PT  - JOURNAL ARTICLE
AU  - Kristjan Kalm
AU  - Dennis Norris
TI  - Sequence learning recodes cortical representations instead of strengthening initial ones
AID  - 10.1101/496893
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 496893
4099  - http://biorxiv.org/content/early/2020/12/08/496893.short
4100  - http://biorxiv.org/content/early/2020/12/08/496893.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.