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Human Inferences about Sequences: A Minimal Transition Probability Model

Article by Florent Meyniel and Maxime Maheu and Stanislas Dehaene
  • Published in 2016
  • Added on
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.

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BibTeX entry

@article{HumanInferencesaboutSequencesAMinimalTransitionProbabilityModel,
	title = {Human Inferences about Sequences: A Minimal Transition Probability Model},
	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.},
	url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005260},
	author = {Florent Meyniel and Maxime Maheu and Stanislas Dehaene},
	comment = {},
	urldate = {2017-01-09},
	collections = {The act of doing maths,Probability and statistics},
	year = 2016
}