A cognitive inhibition evaluation method based on a hierarchical bayesian shift-diffusion model

Abstract

This invention relates to the field of data processing technology in cognitive psychology, specifically to a method for assessing cognitive inhibition based on a hierarchical Bayesian drift-diffusion model. The method includes: acquiring raw trial data of subjects under a changing interference paradigm; logically comparing key selections with actual stimulus changes and generating correctness logic codes; converting raw behavioral data vectors into modeling data vectors; constructing a cognitive hypothesis-driven competitive model space based on the drift-diffusion model; iteratively estimating the posterior distribution of parameters for each competitive model using the MCMC sampling algorithm; calculating the DIC value of each competitive model; selecting the competitive model with the best fit by comparing the deviation information standard DIC values ​​of each competitive model; and outputting the assessment results of the subject's cognitive inhibition sites. This invention can identify the specific sites where cognitive inhibition occurs in the subject based on the model with the best fit to the raw behavioral data, achieving precise localization and assessment of cognitive inhibition sites.

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