Sequence learning recodes cortical representations instead of strengthening initial ones.

ABSTRACT: We contrast two computational models of sequence learning. The associative learner posits that learning proceeds by strengthening existing association weights. Alternatively, recoding posits that learning creates new and more efficient representations of the learned sequences. Importantly, both models propose that humans act as optimal learners but capture different statistics of the stimuli in their internal model. Furthermore, these models make dissociable predictions as to how learning changes the neural representation of sequences. We tested these predictions by using fMRI to extract neural activity patterns from the dorsal visual processing stream during a sequence recall task. We observed that only the recoding account can explain the similarity of neural activity patterns, suggestin

SUBMITTER: Kalm K 

PROVIDER: S-EPMC8177667 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature