Explicit knowledge of task structure is a primary determinant of human model-based action

任务(项目管理) 动作(物理) 认知心理学 控制(管理) 心理学 强化学习 计算机科学 简单(哲学) 钢筋 人工智能 社会心理学 物理 管理 量子力学 经济 哲学 认识论
作者
Pedro Castro-Rodrigues,Thomas Akam,Ivar Snorasson,Marta Camacho,Vítor Paixão,Ana Maia,J. Bernardo Barahona‐Corrêa,Peter Dayan,Blair Simpson,Rui M. Costa,Albino J. Oliveira‐Maia
出处
期刊:Nature Human Behaviour [Nature Portfolio]
卷期号:6 (8): 1126-1141 被引量:40
标识
DOI:10.1038/s41562-022-01346-2
摘要

Explicit information obtained through instruction profoundly shapes human choice behaviour. However, this has been studied in computationally simple tasks, and it is unknown how model-based and model-free systems, respectively generating goal-directed and habitual actions, are affected by the absence or presence of instructions. We assessed behaviour in a variant of a computationally more complex decision-making task, before and after providing information about task structure, both in healthy volunteers and in individuals suffering from obsessive-compulsive or other disorders. Initial behaviour was model-free, with rewards directly reinforcing preceding actions. Model-based control, employing predictions of states resulting from each action, emerged with experience in a minority of participants, and less in those with obsessive-compulsive disorder. Providing task structure information strongly increased model-based control, similarly across all groups. Thus, in humans, explicit task structural knowledge is a primary determinant of model-based reinforcement learning and is most readily acquired from instruction rather than experience. Healthy volunteers and patients with obsessive-compulsive disorder learning a task from experience alone tend to repeat actions that lead to rewards. They are poor at learning predictive models, but their use of these models is strongly increased when explicit information is provided.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
20001554发布了新的文献求助150
1秒前
维生素完成签到 ,获得积分10
3秒前
3秒前
霍悉尼完成签到 ,获得积分10
4秒前
4秒前
6秒前
7秒前
靠得住的小仙女完成签到,获得积分10
7秒前
白金黑猴完成签到,获得积分10
8秒前
8秒前
向前发布了新的文献求助10
10秒前
灵巧书蝶发布了新的文献求助10
11秒前
Lucky发布了新的文献求助10
13秒前
喝点酷比发布了新的文献求助10
13秒前
李文岐完成签到,获得积分10
13秒前
充电宝应助wuqi采纳,获得10
14秒前
科研通AI6.4应助小四喜采纳,获得10
15秒前
15秒前
猪脑过载完成签到 ,获得积分10
16秒前
17秒前
FashionBoy应助科研通管家采纳,获得10
17秒前
搜集达人应助科研通管家采纳,获得10
17秒前
赘婿应助科研通管家采纳,获得30
17秒前
17秒前
17秒前
17秒前
Lucas应助科研通管家采纳,获得10
17秒前
17秒前
17秒前
17秒前
17秒前
18秒前
18秒前
18秒前
猪大大发布了新的文献求助10
19秒前
科研通AI6.3应助20001554采纳,获得30
20秒前
白金黑猴关注了科研通微信公众号
20秒前
开放念云完成签到,获得积分10
20秒前
21秒前
Cc完成签到,获得积分10
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Research Handbook on the Law of the Paris Agreement 1000
Various Faces of Animal Metaphor in English and Polish 800
Signals, Systems, and Signal Processing 610
Superabsorbent Polymers: Synthesis, Properties and Applications 500
Photodetectors: From Ultraviolet to Infrared 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6352281
求助须知:如何正确求助?哪些是违规求助? 8166966
关于积分的说明 17188456
捐赠科研通 5408546
什么是DOI,文献DOI怎么找? 2863291
邀请新用户注册赠送积分活动 1840711
关于科研通互助平台的介绍 1689682