Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model
Sequence Learning - Paradigms, Algorithms, and Applications
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Intention superiority effect [J. Exp. Psychol. Learn. Mem. Cogn. 19 (1993) 1211; J. Exp. Psychol. Learn. Mem. Cogn. 24 (1998) 350] is the finding that the times to retrieve memory items related to uncompleted or partially completed intentions are faster than for those with no associated intentions. However, this relationship reverses when the intended tasks are completed [J. Exp. Psychol. Learn. Mem. Cogn. 24 (1998) 350; Mem. Cogn. 27 (1999) 320]. That is, the times to retrieve memory items related to completed intentions are slower than for those with no associated intentions. In this paper, we present a computational account of the intention superiority effect using the ACT-R cognitive architecture [Atomic components of thought. Mahwah, NJ: Lawrence Erlbaum Associates, 1998]. Our modeling approach is based on the idea that uncompleted or partially completed intentions are available as context in the current goal, and they prime related memory items while inhibiting unrelated memory items. However, once the intended tasks are completed, they are removed from the current goal, which produces an inhibitory effect on memory items associated with them. We describe an ACT-R model that is able to reproduce all of the effects reported in Marsh et al. [J. Exp. Psychol. Learn. Mem. Cogn. 24 (1998) 350].