Neural bases of learning and recognition of statistical regularities

Abstract

Statistical learning is a set of cognitive mechanisms allowing for extracting regularities from the environment and segmenting continuous sensory input into discrete units. The current study used functional magnetic resonance imaging (fMRI) (N = 25) in conjunction with an artificial language learning paradigm to provide new insight into the neural mechanisms of statistical learning, considering both the online process of extracting statistical regularities and the subsequent offline recognition of learned patterns. Notably, prior fMRI studies on statistical learning have not contrasted neural activation during the learning and recognition experimental phases. Here, we found that learning is supported by the superior temporal gyrus and the anterior cingulate gyrus, while subsequent recognition relied on the left inferior frontal gyrus. Besides, prior studies only assessed the brain response during the recognition of trained words relative to novel nonwords. Hence, a further key goal of this study was to understand how the brain supports recognition of discrete constituents from the continuous input versus recognition of mere statistical structure that is used to build new constituents that are statistically congruent with the ones from the input. Behaviorally, recognition performance indicated that statistically congruent novel tokens were less likely to be endorsed as parts of the familiar environment than discrete constituents. fMRI data showed that the left intraparietal sulcus and angular gyrus support the recognition of old discrete constituents relative to novel statistically congruent items, likely reflecting an additional contribution from memory representations for trained items.

Cognitive mechanisms of statistical learning and segmentation of continuous sensory input

“…Consequently, it may be suggested that nonwords were rejected because they violated learnt statistical regularities, phantoms were endorsed based on statistical congruency with learnt regularities, while the higher acceptance rate for words than phantoms can be explained by the facilitatory effect of memory representations of whole triplets as words, presumably extracted and committed to memory as discrete constituents during the learning stage. This interpretation is in line with recent fMRI evidence (Ordin, Polyanskaya, & Soto, 2020a), where activation differences in the neural memory network were reported for presentation of both old and novel-but statistically congruent-triplets.…”

Sustained Pupil Responses Are Modulated by Predictability of Auditory Sequences

“…The mechanisms driving the neural response to regularity are poorly understood, but emerging work (Barascud et al, 2016;Auksztulewicz et al, 2017) has implicated an interplay between auditory cortical, inferior frontal and hippocampal sources in the discovery of regularity. A similar network has also been implicated in detecting more complex predictable structure (see Milne et al, 2018 for a summary and also Abla and Okanoya, 2008;Schapiro et al, 2012;Ordin et al, 2020).…”

Intermodality differences in statistical learning: phylogenetic and ontogenetic influences

“…26,27 Such transitions, characterized by breaches in statistical structure, are likely to require adaptive behavioral responses. This adaptive cycle-in which detecting statistical violations between elements in sequences of events 28 affords optimal behavioral responseshas also honed the evolution of SL mechanisms, which were later redeployed for speech processing. In natural languages, TPs between syllables are often reset at the boundaries between linguistic constituents-words, phrases, and sentences.…”