已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Humans perseverate on punishment avoidance goals in multigoal reinforcement learning

惩罚(心理学) 心理学 担心 钢筋 认知心理学 任务(项目管理) 强化学习 社会心理学 适应(眼睛) 回避学习 发展心理学 神经科学 焦虑 计算机科学 人工智能 管理 经济 精神科
作者
Paul M. Sharp,Evan M. Russek,Quentin J. M. Huys,Raymond J. Dolan,Eran Eldar
出处
期刊:eLife [eLife Sciences Publications Ltd]
卷期号:11 被引量:1
标识
DOI:10.7554/elife.74402
摘要

Managing multiple goals is essential to adaptation, yet we are only beginning to understand computations by which we navigate the resource demands entailed in so doing. Here, we sought to elucidate how humans balance reward seeking and punishment avoidance goals, and relate this to variation in its expression within anxious individuals. To do so, we developed a novel multigoal pursuit task that includes trial-specific instructed goals to either pursue reward (without risk of punishment) or avoid punishment (without the opportunity for reward). We constructed a computational model of multigoal pursuit to quantify the degree to which participants could disengage from the pursuit goals when instructed to, as well as devote less model-based resources toward goals that were less abundant. In general, participants (n = 192) were less flexible in avoiding punishment than in pursuing reward. Thus, when instructed to pursue reward, participants often persisted in avoiding features that had previously been associated with punishment, even though at decision time these features were unambiguously benign. In a similar vein, participants showed no significant downregulation of avoidance when punishment avoidance goals were less abundant in the task. Importantly, we show preliminary evidence that individuals with chronic worry may have difficulty disengaging from punishment avoidance when instructed to seek reward. Taken together, the findings demonstrate that people avoid punishment less flexibly than they pursue reward. Future studies should test in larger samples whether a difficulty to disengage from punishment avoidance contributes to chronic worry.

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
激昂的千雁完成签到,获得积分10
刚刚
3秒前
youth应助冷静采纳,获得10
3秒前
snowy发布了新的文献求助30
6秒前
6秒前
369ninja发布了新的文献求助10
8秒前
乔凌云发布了新的文献求助10
8秒前
9秒前
沉静的迎荷完成签到 ,获得积分10
10秒前
11秒前
syalonyui完成签到,获得积分10
11秒前
乔凌云发布了新的文献求助10
12秒前
sakura完成签到,获得积分10
14秒前
糖丸完成签到,获得积分10
14秒前
顾先森发布了新的文献求助10
15秒前
yuchuncheng完成签到,获得积分10
17秒前
江氏巨颏虎完成签到,获得积分10
17秒前
未来星完成签到,获得积分10
18秒前
YHW发布了新的文献求助20
19秒前
科研通AI6.2应助WakinLEO采纳,获得10
19秒前
20秒前
21秒前
21秒前
_Dearlxy发布了新的文献求助10
22秒前
snowy完成签到,获得积分10
24秒前
计划逃跑发布了新的文献求助10
25秒前
李五百关注了科研通微信公众号
27秒前
123发布了新的文献求助10
28秒前
29秒前
执着的导师应助sakura采纳,获得10
30秒前
31秒前
33秒前
34秒前
lmm完成签到 ,获得积分10
35秒前
卷卷发布了新的文献求助10
35秒前
2224270676发布了新的文献求助10
37秒前
WakinLEO发布了新的文献求助10
38秒前
wanci应助欢呼的白玉采纳,获得10
39秒前
39秒前
jiejie59867发布了新的文献求助10
44秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(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
48V Low-voltage Power Distribution Network (PDN) Architecture Industry Report, 2024 800
ズームレンズの光学設計に関する研究 800
Fundamentals of Pharmaceutical and Biologics Regulations: A Global Perspective, Second Edition 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
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 7297191
求助须知:如何正确求助?哪些是违规求助? 8915665
关于积分的说明 18878769
捐赠科研通 6962972
什么是DOI,文献DOI怎么找? 3210516
关于科研通互助平台的介绍 2379824
邀请新用户注册赠送积分活动 2186984