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TITLE: Bayesian Change-Detection with Identification: Sequential Algorithm and Neuronal Evidence
ABSTRACT:
How do animal and people combine prior knowledge with evidence gathered on a moment-to-moment evidence for decision-making has been well-studied in psychology using choice-reaction time paradigm. Here, we consider the problem of change detection along with identification in multi-hypotheses setting, assuming a known prior. A Bayesian sequential
updating algorithm is proposed, along with the threshold-crossing stopping rule common in sequential analysis. The value of an absorbing boundary is shown to exactly equal the hit rate of a decision-maker conditioned on that response. Computer simulation reveals that the
algorithm shares many similarities with human performance in stimulus detection/identification experiments. Given recent evidence from neuroscience in support of sequential analysis algorithm, we report a re-analysis of the neuronal data recorded by Roitman and Shadlen (2002) in the monkey’s lateral intraparietal cortex (LIP). Results show that neuronal activity accumulates during each trial up until monkey’s behavioral response, that the accumulation (“buildup”) rate is monotonically related to the strength of the stimulus, and that buildup activities encode intended movement rather than sensory information.
Short Bio:
Dr. Jun Zhang is a Professor of Psychology and Professor of Mathematics at the University of Michigan, Ann Arbor. He received the B.Sc. degree in Theoretical Physics from Fudan University in 1985, and Ph.D. degree in Neurobiology from the University of California, Berkeley in 1992. He has also held visiting positions at the University of Melbourne, the
University of Waterloo, and RIKEN Brain Science Institute. During 2007-2010, he worked as the Program Manager for the U.S. Air Force Office of Scientific Research (AFOSR) in charge of the basic research portfolio for Cognition and Decision in the Directorate of Mathematics,
Information, and Life Sciences. Dr. Zhang served as the President for the Society for Mathematical Psychology (SMP) and serves on the Federation of Associations in Brain and Behavioral Sciences (FABBS). He is Associate Editor for the Journal of Mathematical Psychology, and a Fellow of the Association for Psychology Sciences (APS). Dr. Zhang’s
publications span the fields of vision, mathematical psychology, cognitive psychology, cognitive neuroscience, game theory, machine learning, information geometry, etc. His research has been funded by the National Science Foundation (NSF), Air Force Office for Scientific Research (AFOSR), and Army Research Office (ARO).
