Closing The Loop: A Dynamic Neural Network Model Integrating Decision making and Metacognition

分享嘉宾： 卢炜文
单位： 北京师范大学 认知神经科学与学习国家重点实验室

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报告简介

Decision making under uncertainty entails selecting optimal actions from noisy evidence. Currently influential models, such as drift-diffusion and attractor frameworks, posit decisions as bottom-up stochastic evidence accumulation from sensory inputs but ignore critical interactions between decision, commitment, and metacognition. While these models explain basic choice behaviors and accompanying confidence, they fail to reconcile many empirical findings, including motor-area encoding of decision variables and time-dependent urgency signals.

We present a closed-loop neural network model unifying three modules: decision module accumulating evidence, a motor module implementing action thresholds, and a metacognition module regulating deliberation through dual feedback pathways to suppress noise-driven errors and accelerate decision commitment under time constraints, respectively. This architecture can account for crucial characteristics of decision-making and metacognition that were empirically observed. By integrating decision, motor, and metacognitive dynamics, our model provides a biologically grounded framework for optimal decision making, offering testable predictions for neural and behavioral studies.

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报告时间

北京时间 [GMT+8] 2025年4月8日 (周二) 20:00~21:00

会议信息： ZOOM 会议号：827 9274 9402

报告语言： 中文

主持人： 胡啸

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参考文献

Lu, W., & Wan, X. (2025). Closing the loop: A dynamic neural network model integrating decision making and metacognition. bioRxiv: The Preprint Server for Biology. https://doi.org/10.1101/2025.03.06.641797

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