title: Latent structure in random sequences drives neural learning toward a rational bias.
abstract: People generally fail to produce random sequences by overusing alternating patterns and avoiding repeating ones-the gambler's fallacy bias. We can explain the neural basis of this bias in terms of a biologically motivated neural model that learns from errors in predicting what will happen next. Through mere exposure to random sequences over time, the model naturally develops a representation that is biased toward alternation, because of its sensitivity to some surprisingly rich statistical structure that emerges in these random sequences. Furthermore, the model directly produces the best-fitting bias-gain parameter for an existing Bayesian model, by which we obtain an accurate fit to the human data in random sequence production. These results show that our seemingly irrational, biased view of randomness can be understood instead as the perfectly reasonable response of an effective learning mechanism to subtle statistical structure embedded in random sequences.
authors.affiliation: Center for Biomedical Informatics, Texas A&M University Health Science Center, Houston, TX 77030; ysun@tamhsc.edu hwang@tamhsc.edu.
authors.forename: Yanlong
authors.initials: Y
authors.lastname: Sun
authors.affiliation: Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309;
authors.forename: Randall C
authors.initials: RC
authors.lastname: O'Reilly
authors.affiliation: Center for Neural and Emergent Systems, Information and System Sciences Lab, HRL Laboratories LLC, Malibu, CA 90265; and.
authors.forename: Rajan
authors.initials: R
authors.lastname: Bhattacharyya
authors.affiliation: Center for Biomedical Informatics, Texas A&M University Health Science Center, Houston, TX 77030;
authors.forename: Jack W
authors.initials: JW
authors.lastname: Smith
authors.affiliation: Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China 100101.
authors.forename: Xun
authors.initials: X
authors.lastname: Liu
authors.affiliation: Center for Biomedical Informatics, Texas A&M University Health Science Center, Houston, TX 77030; ysun@tamhsc.edu hwang@tamhsc.edu.
authors.forename: Hongbin
authors.initials: H
authors.lastname: Wang
mesh_terms: D001499:Bayes Theorem; D001519:Behavior; D002540:Cerebral Cortex / Q000473:pathology; D005715:Gambling; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D015233:Models, Statistical; D019579:Neocortex / Q000473:pathology; D009415:Nerve Net; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D011336:Probability; D013997:Time Factors
keywords: Bayesian inference; gambler's fallacy; neural network; temporal integration; waiting time