Learning predictive statistics from temporal sequences: Dynamics and strategies

Journal of vision

Peter Tino¹  Yuan Shen²  Rui Wang³  Zoe Kourtzi⁴  Andrew E. Welchman⁴ 

[1] Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China;Department of Psychology, University of Cambridge, Cambridge, UK;Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China,;School of Computer Science, University of Birmingham, Birmingham, UK

关键词: learning;    symbols;    training;   

DOI  :  10.1167/17.12.1

学科分类：眼科学

来源: Association for Research in Vision and Ophthalmology

【 摘 要 】

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.

【 授权许可】

CC BY