Modeling human sequence learning under incidental conditions.

Abstract

This research explored the role that associative learning may play in human sequence learning. Two-choice serial reaction time tasks were performed under incidental conditions using 2 different sequences. In both cases, an experimental group was trained on 4 subsequences: LLL, LRL, RLR, and RRR for Group "Same" and LLR, LRR, RLL, and RRL for Group "Different," with left and right counterbalanced across participants. To control for sequential effects, we assayed sequence learning by comparing their performance with that of a control group, which had been trained on a pseudorandom ordering, during a test phase in which both experimental and control groups experienced the same subsequences. Participants in both groups showed sequence learning, but the group trained on "different" learned more and more rapidly. This result is the opposite that predicted by the augmented simple recurrent network used by F. W. Jones and I. P. L. McLaren (2009, Human sequence learning under incidental and intentional conditions, Journal of Experimental Psychology: Animal Behavior Processes, Vol. 35, pp. 538-553), but can be modeled using a reparameterized version of this network that also includes a more realistic representation of the stimulus array, suggesting that the latter may be a better model of human sequence learning under incidental conditions.