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Flexible structure learning under uncertainty

文献类型：期刊论文

作者Wang, Rui1,2; Gates, Vael3; Shen, Yuan4; Tino, Peter5; Kourtzi, Zoe6
刊名FRONTIERS IN NEUROSCIENCE
出版日期2023-08-03
卷号17页码:14
关键词structure learning uncertainty perceptual decisions decision strategy vision
DOI10.3389/fnins.2023.1195388
通讯作者Kourtzi, Zoe(zk240@cam.ac.uk)
英文摘要Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments. Here, we ask whether uncertainty in dynamic environments affects our ability to learn predictive structures. We exposed participants to sequences of symbols determined by first-order Markov models and asked them to indicate which symbol they expected to follow each sequence. We introduced uncertainty in this prediction task by manipulating the: (a) probability of symbol co-occurrence, (b) stimulus presentation rate. Further, we manipulated feedback, as it is known to play a key role in resolving uncertainty. Our results demonstrate that increasing the similarity in the probabilities of symbol co-occurrence impaired performance on the prediction task. In contrast, increasing uncertainty in stimulus presentation rate by introducing temporal jitter resulted in participants adopting a strategy closer to probability maximization than matching and improving in the prediction tasks. Next, we show that feedback plays a key role in learning predictive statistics. Trial-by-trial feedback yielded stronger improvement than block feedback or no feedback; that is, participants adopted a strategy closer to probability maximization and showed stronger improvement when trained with trial-by-trial feedback. Further, correlating individual strategy with learning performance showed better performance in structure learning for observers who adopted a strategy closer to maximization. Our results indicate that executive cognitive functions (i.e., selective attention) may account for this individual variability in strategy and structure learning ability. Taken together, our results provide evidence for flexible structure learning; individuals adapt their decision strategy closer to probability maximization, reducing uncertainty in temporal sequences and improving their ability to learn predictive statistics in variable environments.
收录类别SCI
WOS关键词OF-VIEW TEST ; NEURONAL OSCILLATIONS ; TEMPORAL STRUCTURE ; WORKING-MEMORY ; ATTENTION ; STRATEGY ; FEEDBACK ; BRAIN ; DYNAMICS ; ACCOUNT
WOS研究方向Neurosciences & Neurology
语种英语
WOS记录号WOS:001048932800001
出版者FRONTIERS MEDIA SA
源URL[http://ir.psych.ac.cn/handle/311026/45896]  
专题心理研究所_脑与认知科学国家重点实验室
通讯作者Kourtzi, Zoe
作者单位1.Chinese Acad Sci, Inst Psychol, CAS Ctr Excellence Brain Sci & Intelligence Techno, State Key Lab Brain & Cognit Sci, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
3.Stanford Univ, Inst Human Ctr AI, Stanford, CA USA
4.Nottingham Trent Univ, Sch Sci & Technol, Nottingham, England
5.Univ Birmingham, Sch Comp Sci, Birmingham, England
6.Univ Cambridge, Dept Psychol, Cambridge, England
推荐引用方式
GB/T 7714		 Wang, Rui,Gates, Vael,Shen, Yuan,et al. Flexible structure learning under uncertainty[J]. FRONTIERS IN NEUROSCIENCE,2023,17:14.
APA Wang, Rui,Gates, Vael,Shen, Yuan,Tino, Peter,&Kourtzi, Zoe.(2023).Flexible structure learning under uncertainty.FRONTIERS IN NEUROSCIENCE,17,14.
MLA Wang, Rui,et al."Flexible structure learning under uncertainty".FRONTIERS IN NEUROSCIENCE 17(2023):14.

入库方式： OAI收割

来源：心理研究所

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