Sequence learning recodes cortical representations instead of strengthening initial ones

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 mode...

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Veröffentlicht in:PLoS computational biology 2021-05, Vol.17 (5), p.e1008969-e1008969, Article 1008969
Hauptverfasser: Kalm, Kristjan, Norris, Dennis
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Sprache:eng
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Zusammenfassung: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 patters 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, suggesting that participants recode the learned sequences using chunks. We show that associative learning can theoretically store only very limited number of overlapping sequences, such as common in ecological working memory tasks, and hence an efficient learner should recode initial sequence representations. Author summary The ability to remember multiple individual events as a sequence is necessary for most complex human tasks. There is clear evidence that human sequence learning is accompanied by change in the way sequences are represented in the brain but the exact nature of the change remains unclear. In this study we use brain imaging to ask what is the neural mechanism underpinning sequence learning: we contrast two computational models of learning-associative and recoding-and test their predictions with neural activity data from the dorsal visual processing stream. We provide evidence that, instead of strengthening the initial cortical representations of sequences, learning proceeds by recoding the initial stimuli using a different set of codes. Furthermore, we show that associative learning without recoding is not theoretically capable of supporting long-term memory of short ecological sequences present in every day tasks such as reading, speaking, or navigating.
ISSN:1553-734X
1553-7358
1553-7358
DOI:10.1371/journal.pcbi.1008969