任务切换
认知灵活性
认知
认知心理学
计算机科学
学习迁移
任务(项目管理)
概化理论
培训转移
强化学习
卡片分类
心理学
人工智能
机器学习
发展心理学
神经科学
工程类
系统工程
作者
Tanya Wen,Raphael Geddert,Seth Madlon‐Kay,Tobias Egner
出处
期刊:
[Cold Spring Harbor Laboratory]
日期:2021-07-23
被引量:3
标识
DOI:10.1101/2021.07.21.453253
摘要
Abstract Adaptive behavior requires learning about the structure of one’s environment to derive optimal action policies, and previous studies have documented transfer of such structural knowledge to bias choices in new environments. Here, we asked whether people could also acquire and transfer more abstract knowledge across different task environments, specifically expectations about cognitive control demands. Over three experiments, participants performed a probabilistic card-sorting task in environments of either a low or high volatility of task rule changes (requiring low or high cognitive flexibility respectively) before transitioning to a medium-volatility environment. Using reinforcement learning modeling, we consistently found that previous exposure to high task rule volatilities led to faster adaptation to rule changes in the subsequent transfer phase. These transfers of expectations about cognitive flexibility demands were both task- (Experiment 2) and stimulus- (Experiment 3) independent, thus demonstrating the formation and generalization of environmental structure knowledge to guide cognitive control. Statement of Relevance We investigated whether structural knowledge of one task environment can be transferred to guide cognitive control strategies in new environments. Past research has found that while learning generally improves subsequent performance, it does so only for the learned task (“near transfer”) and has little or no generalizability to novel task rules and stimuli (“far transfer”). However, recent studies suggest that learning more abstract, structural task features (e.g., cognitive maps) allows for that knowledge to be applied to new environments. Here, we took a critical additional step and showed that people can acquire and transfer expectations about cognitive control demands (specifically cognitive flexibility) across different task environments. To our knowledge, this is the first demonstration of people’s ability to extract and re-use cognitive control learning parameters that transcend specific stimuli and tasks. This transfer of learned cognitive flexibility is particularly noteworthy because such flexibility is impaired in several common psychiatric conditions.
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