Tracking human skill learning with a hierarchical Bayesian sequence model
Fig 7
Calibration of the HCRP model.
(a) RTs predicted by our HCRP model are shown against measured RTs for d versus r trials on held-out test data. (b) Same as (a) for rH versus rL trials. The two dashed lines mark the mean RTs for d and rH trials in session 8. The RT advantage of d over rH by session 8 marks (> 2)-order sequence learning. (c-d) rH versus rL trials are labelled according to the old trigram model (i.e. old sequence) or the new trigram model (i.e. new sequence). The grey band on the bottom shows the sequence that participants practiced: the old sequence in sessions 1–8 (dark grey), the new sequence in session 9 (light grey) and alternating the two sequences in session 10. (e) (> 2)-order sequence learning, quantified as the standardized RT difference between rH and d trials, shown for measured and predicted RTs. In session 1, rH trials are more expected because they reoccur sooner on average. By session 8, d trials are more expected because they are more predictable, given a > 2 context. This was predicted by the HCRP but not the trigram model. (f) Correlation of the measured and predicted (> 2)-order effect in session 1 and session 8. (g) Average predictive performance of the HCRP and the trigram models. (a-g) The error bands and bars represent the 95%CI.