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Learning predictive statistics from temporal sequences: Dynamics and strategies
Wang, Rui1,2; Shen, Yuan3,4; Tino, Peter4; Welchman, Andrew E.2; Kourtzi, Zoe2
2017-10-01
Source PublicationJOURNAL OF VISION
ISSN1534-7362
SubtypeArticle
Volume17Issue:12Pages:1-16
Contribution Rank1
Abstract

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Keywordlearning behavior vision
DOI10.1167/17.12.1
Indexed BySCI ; SSCI
Language英语
Funding OrganizationEngineering and Physical Sciences Research Council(EP/L000296/1) ; Biotechnology and Biological Sciences Research Council(H012508) ; Leverhulme Trust(RF-2011-378) ; European Community's Seventh Framework Programme (FP7)(PITN-GA-2011-290011) ; Wellcome Trust(095183/Z/10/Z)
WOS Research AreaOphthalmology
WOS SubjectOphthalmology
WOS IDWOS:000417128900001
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
WOS Keyword8-MONTH-OLD INFANTS ; VISUAL-ATTENTION ; TIME ; PROBABILITIES ; PERFORMANCE ; LANGUAGE ; MEMORY ; MODEL ; TASK
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/26010
Collection中国科学院心理健康重点实验室
Affiliation1.Chinese Acad Sci, Key Lab Mental Hlth, Inst Psychol, Beijing, Peoples R China
2.Univ Cambridge, Dept Psychol, Cambridge, England
3.Xian Jiaotong Liverpool Univ, Dept Math Sci, Suzhou, Peoples R China
4.Univ Birmingham, Sch Comp Sci, Birmingham, W Midlands, England
Recommended Citation
GB/T 7714
Wang, Rui,Shen, Yuan,Tino, Peter,et al. Learning predictive statistics from temporal sequences: Dynamics and strategies[J]. JOURNAL OF VISION,2017,17(12):1-16.
APA Wang, Rui,Shen, Yuan,Tino, Peter,Welchman, Andrew E.,&Kourtzi, Zoe.(2017).Learning predictive statistics from temporal sequences: Dynamics and strategies.JOURNAL OF VISION,17(12),1-16.
MLA Wang, Rui,et al."Learning predictive statistics from temporal sequences: Dynamics and strategies".JOURNAL OF VISION 17.12(2017):1-16.
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