Modeling method of cognitive decision-oriented multi-dimensional hierarchical drift diffusion model

Abstract

The invention discloses a modeling method of a cognitive decision-oriented multi-dimensional hierarchical drift diffusion model. The method comprises the following steps that: cognitive decision-making tasks which need to be implemented and paradigms are selected and determined, and behavior response data of a subject on each cognitive decision-making task is acquired; an offset diffusion model is constructed for behavior response data of the subject in a single test; the selection probability of the subject and the reaction time of expected response are analyzed and calculated; an estimated value of parameters of the offset diffusion model is calculated and outputted, wherein the parameters of the offset diffusion model including a decision threshold, a relative starting point, a drift rate and a non-decision time of the subject; and behavior reaction data of the subject in the plurality of decision paradigms in different cognitive domains are integrated into the same model framework, and a low-to-high multi-dimensional hierarchical drift diffusion model is formed. The multi-dimensional hierarchical drift diffusion model can be used as a model for cognitive decision evaluation of MCI patients, and is used for assisting in understanding the cognitive impairment condition of the MCI patients.

Description

technical field [0001] The invention relates to a cognitive decision-oriented multi-dimensional layered drift diffusion model modeling method, which belongs to the technical field of cognitive psychology. Background technique [0002] Individuals experience some degree of cognitive decline during the aging process. Mild Cognitive Impairment (MCI) is a prodromal state of Alzheimer's disease, an intermediate state between normal aging and dementia, and can be used as a "predictor" of Alzheimer's disease. In older adults, MCI can lead to poor performance on decision-making tasks that require higher cognitive abilities such as sensory perception, attentional resources, and memory. [0003] With the development of computer modeling, the Drift Diffusion Model (DDM for short) has gradually emerged and has been rapidly applied to psychology, especially to cognitive decision-making tasks. DDM simulates and refines the neural dynamic process of the human brain in decision-making tas...

AI Technical Summary

Problems solved by technology

[0005] HDDM adopts a hierarchical design based on the Bayesian framework, which can comprehensively consider the characteristics of decision-making components at different levels. However, the current application scenarios of HDDM are limited to modeling single cognitive decision-making tasks and paradigms at the individual and group levels. The decision-making process of the test subjects has not been extended to the context of clustering multiple cognitive decision-making tasks, and the sensitivity thresholds of different cognitive domains (divided into three categories of perception, advanced cognition and social cognition) cannot be integrated and evaluated and overall metacognitive processing efficiency

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