Abstract
The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. 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. 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. 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. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.
Highlights
From bird song to music, sea waves, or traffic lights, many processes in real life unfold across time and generate time series of events
One possibility that we explore here is that there are core building blocks of sequence knowledge that the brain uses across many different domains [47]
The local transition probability model assumes that several brain circuits involved in sequence learning entertain the hypothesis that the sequence of items has been generated by a “Markovian” generative process, i.e. only the previous item yt–1 has a predictive power onto the current item yt
Summary
From bird song to music, sea waves, or traffic lights, many processes in real life unfold across time and generate time series of events. Studies of “novelty detection” for instance show that the mere exposure to a sequence of stimuli elicits reproducible “novelty” brain responses that vary quantitatively as a function of the item infrequency and divergence from previous observations [1,2,3,4,5,6,7,8,9,10,11]. Studies using two-alternative forced-choices have revealed “sequential effects”, i.e. fluctuations in performance induced by local regularities in the sequence. Studies asking subjects to produce random sequences or to rate the apparent “randomness” of given sequences, show a notorious underestimation of the likelihood of alternations [24,25,26,27,28,29]
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