Lingering neural representations of past task features adversely affect future behavior

背景(考古学) 任务(项目管理) 情感(语言学) 计算机科学 刺激(心理学) 脑电图 动作(物理) 事件(粒子物理) 认知心理学 集合(抽象数据类型) 事件相关电位 心理学 人工智能 沟通 神经科学 古生物学 物理 管理 量子力学 经济 生物 程序设计语言
作者
Benjamin O. Rangel,Eliot Hazeltine,Jan R. Wessel
标识
DOI:10.1101/2022.03.08.483546
摘要

Abstract During goal-directed behavior, humans purportedly form and retrieve so called ‘event files’ – conjunctive representations that link context-specific information about stimuli, their associated actions, and the expected action-outcomes. The automatic formation – and later retrieval – of such conjunctive ‘event file’ representations can substantially facilitate efficient action selection. However, recent behavioral work suggests that these event-files may also adversely affect future behavior, especially when action requirements have changed between successive instances of the same task context (e.g., during task-switching). Here, we directly tested this hypothesis through a recently developed method that allows measuring the strength of the neural representations of context-specific stimulus-action conjunctions (i.e., event files). Thirty-five male and female adult humans performed a task-switching paradigm while undergoing EEG recordings. Replicating previous behavioral work, we found that changes in action requirements between two spaced repetitions of the same task incurred a significant reaction time cost. By combining multi-variate pattern analysis and representational similarity analysis of the EEG recordings with linear mixed-effects modeling of trial-to-trial behavior, we then found that the magnitude of this behavioral cost was directly proportional to the strength of the conjunctive representation formed during the most recent previous exposure to the same task – i.e., the most recent ‘event file’. This confirms that the formation of conjunctive representations of specific task contexts, stimuli, and actions in the brain can indeed adversely affect future behavior. Moreover, these findings demonstrate the potential of neural decoding of complex task set representations towards the prediction of behavior beyond the current trial.

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