Human sequence learning involves creating new neural representations not strengthening old ones

We contrast two accounts of how novel sequences are learned. The first is that learning changes the signal-to-noise ratio (SNR) of existing neural representations by reducing noise or increasing signal gain. Alternatively, learning might cause the initial representation of the sequence to be recoded into more efficient representations such as chunks. Both mechanisms reduce the amount of information required to store sequences, but make contrasting predictions about changes in neural activity patterns. We applied representational similarity analysis to patterns of fMRI activity as participants encoded, maintained, and recalled novel and learned sequences of oriented Gabor patches. We found no evidence for the SNR-change hypothesis. Instead, we observed that two brain regions in the dorsal visual processing stream encoded learned sequences as predicted by the chunking model. Our results suggest that learning-induced recoding elicits chunk-like representations of the learned sequence rather than simply strengthening the initial representations.