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
刚刚
1秒前
1秒前
yiyeshanren完成签到,获得积分10
1秒前
愉快的戎发布了新的文献求助10
1秒前
joker发布了新的文献求助30
1秒前
1秒前
chyu1057发布了新的文献求助30
1秒前
石榴发布了新的文献求助10
2秒前
烟花应助科研通管家采纳,获得10
2秒前
隐形的凡阳完成签到,获得积分10
2秒前
wsy完成签到,获得积分10
2秒前
在水一方应助科研通管家采纳,获得10
2秒前
C_发布了新的文献求助10
2秒前
科研通AI2S应助科研通管家采纳,获得10
2秒前
2秒前
完美世界应助科研通管家采纳,获得10
2秒前
2秒前
哑巴完成签到,获得积分20
3秒前
Owen应助科研通管家采纳,获得10
3秒前
852应助科研通管家采纳,获得10
3秒前
好好学习完成签到,获得积分10
3秒前
SolitarySnow发布了新的文献求助10
3秒前
所所应助科研通管家采纳,获得10
3秒前
李爱国应助科研通管家采纳,获得10
3秒前
jlhnt完成签到,获得积分10
3秒前
orixero应助科研通管家采纳,获得10
3秒前
ybyb完成签到,获得积分10
3秒前
ding应助科研通管家采纳,获得10
3秒前
4秒前
CodeCraft应助科研通管家采纳,获得10
4秒前
脑洞疼应助科研通管家采纳,获得10
4秒前
Yy应助科研通管家采纳,获得30
4秒前
Cyrus完成签到,获得积分10
4秒前
JamesPei应助科研通管家采纳,获得10
4秒前
汉堡包应助不停采纳,获得10
4秒前
桐桐应助科研通管家采纳,获得10
4秒前
所所应助科研通管家采纳,获得10
4秒前
王大发布了新的文献求助20
5秒前
5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
Principles of town planning: translating concepts to applications 1000
2016 Venous Blood Study (VBS) (Final V3.0) 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The Effective Clinical Neurologist 3ed 500
The Great Hymn to Šamaš 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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7702648
求助须知:如何正确求助?哪些是违规求助? 9261083
关于积分的说明 20030434
捐赠科研通 7278251
什么是DOI,文献DOI怎么找? 3294279
关于科研通互助平台的介绍 2449697
邀请新用户注册赠送积分活动 2300929