心理科学进展2025，Vol.33Issue(12)：2043-2053,中插1,12.DOI:10.3724/SP.J.1042.2025.2043
基于证据积累的认知决策神经网络模型
Cognitive decision neural networks based on evidence accumulation framework
摘要
Abstract
Reaction time(RT)is a window into understanding human decision-making processes.The Evidence Accumulation Model(EAM)is the dominant theory of computational framework for modeling RT.However,EAMs,such as the Drift Diffusion Model(DDM),offer statistical descriptions of decision outcomes but without detailed algorithms for stimulus encoding or neural mechanisms,thereby omitting the algorithmic and hardware levels in David Marr's three-level framework(computation,algorithm,and hardware).We suggest that these limitations can be addressed by combining Artificial Neural Networks(ANNs)and evidence accumulation model to simulate the entire decision-making process-from stimulus encoding to evidence accumulation and decision output.These new models,termed Cognitive Decision Neural Networks,enable comprehensive modeling of human decision-making on non-biological hardware(in silico),providing a novel approach to understanding cognitive processes.Cognitive Decision Neural Networks have demonstrated preliminary potential in multi-option decision-making,temporal stimulus processing,and neural activation simulation.Such models provide a novel approach to simulating the full spectrum of human decision-making processes.In the future,integration with digital twin brain models could extend their applicability to more complex decision-making scenarios,thereby advancing a deeper understanding of human cognition.关键词
认知过程/证据积累模型/计算建模/人工神经网络Key words
cognitive process/evidence accumulation models/computational modeling/artificial neural networks分类
社会科学引用本文复制引用
陈思羽,潘晚坷,胡传鹏..基于证据积累的认知决策神经网络模型[J].心理科学进展,2025,33(12):2043-2053,中插1,12.基金项目
国家自然科学基金(项目编号:32471097)资助. （项目编号:32471097）
