracker according to claim 1, characterized in that: The step (7) specifically comprises the following steps: (7.1) Calculate the total number of fixations; (7.2) Calculate the total fixation duration; (7.3) Calculate the discreteness of the gaze point distribution; (7.4) Calculate gaze entropy; (7.5) Calculate the gaze point skewness; (7.6) Calculate the scan amplitude; (7.7) Calculate scanning speed; (7.8) Calculate the scanning path length; (7.9) Calculate the ratio of glance time to fixation time; (7.10) Calculate the number of lookbacks; (7.11) Calculate the playback frequency; (7.12) Calculate the average time interval between look-backs.

7. The method for implementing the reality monitoring picture encoding task paradigm under the eye tracker according to claim 1, characterized in that: The step (9) specifically comprises the following steps: (9.1) Calculate the correlation coefficient and conduct correlation analysis; (9.2) Perform principal component analysis on the original related eye movement indicators; (9.3) Construct a characteristic comprehensive eye movement index to explain reality monitoring ability; (9.4) Use the random forest model to calculate the importance of characteristic indicators and select indicators with higher characteristic importance; (9.5) Calculate the Shapley value and obtain the contribution of characteristic eye movement indicators in the random forest model to the prediction of real-world monitoring ability.

8. A device for implementing a real-life monitoring picture encoding task paradigm under an eye tracker, characterized in that: The device comprises: a processor configured to execute computer-executable instructions; A memory storing one or more computer executable instructions, wherein when the computer executable instructions are executed by the processor, the steps of the method for implementing the reality monitoring picture encoding task paradigm under the eye tracker described in any one of claims 1 to 7 are implemented.

9. A processor for implementing a real-life monitoring picture encoding task paradigm under an eye tracker, characterized in that: The processor is configured to execute computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for implementing the reality monitoring picture encoding task paradigm under the eye tracker described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program can be executed by a processor to implement the various steps of the method for implementing a reality monitoring picture encoding task paradigm under an eye tracker as described in any one of claims 1 to 7.

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