purely random) and restricted to only two possible items or actions. Various effects suggestive of local sequence learning have been consistently reported, even when experimental sequences are devoid of any regularity (i.e. The ability to detect such sequential regularities is fundamental to adaptive behavior, and many experiments in psychology and neuroscience have assessed this ability by appealing to tasks involving sequences of events. Sequences of observations are therefore often underpinned by some regularity that depends on the underlying generative process. The funders had no role in the study design, analysis or decision to publish.Ĭompeting interests: The authors have declared that no competing interests exist.įrom bird song to music, sea waves, or traffic lights, many processes in real life unfold across time and generate time series of events. 604102 - “Human Brain Project“ (FM, SD) and by an advanced European Research Council grant “NeuroSyntax” (SD). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Codes are available at įunding: This work was funded by Institut National de la Santé Et de la Recherche Médicale (SD), Commissariat à l’Energie Atomique (SD, FM), Collège de France (SD), a “Frontières du Vivant” doctoral fellowship involving the Ministère de l’Enseignement Supérieur et de la Recherche and Programme Bettencourt (MM), a grant from the European Union Seventh Framework Programme (FP7/2007 2013, ) under grant agreement no. Received: AugAccepted: NovemPublished: December 28, 2016Ĭopyright: © 2016 Meyniel et al. Gershman, Harvard University, UNITED STATES PLoS Comput Biol 12(12):Įditor: Samuel J. Our model therefore unifies many previous findings and suggests that a neural machinery for inferring transition probabilities must lie at the core of human sequence knowledge.Ĭitation: Meyniel F, Maheu M, Dehaene S (2016) Human Inferences about Sequences: A Minimal Transition Probability Model. whether humans think that a given sequence of observations has been generated randomly or not. Last, we consider the notoriously biased subjective perception of randomness, i.e. These signals are reportedly modulated in a quantitative manner by both the local and global statistics of observations. We also consider the “surprise-like” signals recorded in electrophysiology and even functional MRI, that are elicited by a random stream of observations. the pervasive fluctuations in performance induced by the recent history of observations. Such findings include the “sequential effects” evidenced in many behavioral tasks, i.e. We focus on five representative studies by other groups. We list six such properties and we test them successfully against various experimental findings reported in distinct fields of the literature over the past century. Expectations derived from such a model should conform to several properties. Humans may then use these estimates to predict future observations. We explore the possibility that the computation of time-varying transition probabilities may be a core building block of sequence knowledge in humans. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations.
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