Abstract
Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence of a dissociation, in humans, between two systems, one for handling statistical regularities governing the probabilities of individual items and their transitions, and another for handling deterministic rules. Here, to address this issue, we used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence processing. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over distinct hypothesis spaces for statistics and rules, underlies the human capability for sequence processing.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵† These authors share senior authorship.
We addressed potentiel concern regarding the possibility that results could be explained by another model, a single-system model with appropriate priors: (1) we now explain better in the main text the alternative models we have previously considered and which are relevant to this question, and (2) we added a new series of models that explores all possible combinations of priors. Also, we now frame our research question within the context of sequence processing generally, and not specifically in the context of sequence learning (which often refers to a narrower type of cognitive process and of behavioural task). Finally, we now cite important antecedent works that were missing and now relate our work to prior research on artificial grammar learning, human perception of randomness, memory decay characterizing human sequence processing, human discretization of continuous features in humans, and the role of repetitions in auditory processing. The revisions include: - the substantial modification of 2 main figures, - the addition of 5 new extended data figures, - a new supplementary note, - several new citations, - an improvement of the readability of the manuscript. - the correction of a mistake in the code which moved the change-point one observation later for some analyses.
