Neural bases of learning and recognition of statistical regularities
Ordin, M., Polyanskaya, L. and Soto, D. (2020), Neural bases of learning and recognition of statistical regularities. Ann. N.Y. Acad. Sci., 1467: 60-76. doi:10.1111/nyas.14299
Laburpena
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.