Sequence learning recodes cortical representations instead of strengthening initial ones
Fig 8
(A) Evidence for three alternative chunking models and their components at the beginning of the scanning experiment, when participants had seen the two repeating sequences 12 times each during the practice session. The three models use only single type n-grams: the 1-gram model encodes sequences using four single-item n-grams, 2-gram model with two bi-grams, and the 4-gram model with a single four-gram. The left panel shows the probability of the set of n-grams (code) each model specifies in terms of their negative log values. The centre panel shows the probability of their mappings (encoding) and the right panel the combination of the two into model evidence. The blue and red parts of the model evidence bar represent model code (n-grams) and encoding (mappings) probabilities in terms of their negative logs and the total length of the bar displays the model evidence as their sum. This allows intuitive visualisation of the code-encoding trade-off calculated by the Bayesian model comparison. The 4-gram model is the optimal model at the start of the experiment. (B) Model evidence across trials. X-axis shows the trial number and y-axis shows the log of model evidence. The optimal model is inferred at every trial; the 1-gram model encodes sequences only with four uni-grams, and the 4-gram model only uses four-grams. Note that at the beginning of the experiment the 4-gram model is equivalent to the optimal model: however, as new sequences are presented the optimal model encodes new data with shorter chunks (uni-grams) while the 4-gram model encodes new unique sequences with four-grams. Note that as new data is observed the evidence for any particular model decreases as the set of data becomes larger and the space of possible models increases exponentially. Also note that the log scale transforms the change of evidence over trials into linear form.