A method and system for training children's attention abilities based on attention function profiling and adaptive regulation.

Abstract

This application discloses a method and system for training children's attentional abilities based on attentional function profiling and adaptive adjustment. The method includes: reading a multidimensional attentional function profile of a child to determine training targets and priorities; generating an individualized training task matrix for the child based on the training targets and priorities; presenting the training tasks in the individualized training task matrix and collecting behavioral data from the child; adaptively adjusting the task parameters of the individualized training task matrix based on the child's behavioral data and continuing the training process; performing transfer verification tests, including near transfer tests and far transfer tests, to obtain transfer verification results; updating the multidimensional attentional function profile based on the child's behavioral data and the transfer verification results, and outputting a training report. This application avoids a one-size-fits-all training approach, allowing training resources to be concentrated on the specific cognitive dimensions that most need improvement, significantly enhancing the targeting and effectiveness of the training.

Description

Technical Field

[0001] This application relates to the fields of children's cognitive training, educational rehabilitation, psychological measurement, computerized cognitive training and intelligent intervention, and in particular to a method and system for training children's attention ability based on attention function profiling and adaptive regulation. Background Technology

[0002] In applications related to children's cognitive development and learning ability cultivation, attention, as a fundamental cognitive ability supporting classroom listening, reading comprehension, and task execution, is increasingly in demand for training. Specifically, this scenario involves systematically intervening in multiple dimensions of children's attentional functions, such as sustained attention, selective attention, inhibitory control, and cognitive flexibility, in order to improve their performance in learning and daily life.

[0003] To achieve the above goals, existing technologies mainly employ gamified attention training, EEG or eye-tracking biofeedback training, VR scene training, integrated assessment and training platforms, and rehabilitation systems for specific populations such as those with ADHD.

[0004] However, the above-mentioned existing technologies have the following inherent defects in practical applications: (1) The training targets are general and often combine different attention sub-dimensions into a single attention index, making it difficult to train specific weak dimensions of children; (2) The adaptive strategies are simple and usually rely on the single accuracy rate or the difficulty of score increase or decrease, with less comprehensive consideration of response stability, false alarm characteristics, conditional costs and transfer performance; (3) The training results are not interpretable enough, and most schemes only output game scores, training duration or level changes, making it difficult to write back into a structured attention profile; (4) Some schemes rely on complex hardware such as EEG, eye movement, heart rate, skin conductance, cameras or VR devices, resulting in high deployment costs and making it unsuitable for large-scale screening and training of children; (5) Some training tasks are highly dependent on language or subject, and are easily affected by reading level, comprehension ability and school education experience. Summary of the Invention

[0005] This application provides a method and system for training children's attention abilities based on attention function profiling and adaptive regulation. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0006] In a first aspect, embodiments of this application provide a method for training children's attention ability based on attention function profiling and adaptive regulation, the method comprising: The multidimensional attention function profile of the test children was read. The multidimensional attention function profile was used to describe the attention information of the test children in different preset cognitive dimensions through standardized parameters. Based on the degree of deviation and dimensional combination of the children’s attention information relative to the pre-set age-matched norms, the training targets and training priorities are determined. Based on training targets and training priorities, an individualized training task matrix is ​​generated for the test children. This matrix includes training order, number of questions per dimension, training duration, initial difficulty, and stage goals. The experiment presents low-language-dependent or non-language training tasks from an individualized training task matrix to the children and collects behavioral data of the children in real time during the training process. Based on the behavioral data of the test children, the task parameters of the individualized training task matrix are adaptively adjusted, and the training process continues. When the training period reaches the preset training period, transfer verification, including near transfer test and far transfer test, is performed to obtain the transfer verification results; Based on the behavioral data of the children in the study and the results of transfer verification, the multidimensional attention function profile is updated and a training report is generated.

[0007] Optionally, based on the degree and combination of deviations of the test children from the pre-set age-appropriate norms indicated by attention information, training targets and priorities are determined, including: From attention information, identify the degree of deviation and dimensional combination patterns of the subjects' deviation from the pre-set age-appropriate norms; Based on the degree of deviation, each cognitive dimension is marked as a weakness level including strength, normal, mild weakness, moderate weakness, and severe weakness; Cognitive dimensions marked as severely weak were identified as training targets; Based on the combination pattern of weakness level and dimension, determine the training priority.

[0008] Optionally, training priorities can be determined based on the combination of weakness level and dimension, including: Based on the dimension combination pattern, at least one auxiliary training dimension is determined for the cognitive dimension corresponding to the training target. The auxiliary training dimension is determined according to the preset joint training rules. Based on the correlation between the training target and at least one auxiliary training dimension, and their respective weakness levels, the training order and question volume ratio are determined as training priorities.

[0009] Optional, preset joint training rules include: When the sustained attention dimension is marked as weak and the false positive rate exceeds a preset threshold, the sustained attention dimension and the inhibition control dimension are determined as auxiliary training dimensions; or... When sustained attention is marked as weak and its reaction time coefficient of variation exceeds a preset threshold, sustained attention and alertness attention are designated as auxiliary training dimensions; or... When both the visual search dimension and the visual crowding dimension are marked as weak, the dimension corresponding to the joint training of spatial resolution and search efficiency is determined as the auxiliary training dimension; or... When both the attention shifting dimension and the cognitive flexibility dimension are marked as weak, the dimension corresponding to the joint training of cue redirection and rule switching is determined as the auxiliary training dimension; or... When both the spatial location memory dimension and the multi-target attention allocation dimension are marked as weak, the dimension corresponding to the joint training of spatial preservation and target tracking is determined as the auxiliary training dimension; or... When both the auditory anti-interference dimension and the inhibition control dimension are marked as weak, the dimension corresponding to the joint training of cross-channel anti-interference and conflict suppression is determined as the auxiliary training dimension.

[0010] Optionally, the children are presented with low-language-dependent or non-language training tasks from the individualized training task matrix, including: From low-language-dependent or non-language training tasks in the individualized training task matrix, obtain visual training tasks, spatial training tasks, auditory training tasks, or executive control training tasks; Present the children with at least one of the following training tasks: visual training tasks, spatial training tasks, auditory training tasks, or executive control training tasks; Among them, visual training tasks include continuous target monitoring tasks, visual search tasks, perceptual organization tasks, visual crowding / spatial discrimination tasks, or spatial location memory tasks. Spatial training tasks include attention shifting tasks, attention orientation tasks, vigilance attention tasks, or multi-target attention allocation tasks; Auditory training tasks include auditory interference resistance tasks; Training tasks involving control include attentional inhibition control tasks or cognitive flexibility tasks.

[0011] Optionally, based on the behavioral data of the test children, the task parameters of the individualized training task matrix are adaptively adjusted, including: During a single training session, the behavioral data of the test children are monitored in real time. The behavioral data of the test children includes at least the accuracy rate, the false negative rate, the reaction time, the coefficient of variation of the reaction time, the number of consecutive correct answers, the number of consecutive incorrect answers, and the number of interruptions. When the behavioral data of the test children indicate that the test children perform better than the preset upper threshold in a number of consecutive trials or a section, the difficulty level of the current training task is increased; the criteria for judging performance better than the upper threshold include accuracy higher than the first preset value, false alarm rate lower than the second preset value, and reaction time coefficient of variation reduced. When the behavioral data of the test children indicate that the test children's performance is worse than the preset lower threshold, the difficulty level of the current training task is reduced or the intensity of the prompts is increased; among them, the judgment conditions for performance worse than the lower threshold include the accuracy rate being lower than the third preset value, the number of consecutive errors increasing, or the number of interruptions increasing; Based on the adjusted difficulty level or cue intensity, the task parameters in the individualized training task matrix are dynamically updated. The task parameters include at least one of the following: stimulus presentation duration, stimulus interval, number of targets, number of interferences, interference similarity, movement speed, cue effectiveness, rule complexity, response window, reward threshold, number of questions, and cue intensity.

[0012] Optionally, the method also includes: Based on multiple training records within a preset training period, the training gain index, stability improvement index, and transfer gain index for each training dimension are calculated. Based on the training gain index, stability improvement index, and transfer gain index, update the training prescription for the next training cycle; wherein, updating the training prescription includes adjusting at least one of the following for the next cycle: training dimension weights, training order, number of questions per dimension, starting difficulty level, joint training combination, and transfer validation frequency. If there is a training dimension where training performance improves but reaction time coefficient of variation does not improve, then the difficulty progression of that dimension should be reduced and the proportion of stability training should be increased; if there is a training dimension that produces significant transfer gain to adjacent dimensions, then the frequency of repeated training for that dimension should be reduced.

[0013] Optionally, perform transfer verification including near transfer tests and far transfer tests to obtain transfer verification results, including: After the preset training period ends, the children are presented with near transfer test tasks and far transfer test tasks; Among them, the near transfer test task is used to evaluate the performance improvement in tasks of the same or adjacent type as the training task, including near transfer tasks in the same dimension or transfer tasks in adjacent dimensions; the far transfer test task is used to evaluate the potential impact on dimensions that were not directly trained or tasks related to real learning. Collect test behavior data of the children in the near transfer test task and the far transfer test task; Based on the test behavior data, determine the transfer validation results, which include at least one of the following: same-dimensional near transfer gain, adjacent-dimensional transfer gain, and far-dimensional transfer gain.

[0014] Optionally, based on the behavioral data of the children in the study and the results of transfer validation, the multidimensional attention function profile is updated and a training report is output, including: Based on the behavioral data of the children in the study, we quantified the changes in standard scores, percentile changes, changes in response stability, and changes in error patterns of the children in each cognitive dimension. Based on changes in standard score, percentile, response stability, error mode, and transfer validation results, an updated multidimensional attention function profile is generated. Based on the updated multidimensional attention function profile, a training report is generated and output.

[0015] Secondly, embodiments of this application provide a children's attention ability training system based on attention function profiling and adaptive regulation, the system comprising: The multidimensional attention function profile reading module is used to read the multidimensional attention function profile of the test children. The multidimensional attention function profile is used to describe the attention information of the test children in different preset cognitive dimensions through standardized parameters. The training target and training priority determination module is used to determine the training target and training priority based on the degree of deviation and dimensional combination of the test children's deviation from the preset age norms indicated by attention information. The individualized training task matrix generation module is used to generate an individualized training task matrix for the test children based on training targets and training priorities. The matrix includes training order, number of questions per dimension, training duration, initial difficulty and stage goals. The training module is used to present low-language-dependent or non-language training tasks from an individualized training task matrix to the test children and to collect behavioral data of the test children in real time during the training process. The adaptive adjustment module is used to adaptively adjust the task parameters of the individualized training task matrix based on the behavioral data of the test children, and continue to execute the training process; The transfer verification module is used to perform transfer verification, including near transfer test and far transfer test, when the training cycle reaches the preset training cycle, and obtain the transfer verification results. The training report output module is used to update the multidimensional attention function profile and output a training report based on the behavioral data of the test children and the transfer verification results.

[0016] The technical solutions provided in this application embodiment may include the following beneficial effects: In this embodiment, on the one hand, by introducing a multidimensional attention function profile as input, and determining specific training targets and priorities based on the degree of deviation of each dimension from the age-appropriate norm and the combination pattern of dimensions, a shift from general attention training to multidimensional refined intervention is achieved. This method can accurately identify the specific weaknesses of children in each sub-function of attention, avoiding a one-size-fits-all training model, and allowing training resources to be concentrated on the specific cognitive dimensions that need the most improvement, significantly enhancing the pertinence and effectiveness of training. On the other hand, by collecting behavioral data in real time during a single training session to dynamically adjust task parameters, and by updating the training prescription for the next cycle after the training cycle ends by combining trend indicators such as training gains, stability improvement, and transfer gains, this two-level adaptive mechanism surpasses the traditional simple upgrade and downgrade model that relies solely on a single score. It keeps the training difficulty at the zone of proximal development most suitable for the child's current ability, ensuring the scientific nature and stability of the training process.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] Figure 1 This is a flowchart illustrating a method for training children's attention ability based on attention function profiling and adaptive regulation, as provided in an embodiment of this application. Figure 2 This is a schematic diagram illustrating the presentation process of a continuous target monitoring task provided in this application; Figure 3 This is a schematic diagram illustrating the presentation process of a visual search task provided in this application; Figure 4 This is a schematic diagram of the presentation process of a perceptual organization task provided in this application; Figure 5 This is a schematic diagram of the presentation process of a visual crowding / spatial resolution task provided in this application; Figure 6 This is a schematic diagram illustrating the presentation process of a spatial location memory task provided in this application; Figure 7 This is a schematic diagram illustrating the presentation process of an attention transfer task provided in this application; Figure 8 This is a schematic diagram illustrating the presentation process of a multi-target attention allocation task provided in this application; Figure 9 This is a schematic diagram of the presentation process of an auditory interference resistance task provided in this application; Figure 10 This is a schematic diagram of the presentation process of an attention inhibition control task provided in this application; Figure 11 This is a schematic diagram illustrating the presentation process of a cognitive flexibility task provided in this application; Figure 12 This is a schematic diagram of a dual-ring adaptive training method provided in this application; Figure 13 This is a schematic diagram of a children's attention ability training system based on attention function profiling and adaptive regulation, as provided in this application. Figure 14 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] The following description and accompanying drawings fully illustrate specific embodiments of this application to enable those skilled in the art to practice them.

[0021] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0022] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with some aspects of this application as detailed in the appended claims.

[0023] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0024] This application provides a method and system for training children's attention ability based on attention function profiling and adaptive regulation, to solve the problems existing in the aforementioned related technologies. In the embodiments of this application, on the one hand, by introducing a multi-dimensional attention function profiling as input, and determining specific training targets and priorities based on the degree of deviation of each dimension from the age-appropriate norm and the combination pattern of dimensions, a shift from general attention training to multi-dimensional refined intervention is achieved. This method can accurately identify the specific weaknesses of children in each sub-function of attention, avoiding a one-size-fits-all training model, allowing training resources to be concentrated on the specific cognitive dimensions that most need improvement, significantly improving the pertinence and effectiveness of training. On the other hand, by collecting behavioral data in real time during a single training session to dynamically adjust task parameters, and by updating the training prescription for the next cycle after the training cycle ends by combining trend indicators such as training gains, stability improvement, and transfer gains, this two-level adaptive mechanism surpasses the traditional simple upgrade and downgrade model that relies solely on a single score. It keeps the training difficulty always within the zone of proximal development most suitable for the child's current ability, ensuring the scientific nature and stability of the training process. Exemplary embodiments are described in detail below.

[0025] The following will be combined with the appendix Figure 1 -Appendix Figure 12 This application provides a detailed description of a method for training children's attention abilities based on attention function profiling and adaptive regulation, as provided in its embodiments. This method can be implemented using a computer program and can run on a children's attention ability training system based on the von Neumann architecture and employing attention function profiling and adaptive regulation. This computer program can be integrated into applications or run as a standalone utility application.

[0026] Please see Figure 1 This document provides a flowchart illustrating a method for training children's attention abilities based on attention function profiling and adaptive regulation, as described in an embodiment of this application. Figure 1 As shown, the method in this application embodiment may include the following steps: S101, Read the multidimensional attention function profile of the test child. The multidimensional attention function profile is used to describe the attention information of the test child in different preset cognitive dimensions through standardized parameters. The multidimensional attentional profiling is a structured data model that comprehensively characterizes an individual's attentional features through multiple independent cognitive dimensions (such as sustained attention, inhibitory control, visual search, and auditory immunity). The different pre-defined cognitive dimensions are pre-defined sub-functional categories that constitute the attentional function system, such as sustained attention, attention shifting, and working memory. Attention information refers to various quantitative indicators reflecting the strength of a subject's attentional abilities, including but not limited to accuracy, reaction time, false alarm rate, and reaction time coefficient of variation.

[0027] It should be noted that the preset different cognitive dimensions include at least two of the following: sustained attention, visual search, perceptual organization, spatial memory, visual crowding, attention shift, attention orientation, vigilant attention, multi-target attention allocation, auditory interference resistance, attentional inhibition control, and cognitive flexibility.

[0028] In one possible implementation, a multidimensional profile of the child's attentional function is read, which includes data on each dimension obtained through standardized tests and presented in the form of standard scores and percentiles: sustained attention dimension (standard score 80, below the 15th percentile of the age-matched norm), visual search dimension (standard score 95, at the 40th percentile of the age-matched norm), inhibitory control dimension (standard score 70, below the 5th percentile of the age-matched norm), and auditory immunity dimension (standard score 105, at the 60th percentile of the age-matched norm).

[0029] S102, based on the degree of deviation and dimensional combination of the test children relative to the preset age-matched norms indicated by attention information, determine the training targets and training priorities; In some embodiments of this application, the specific process of determining training targets and training priorities based on the degree of deviation and dimensional combination pattern of the tested child relative to the preset age-matched norm, as indicated by attention information, includes: identifying the degree of deviation and dimensional combination pattern of the tested child relative to the preset age-matched norm from the attention information; marking each cognitive dimension as a weakness level including strength, normal, mild weakness, moderate weakness, and severe weakness according to the degree of deviation; determining the cognitive dimensions marked as severely weak as training targets; and determining the training priority according to the weakness level and dimensional combination pattern.

[0030] Specifically, the process of determining training priorities based on weakness levels and dimension combination patterns includes: determining at least one auxiliary training dimension for the cognitive dimension corresponding to the training target based on the dimension combination pattern; the auxiliary training dimension is determined according to preset joint training rules; and determining the training order and question ratio of each based on the correlation between the training target and at least one auxiliary training dimension and their respective weakness levels, which serves as the training priority.

[0031] Specifically, the preset joint training rules include: when the sustained attention dimension is marked as weak and the false alarm rate is higher than a preset threshold, the sustained attention dimension and the inhibitory control dimension are determined as auxiliary training dimensions; or, when the sustained attention dimension is marked as weak and the reaction time coefficient of variation is higher than a preset threshold, the sustained attention dimension and the alertness attention dimension are determined as auxiliary training dimensions; or, when both the visual search dimension and the visual crowding dimension are marked as weak, the dimension corresponding to the joint training of spatial resolution and search efficiency is determined as an auxiliary training dimension; or, when both the attention shift dimension and the cognitive flexibility dimension are marked as weak, the dimension corresponding to the joint training of cue redirection and rule switching is determined as an auxiliary training dimension; or, when both the spatial location memory dimension and the multi-target attention allocation dimension are marked as weak, the dimension corresponding to the joint training of spatial retention and target tracking is determined as an auxiliary training dimension; or, when both the auditory anti-interference dimension and the inhibitory control dimension are marked as weak, the dimension corresponding to the joint training of cross-channel anti-interference and conflict suppression is determined as an auxiliary training dimension.

[0032] In one possible implementation, if a dimension is below the 15th percentile of the age-matched norm, it is marked as slightly weak; below the 10th percentile, it is marked as moderately weak; and below the 5th percentile, it is marked as severely weak. The system prioritizes training severely weak dimensions and configures joint training tasks for their associated auxiliary dimensions.

[0033] S103 generates an individualized training task matrix for the test children based on training targets and training priorities. This matrix includes training order, number of questions per dimension, training duration, initial difficulty, and stage goals. The individualized training task matrix is ​​a data structure or logical set specifically tailored for a particular child, containing a detailed training implementation plan. It is not a single task, but a comprehensive training plan encompassing multiple dimensions and parameters. The training sequence refers to the order in which different attention dimensions (or different training tasks) are executed within the training cycle. The number of questions per dimension is the number of training questions or trials allocated to each specific attention dimension (e.g., visual search, auditory interference resistance). The training duration is the expected time consumed in a single training session or each training phase. The initial difficulty level is the initial difficulty level set for a specific dimension task at the start of training (e.g., stimulus presentation time, number of distractions, etc.). The phase goal is the expected behavioral indicators (e.g., accuracy exceeding 85%, or reaction time coefficient of variation decreasing to a certain threshold) that the child is expected to achieve in a specific dimension within the training cycle.

[0034] In one possible implementation, for a child identified as having deficiencies in sustained attention (severely weak, primary target) and inhibitory control (secondary target), the system generates the following individualized training task matrix: the training sequence is to first conduct sustained attention training, interspersed with inhibitory control training; the task ratio for each dimension is 70% for sustained attention tasks and 30% for inhibitory control tasks; the total duration of a single training session is 20 minutes; the initial difficulty of the sustained attention task is set at a target occurrence probability of 30% and moderate fluctuation in stimulus intervals; the stage goal is set to reduce the child's missed detection rate in the sustained attention task to below 10% and reduce the reaction time coefficient of variation by 15% compared to the pretest.

[0035] S104 presents low-language-dependent or non-language training tasks from the individualized training task matrix to the test children and collects the test children's behavioral data in real time during the training process. In some embodiments of this application, the specific process of presenting low-language-dependent or non-language training tasks from an individualized training task matrix to the test children includes: obtaining visual training tasks, spatial training tasks, auditory training tasks, or executive control training tasks from the low-language-dependent or non-language training tasks in the individualized training task matrix; presenting at least one of the visual training tasks, spatial training tasks, auditory training tasks, or executive control training tasks to the test children; wherein, visual training tasks include continuous target monitoring tasks, visual search tasks, perceptual organization tasks, visual crowding / spatial discrimination tasks, or spatial location memory tasks; spatial training tasks include attention shifting tasks, attention orientation tasks, vigilance attention tasks, or multi-target attention allocation tasks; auditory training tasks include auditory anti-interference tasks; and executive control training tasks include attentional inhibition control tasks or cognitive flexibility tasks.

[0036] For example Figure 2 As shown, the continuous target monitoring task requires children to maintain target attention and response readiness for extended periods. During training, children must respond when the target stimulus appears and inhibit responses to non-target stimuli. The system allows adjustment of task duration, target appearance probability, stimulus interval fluctuation, similarity of non-target stimuli, mid- and late-stage load, and feedback frequency. The system optimizes the monitoring accuracy, false negative rate, false positive rate, correct reaction time, standard deviation of correct reaction time, and performance degradation indicators at the beginning and end of the training.

[0037] For example Figure 3 As shown, in the visual search task, children's visual scanning, target detection, and selective attention abilities are trained by locating target images in an array of similar stimuli. The system allows adjustment of the total number of items, the proportion of targets present, the similarity between targets and distractors, changes in target orientation, presentation time, and the density of distractors.

[0038] For example Figure 4As shown, in the perceptual organization task, through the implicit structure recognition task composed of directional units or local elements, children's ability to integrate the overall structure from local cues is trained. The system can adjust the perturbation angle of local elements, the completeness of the target structure, the proportion of noise elements, the response time window, and the number of target graphic categories.

[0039] For example Figure 5 As shown, in the visual crowding / spatial discrimination task, a central target and adjacent distracting stimuli are presented in close proximity, requiring children to determine the direction, identity, or category of the central target, thus training their spatial discrimination ability in crowded environments. The system allows adjustment of the distance between the target and distracting stimuli, the number of distracting stimuli, the similarity of the stimuli, the presentation duration, and the feedback method.

[0040] For example Figure 6 As shown, in the spatial location memory task, children's spatial retention, location updating, and target localization abilities are trained by memorizing shapes, colors, or symbols at different spatial locations and recognizing them after a short period of time. The system allows adjustment of the number of targets, retention interval, location similarity, number of interference insertions, and probability of location change.

[0041] For example Figure 7 As shown, in the attention shifting task, the cue-target task trains children's ability to quickly shift their attention between different locations, objects, or rules. The system can modify cue effectiveness, cue-target time interval, cue-target position relationship, target probability, and rule switching frequency.

[0042] For example, in attention orientation tasks, spatial cues can be used to indicate the possible location of a target, guiding children to quickly orient their attentional resources based on external clues. The system can adjust the cue effectiveness rate, cue format, cue lead time, target appearance probability, and invalid cue ratio.

[0043] For example, in alertness-based attention tasks, children are trained to maintain a state of readiness and basic alertness through target waiting at irregular time intervals and rapid reaction tasks. The system can adjust the fluctuation of waiting time, the strength of cue signals, the frequency of target appearance, the cue-target interval, and the missed detection feedback rules.

[0044] For example Figure 8 As shown, in the multi-target attention allocation task, by simultaneously tracking multiple moving targets or synchronously monitoring changes in multiple stimuli, children are trained to allocate and maintain attentional resources among multiple targets or locations. The system allows adjustment of the number of targets, total number of objects, movement speed, occlusion frequency, collision rules, and tracking duration.

[0045] For example Figure 9As shown, in the auditory interference resistance task, children's cross-channel selective attention and auditory interference resistance are trained by identifying target sounds, pitches, rhythms, or syllables amidst background noise, competing speech, non-target tones, or ambient sounds. The system allows adjustment of the signal-to-noise ratio, similarity between the target and interference, sound flow velocity, binaural presentation mode, and target occurrence probability.

[0046] For example Figure 10 As shown, in the attention inhibition control task, through goal judgment tasks under consistent, conflicting, and neutral conditions, the system trains children's ability to inhibit automatic responses and irrelevant interference. The system allows adjustment of the conflict ratio, interference intensity, response window, error feedback method, and probability of consecutive conflict occurrences.

[0047] For example Figure 11 As shown, in the cognitive flexibility task, by switching judgment rules, target attributes, or task sets in different trials, children's ability to quickly adjust processing strategies and switch rules is trained. The system can adjust the rule switching probability, repetition block length, switching prompt format, conflict superposition degree, and feedback intensity after switching.

[0048] S105, Based on the behavioral data of the test children, adaptively adjust the task parameters of the individualized training task matrix and continue the training process; In some embodiments of this application, the specific process of adaptively adjusting the task parameters of the individualized training task matrix based on the behavioral data of the test children includes: during a single training session, real-time monitoring of the test children's behavioral data, which includes at least accuracy, false negative rate, false positive rate, reaction time, reaction time coefficient of variation, number of consecutive correct answers, number of consecutive incorrect answers, and number of interruptions; when the test children's behavioral data indicates that the test children's performance is better than a preset upper limit threshold in a number of consecutive trials or a section, the difficulty level of the current training task is increased; wherein, the criteria for determining performance better than the upper limit threshold include an accuracy higher than a first preset value and a false positive rate lower than a second preset value. The value and reaction time coefficient of variation decrease; when the behavioral data of the test children indicate that the performance of the test children is worse than the preset lower threshold, the difficulty level of the current training task is reduced or the cue intensity is increased; the judgment conditions for performance worse than the lower threshold include the accuracy rate being lower than the third preset value, the number of consecutive errors increasing or the number of interruptions increasing; according to the adjusted difficulty level or cue intensity, the task parameters in the individualized training task matrix are dynamically updated, and the task parameters include at least one of the following: stimulus presentation duration, stimulus interval, number of targets, number of interferences, interference similarity, movement speed, cue effectiveness, rule complexity, response window, reward threshold, number of questions, and cue intensity.

[0049] In one possible implementation, adaptive adjustment of the task parameters of the individualized training task matrix can be understood as in-training real-time adaptation. In-training real-time adaptation refers to the system adjusting task parameters based on the child's real-time performance during a single training session, keeping the child within a training range that is achievable yet challenging. The system monitors in real-time metrics including accuracy, false negative rate, false positive rate, correct reaction time, standard deviation of correct reaction time, coefficient of variation of reaction time, number of consecutive correct answers, number of consecutive incorrect answers, number of interruptions, and task completion rate.

[0050] For example, when a child's performance exceeds a preset upper limit in several consecutive trials or a training session—for example, an accuracy rate higher than 85%, a false alarm rate lower than a preset threshold, and a decrease in the coefficient of variation of reaction time—the system increases the task difficulty. When performance falls below a preset lower limit—for example, an accuracy rate lower than 65%, an increase in consecutive errors, or an increase in the number of interruptions—the system decreases the task difficulty or provides transitional prompts. Adjustable parameters include stimulus presentation duration, stimulus interval, number of targets, number of distractors, distractor similarity, movement speed, cue effectiveness, rule complexity, response window, reward threshold, number of questions, and cue intensity.

[0051] Furthermore, this application also provides an inter-training plan update, which specifically includes: calculating the training benefit index, stability improvement index, and transfer gain index for each training dimension based on multiple training records within a preset training period; updating the training prescription for the next training period according to the training benefit index, stability improvement index, and transfer gain index; wherein, updating the training prescription includes adjusting at least one of the following for the next period: training dimension weight, training order, number of questions per dimension, initial difficulty level, joint training combination, and transfer verification frequency; wherein, if there is a training dimension where training performance improves but reaction time coefficient of variation does not improve, the difficulty progression of that dimension is reduced and the proportion of stability training is increased; if there is a training dimension that produces significant transfer gain to adjacent dimensions, the frequency of repeated training for that dimension is reduced.

[0052] The inter-training plan update refers to the system updating the prescription for the next training cycle based on phased trends after several training sessions. This mechanism considers not only average performance but also stability indicators, error patterns, and transfer metrics.

[0053] In one implementation, the system calculates the training gain index, stability improvement index, and transfer gain index for each dimension based on the most recent N training records. If the training performance in a certain dimension improves but the reaction time coefficient of variation does not improve, the system reduces the difficulty progression and increases the proportion of stability training. If training in a certain dimension produces a significant transfer gain for adjacent dimensions, the system can reduce repetitive training and improve training efficiency. Adjustable aspects of the inter-training plan update include: the weight of the training dimension in the next cycle, the training order, the number of questions per dimension, the initial difficulty level, the joint training combination, and the transfer validation frequency.

[0054] S106, when the training period reaches the preset training period, perform transfer verification including near transfer test and far transfer test to obtain transfer verification results; In some embodiments of this application, the specific process of performing transfer verification, including near transfer tests and far transfer tests, to obtain transfer verification results includes: after a preset training period, presenting near transfer test tasks and far transfer test tasks to the test children; wherein, the near transfer test task is used to evaluate the performance improvement in tasks of the same or adjacent type as the training task, including near transfer tasks of the same dimension or transfer tasks of adjacent dimensions; the far transfer test task is used to evaluate the potential impact on non-directly trained dimensions or tasks related to actual learning; collecting test behavior data of the test children in the near transfer test tasks and far transfer test tasks; and determining the transfer verification results based on the test behavior data, wherein the transfer verification results include at least one of near transfer gain of the same dimension, transfer gain of adjacent dimensions, and far transfer gain.

[0055] It's important to note that this method incorporates training effectiveness evaluation and profile updates into a closed-loop process. It not only focuses on the scores of the training task itself but also integrates near and far transfer test results, comprehensively analyzing changes in standard scores, response stability, and error patterns before and after training. These results are then used to create an updated multidimensional attentional function profile. The resulting training report includes dimensional improvements, transfer effects, and recommendations for the next stage, transforming the originally abstract gamified results into structured cognitive assessment data with clinical and educational significance. This significantly enhances the interpretability and practical value of the training results.

[0056] S107 updates the multidimensional attention function profile and outputs a training report based on the behavioral data of the test children and the transfer verification results.

[0057] In some embodiments of this application, the specific process of updating the multidimensional attention function profile and outputting a training report based on the behavioral data of the test children and the transfer verification results includes: quantifying the changes in standard scores, percentiles, response stability, and error patterns of the test children in each cognitive dimension based on the behavioral data of the test children; generating an updated multidimensional attention function profile based on the changes in standard scores, percentiles, response stability, error patterns, and transfer verification results; and generating and outputting a training report based on the updated multidimensional attention function profile.

[0058] The system can regenerate a post-training attention profile based on changes in standard scores, percentiles, reaction stability, error patterns, and transfer validation results before and after training. The post-training profile includes at least the magnitude of dimensional progress, weak areas that still require priority training, improved dimensions where training frequency can be reduced, dimensional combinations with significant transfer gains, and training suggestions for the next stage.

[0059] For example Figure 12 As shown, Figure 12 This application provides a schematic diagram of a dual-loop adaptive training system. The system identifies the weak dimensions of the tested children based on a multidimensional attention function profile, and selects training dimensions and individualized training combinations accordingly. Then, it enters the training task scheduling stage to determine the training cycle and units. Next, the process is divided into an inner loop and an outer loop. In a single training session, the inner loop presents tasks, collects behavioral data in real time, and makes immediate adaptive adjustments to maintain an appropriate training difficulty. The outer loop summarizes and updates the training prescription for the next cycle (such as dimension weights, question ratios, difficulty levels, etc.) based on the phase results after multiple training sessions. Finally, after a preset training cycle, transfer verification is performed. The multidimensional attention function profile is updated by combining the verification results with the training data, and a training report containing near / far transfer assessment and updated profile is output, forming a complete closed-loop path of assessment-training-verification-retraining.

[0060] In this embodiment, the method relies entirely on behavioral data such as accuracy and reaction time generated by the children during screen interaction to achieve personalized training and evaluation throughout the entire process, without relying on expensive biosignal acquisition equipment or complex VR hardware. This design significantly reduces the deployment threshold and usage cost of the system, enabling its widespread application in non-professional medical environments such as schools, communities, and homes, which is conducive to the large-scale promotion and popularization of children's attention training.

[0061] In this application embodiment, it is explicitly specified that the tasks presented to the children are low-language-dependent or non-language training tasks, covering multiple modalities such as visual, spatial, auditory, and executive control. This design effectively shields the interference of language ability, reading level, and academic background on training performance, ensuring that the measurement results truly reflect the children's attention function itself, rather than being limited by their language comprehension ability, thereby guaranteeing the fairness and universality of the training assessment.

[0062] In this embodiment, on the one hand, by introducing a multidimensional attention function profile as input, and determining specific training targets and priorities based on the degree of deviation of each dimension from the age-appropriate norm and the combination pattern of dimensions, a shift from general attention training to multidimensional refined intervention is achieved. This method can accurately identify the specific weaknesses of children in each sub-function of attention, avoiding a one-size-fits-all training model, and allowing training resources to be concentrated on the specific cognitive dimensions that need the most improvement, significantly enhancing the pertinence and effectiveness of training. On the other hand, by collecting behavioral data in real time during a single training session to dynamically adjust task parameters, and by updating the training prescription for the next cycle after the training cycle ends by combining trend indicators such as training gains, stability improvement, and transfer gains, this two-level adaptive mechanism surpasses the traditional simple upgrade and downgrade model that relies solely on a single score. It keeps the training difficulty at the zone of proximal development most suitable for the child's current ability, ensuring the scientific nature and stability of the training process.

[0063] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.

[0064] Please see Figure 13 This illustration shows a schematic diagram of a children's attention training system based on attention function profiling and adaptive regulation, provided in an exemplary embodiment of this application. This children's attention training system based on attention function profiling and adaptive regulation can be implemented as all or part of an electronic device through software, hardware, or a combination of both. System 1 includes a multi-dimensional attention function profiling module 10, a training target and training priority determination module 20, an individualized training task matrix generation module 30, a training module 40, an adaptive regulation module 50, a transfer verification module 60, and a training report output module 70.

[0065] The multidimensional attention function profile reading module 10 is used to read the multidimensional attention function profile of the test child. The multidimensional attention function profile is used to describe the attention information of the test child in different preset cognitive dimensions through standardized parameters. The training target and training priority determination module 20 is used to determine the training target and training priority based on the degree of deviation and dimensional combination of the test child's deviation from the preset age norm indicated by attention information. The individualized training task matrix generation module 30 is used to generate an individualized training task matrix for the test children based on training targets and training priorities. The matrix includes training order, number of questions per dimension, training duration, initial difficulty and stage goals. Training module 40 is used to present low-language-dependent or non-language training tasks in the individualized training task matrix to the test children and collect the test children's behavioral data in real time during the training process. The adaptive adjustment module 50 is used to adaptively adjust the task parameters of the individualized training task matrix based on the behavioral data of the test children, and continue to execute the training process. The transfer verification module 60 is used to perform transfer verification, including near transfer test and far transfer test, when the training period reaches the preset training period, and obtain the transfer verification result. The training report output module 70 is used to update the multidimensional attention function profile and output a training report based on the behavioral data of the test children and the transfer verification results.

[0066] It should be noted that the above-described children's attention ability training system based on attention function profiling and adaptive regulation, when executing the children's attention ability training method based on attention function profiling and adaptive regulation, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the electronic device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the children's attention ability training system based on attention function profiling and adaptive regulation and the children's attention ability training method embodiment provided above belong to the same concept, and their implementation process is detailed in the method embodiment, which will not be repeated here.

[0067] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0068] In this embodiment, on the one hand, by introducing a multidimensional attention function profile as input, and determining specific training targets and priorities based on the degree of deviation of each dimension from the age-appropriate norm and the combination pattern of dimensions, a shift from general attention training to multidimensional refined intervention is achieved. This method can accurately identify the specific weaknesses of children in each sub-function of attention, avoiding a one-size-fits-all training model, and allowing training resources to be concentrated on the specific cognitive dimensions that need the most improvement, significantly enhancing the pertinence and effectiveness of training. On the other hand, by collecting behavioral data in real time during a single training session to dynamically adjust task parameters, and by updating the training prescription for the next cycle after the training cycle ends by combining trend indicators such as training gains, stability improvement, and transfer gains, this two-level adaptive mechanism surpasses the traditional simple upgrade and downgrade model that relies solely on a single score. It keeps the training difficulty at the zone of proximal development most suitable for the child's current ability, ensuring the scientific nature and stability of the training process.

[0069] This application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the child attention training method based on attention function profiling and adaptive regulation provided in the above-described method embodiments.

[0070] This application also provides a computer program product containing instructions that, when run on a computer, causes the computer to execute the child attention training method based on attention function profiling and adaptive regulation according to the above-described method embodiments.

[0071] Please see Figure 14 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 14 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0072] The communication bus 1002 is used to realize the connection and communication between these components.

[0073] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0074] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0075] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts within the electronic device 1000 using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 1001 may integrate one or more of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip, without being integrated into the processor 1001.

[0076] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage system located remotely from the aforementioned processor 1001. Figure 14 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a children's attention training application based on attention function profiling and adaptive regulation.

[0077] exist Figure 14In the illustrated electronic device 1000, the user interface 1003 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 1001 can be used to call the child attention ability training application based on attention function profile and adaptive regulation stored in the memory 1005, and specifically perform the following operations: The multidimensional attention function profile of the test children was read. The multidimensional attention function profile was used to describe the attention information of the test children in different preset cognitive dimensions through standardized parameters. Based on the degree of deviation and dimensional combination of the children’s attention information relative to the pre-set age-matched norms, the training targets and training priorities are determined. Based on training targets and training priorities, an individualized training task matrix is ​​generated for the test children. This matrix includes training order, number of questions per dimension, training duration, initial difficulty, and stage goals. The experiment presents low-language-dependent or non-language training tasks from an individualized training task matrix to the children and collects behavioral data of the children in real time during the training process. Based on the behavioral data of the test children, the task parameters of the individualized training task matrix are adaptively adjusted, and the training process continues. When the training period reaches the preset training period, transfer verification, including near transfer test and far transfer test, is performed to obtain the transfer verification results; Based on the behavioral data of the children in the study and the results of transfer verification, the multidimensional attention function profile is updated and a training report is generated.

[0078] In one embodiment, when the processor 1001 executes the combination of deviation degree and dimension of the test child relative to the preset age norm based on attention information to determine the training target and training priority, it specifically performs the following operations: From attention information, identify the degree of deviation and dimensional combination patterns of the subjects' deviation from the pre-set age-appropriate norms; Based on the degree of deviation, each cognitive dimension is marked as a weakness level including strength, normal, mild weakness, moderate weakness, and severe weakness; Cognitive dimensions marked as severely weak were identified as training targets; Based on the combination pattern of weakness level and dimension, determine the training priority.

[0079] In one embodiment, when the processor 1001 determines the training priority based on the combination pattern of weakness level and dimension, it specifically performs the following operations: Based on the dimension combination pattern, at least one auxiliary training dimension is determined for the cognitive dimension corresponding to the training target. The auxiliary training dimension is determined according to the preset joint training rules. Based on the correlation between the training target and at least one auxiliary training dimension, and their respective weakness levels, the training order and question volume ratio are determined as training priorities.

[0080] In one embodiment, when the processor 1001 performs a low-language-dependent or non-language training task from the individualized training task matrix to the test child, it specifically performs the following operations: From low-language-dependent or non-language training tasks in the individualized training task matrix, obtain visual training tasks, spatial training tasks, auditory training tasks, or executive control training tasks; Present the children with at least one of the following training tasks: visual training tasks, spatial training tasks, auditory training tasks, or executive control training tasks; Among them, visual training tasks include continuous target monitoring tasks, visual search tasks, perceptual organization tasks, visual crowding / spatial discrimination tasks, or spatial location memory tasks. Spatial training tasks include attention shifting tasks, attention orientation tasks, vigilance attention tasks, or multi-target attention allocation tasks; Auditory training tasks include auditory interference resistance tasks; Training tasks involving control include attentional inhibition control tasks or cognitive flexibility tasks.

[0081] In one embodiment, when the processor 1001 performs the task parameters of adaptively adjusting the individualized training task matrix based on the behavioral data of the test children, it specifically performs the following operations: During a single training session, the behavioral data of the test children are monitored in real time. The behavioral data of the test children includes at least the accuracy rate, the false negative rate, the reaction time, the coefficient of variation of the reaction time, the number of consecutive correct answers, the number of consecutive incorrect answers, and the number of interruptions. When the behavioral data of the test children indicate that the test children perform better than the preset upper threshold in a number of consecutive trials or a section, the difficulty level of the current training task is increased; the criteria for judging performance better than the upper threshold include accuracy higher than the first preset value, false alarm rate lower than the second preset value, and reaction time coefficient of variation reduced. When the behavioral data of the test children indicate that the test children's performance is worse than the preset lower threshold, the difficulty level of the current training task is reduced or the intensity of the prompts is increased; among them, the judgment conditions for performance worse than the lower threshold include the accuracy rate being lower than the third preset value, the number of consecutive errors increasing, or the number of interruptions increasing; Based on the adjusted difficulty level or cue intensity, the task parameters in the individualized training task matrix are dynamically updated. The task parameters include at least one of the following: stimulus presentation duration, stimulus interval, number of targets, number of interferences, interference similarity, movement speed, cue effectiveness, rule complexity, response window, reward threshold, number of questions, and cue intensity.

[0082] In one embodiment, the processor 1001 also performs the following operations: Based on multiple training records within a preset training period, the training gain index, stability improvement index, and transfer gain index for each training dimension are calculated. Based on the training gain index, stability improvement index, and transfer gain index, update the training prescription for the next training cycle; wherein, updating the training prescription includes adjusting at least one of the following for the next cycle: training dimension weights, training order, number of questions per dimension, starting difficulty level, joint training combination, and transfer validation frequency. If there is a training dimension where training performance improves but reaction time coefficient of variation does not improve, then the difficulty progression of that dimension should be reduced and the proportion of stability training should be increased; if there is a training dimension that produces significant transfer gain to adjacent dimensions, then the frequency of repeated training for that dimension should be reduced.

[0083] In one embodiment, when the processor 1001 processes and executes migration verification including near migration tests and far migration tests, and obtains the migration verification result, it specifically performs the following operations: After the preset training period ends, the children are presented with near transfer test tasks and far transfer test tasks; Among them, the near transfer test task is used to evaluate the performance improvement in tasks of the same or adjacent type as the training task, including near transfer tasks in the same dimension or transfer tasks in adjacent dimensions; the far transfer test task is used to evaluate the potential impact on dimensions that were not directly trained or tasks related to real learning. Collect test behavior data of the children in the near transfer test task and the far transfer test task; Based on the test behavior data, determine the transfer validation results, which include at least one of the following: same-dimensional near transfer gain, adjacent-dimensional transfer gain, and far-dimensional transfer gain.

[0084] In one embodiment, when the processor 1001 updates the multidimensional attention function profile and outputs a training report based on the behavioral data of the test children and the transfer validation results, it specifically performs the following operations: Based on the behavioral data of the children in the study, we quantified the changes in standard scores, percentile changes, changes in response stability, and changes in error patterns of the children in each cognitive dimension. Based on changes in standard score, percentile, response stability, error mode, and transfer validation results, an updated multidimensional attention function profile is generated. Based on the updated multidimensional attention function profile, a training report is generated and output.

[0085] In this embodiment, on the one hand, by introducing a multidimensional attention function profile as input, and determining specific training targets and priorities based on the degree of deviation of each dimension from the age-appropriate norm and the combination pattern of dimensions, a shift from general attention training to multidimensional refined intervention is achieved. This method can accurately identify the specific weaknesses of children in each sub-function of attention, avoiding a one-size-fits-all training model, and allowing training resources to be concentrated on the specific cognitive dimensions that need the most improvement, significantly enhancing the pertinence and effectiveness of training. On the other hand, by collecting behavioral data in real time during a single training session to dynamically adjust task parameters, and by updating the training prescription for the next cycle after the training cycle ends by combining trend indicators such as training gains, stability improvement, and transfer gains, this two-level adaptive mechanism surpasses the traditional simple upgrade and downgrade model that relies solely on a single score. It keeps the training difficulty at the zone of proximal development most suitable for the child's current ability, ensuring the scientific nature and stability of the training process.

[0086] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program for training children's attention ability based on attention function profiling and adaptive regulation can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium for the program for training children's attention ability based on attention function profiling and adaptive regulation can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.

[0087] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A method for training children's attention ability based on attention function profiling and adaptive regulation, characterized in that, The method includes: Read the multidimensional attention function profile of the test children, which is used to describe the attention information of the test children in different preset cognitive dimensions through standardized parameters; Based on the degree of deviation and dimensional combination pattern of the subject child relative to the preset age norm indicated by the attention information, the training target and training priority are determined; Based on the training targets and training priorities, an individualized training task matrix is ​​generated for the test children. The matrix includes the training order, the number of questions per dimension, the training duration, the initial difficulty, and the stage goals. The test children are presented with low-language-dependent or non-language training tasks from the individualized training task matrix, and behavioral data of the test children are collected in real time during the training process. Based on the behavioral data of the tested children, the task parameters of the individualized training task matrix are adaptively adjusted, and the training process continues. When the training period reaches the preset training period, transfer verification including near transfer test and far transfer test is performed to obtain the transfer verification results; Based on the behavioral data of the tested children and the transfer verification results, the multidimensional attention function profile is updated and a training report is output.

2. The method according to claim 1, characterized in that, The step of determining training targets and training priorities based on the degree and dimensional combination of deviation of the test child from the preset age-appropriate norm indicated by the attention information includes: From the attention information, identify the degree of deviation and dimensional combination pattern of the test child relative to the preset age norm; Based on the degree of deviation, each cognitive dimension is marked as a weakness level including strength, normal, mild weakness, moderate weakness, and severe weakness; Cognitive dimensions marked as severely weak were identified as training targets; Based on the combination pattern of the weakness level and dimension, the training priority is determined.

3. The method according to claim 2, characterized in that, The step of determining training priority based on the combination pattern of the weakness level and dimension includes: Based on the dimension combination pattern, at least one auxiliary training dimension is determined for the cognitive dimension corresponding to the training target, and the auxiliary training dimension is determined according to a preset joint training rule; Based on the correlation between the training target and the at least one auxiliary training dimension, and their respective weakness levels, the training order and question ratio are determined as training priorities.

4. The method according to claim 3, characterized in that, The preset joint training rules include: When the sustained attention dimension is marked as weak and the false positive rate exceeds a preset threshold, the sustained attention dimension and the inhibition control dimension are determined as auxiliary training dimensions; or... When sustained attention is marked as weak and its reaction time coefficient of variation exceeds a preset threshold, sustained attention and alertness attention are designated as auxiliary training dimensions; or... When both the visual search dimension and the visual crowding dimension are marked as weak, the dimension corresponding to the joint training of spatial resolution and search efficiency is determined as the auxiliary training dimension; or... When both the attention shifting dimension and the cognitive flexibility dimension are marked as weak, the dimension corresponding to the joint training of cue redirection and rule switching is determined as the auxiliary training dimension; or... When both the spatial location memory dimension and the multi-target attention allocation dimension are marked as weak, the dimension corresponding to the joint training of spatial preservation and target tracking is determined as the auxiliary training dimension; or... When both the auditory anti-interference dimension and the inhibition control dimension are marked as weak, the dimension corresponding to the joint training of cross-channel anti-interference and conflict suppression is determined as the auxiliary training dimension.

5. The method according to claim 1, characterized in that, Presenting the individualized training task matrix to the test children with low language dependence or non-language training tasks includes: Visual training tasks, spatial training tasks, auditory training tasks, or executive control training tasks are obtained from the low-language-dependent or non-language training tasks in the individualized training task matrix. Present the children with at least one of the visual training tasks, spatial training tasks, auditory training tasks, or executive control training tasks; The visual training tasks include continuous target monitoring tasks, visual search tasks, perceptual organization tasks, visual crowding / spatial discrimination tasks, or spatial location memory tasks. The spatial training tasks include attention shifting tasks, attention orientation tasks, vigilant attention tasks, or multi-target attention allocation tasks; The auditory training tasks include auditory anti-interference tasks; The training tasks for executive control include attentional inhibition control tasks or cognitive flexibility tasks.

6. The method according to claim 1, characterized in that, The step of adaptively adjusting the task parameters of the individualized training task matrix based on the behavioral data of the test children includes: During a single training session, the behavioral data of the test children are monitored in real time. The behavioral data of the test children includes at least the accuracy rate, false negative rate, reaction time, reaction time coefficient of variation, number of consecutive correct answers, number of consecutive incorrect answers, and number of interruptions. When the behavioral data of the test child indicates that the test child's performance is better than a preset upper threshold in a series of consecutive trials or a section, the difficulty level of the current training task is increased; wherein, the judgment conditions for the performance being better than the upper threshold include an accuracy rate higher than a first preset value, a false alarm rate lower than a second preset value, and a decrease in the reaction time coefficient of variation. When the behavioral data of the test children indicates that the performance of the test children is worse than a preset lower threshold, the difficulty level of the current training task is reduced or the intensity of the prompts is increased; wherein, the judgment conditions for performance being worse than the lower threshold include the accuracy rate being lower than a third preset value, the number of consecutive errors increasing, or the number of interruptions increasing; Based on the adjusted difficulty level or cue intensity, the task parameters in the individualized training task matrix are dynamically updated. The task parameters include at least one of the following: stimulus presentation duration, stimulus interval, number of targets, number of interferences, interference similarity, movement speed, cue effectiveness, rule complexity, response window, reward threshold, number of questions, and cue intensity.

7. The method according to claim 1, characterized in that, The method further includes: Based on multiple training records within a preset training period, the training gain index, stability improvement index, and transfer gain index for each training dimension are calculated. The training prescription for the next training cycle is updated based on the training gain index, stability improvement index, and transfer gain index; wherein, the updated training prescription includes adjusting at least one of the following for the next cycle: training dimension weights, training order, number of questions per dimension, starting difficulty level, joint training combination, and transfer validation frequency. If there is a training dimension where training performance improves but reaction time coefficient of variation does not improve, then the difficulty progression of that dimension should be reduced and the proportion of stability training should be increased; if there is a training dimension that produces significant transfer gain to adjacent dimensions, then the frequency of repeated training for that dimension should be reduced.

8. The method according to claim 1, characterized in that, The execution includes transfer verification with near transfer tests and far transfer tests, yielding transfer verification results, including: After the preset training period ends, the children in the test are presented with near transfer test tasks and far transfer test tasks; The near transfer test task is used to evaluate the performance improvement in tasks of the same or adjacent type as the training task, including near transfer tasks in the same dimension or transfer tasks in adjacent dimensions; the far transfer test task is used to evaluate the potential impact on dimensions not directly trained or tasks related to real learning. Collect test behavior data of the children in the near transfer test task and the far transfer test task; Based on the test behavior data, the transfer validation result is determined, and the transfer validation result includes at least one of the following: same-dimensional near transfer gain, adjacent-dimensional transfer gain, and far transfer gain.

9. The method according to claim 1, characterized in that, The step of updating the multidimensional attention function profile and outputting a training report based on the behavioral data of the tested children and the transfer verification results includes: Based on the behavioral data of the test children, the changes in standard scores, percentile changes, changes in response stability, and changes in error patterns of the test children in each cognitive dimension were quantified. Based on the changes in standard score, percentile, response stability, error mode, and transfer verification results, an updated multidimensional attention function profile is generated. Based on the updated multidimensional attention function profile, a training report is generated and output.

10. A children's attention ability training system based on attention function profiling and adaptive regulation, characterized in that, The system includes: A multidimensional attention function profile reading module is used to read the multidimensional attention function profile of the test child. The multidimensional attention function profile is used to describe the attention information of the test child in different preset cognitive dimensions through standardized parameters. The training target and training priority determination module is used to determine the training target and training priority based on the degree of deviation and dimensional combination pattern of the test child relative to the preset age norm indicated by the attention information. The individualized training task matrix generation module is used to generate an individualized training task matrix for the test child based on the training target and training priority. The matrix includes training order, number of questions per dimension, training duration, initial difficulty and stage goal. The training module is used to present low-language-dependent or non-language training tasks from the individualized training task matrix to the test children and to collect the test children's behavioral data in real time during the training process. An adaptive adjustment module is used to adaptively adjust the task parameters of the individualized training task matrix based on the behavioral data of the test children, and continue to execute the training process; The transfer verification module is used to perform transfer verification, including near transfer test and far transfer test, when the training cycle reaches the preset training cycle, and obtain the transfer verification results. The training report output module is used to update the multidimensional attention function profile and output a training report based on the behavioral data of the test children and the transfer verification results.

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