Learning the Abstract General Task Structure in a Rapidly Changing Task Content

Maayan Pereg; Danielle Harpaz; Katrina Sabah; Mattan S. Ben-Shachar; Inbar Amir; Gesine Dreisbach; Nachshon Meiran

doi:10.5334/joc.176

Abstract

The ability to learn abstract generalized structures of tasks is crucial for humans to adapt to changing environments and novel tasks. In a series of five experiments, we investigated this ability using a Rapid Instructed Task Learning paradigm (RITL) comprising short miniblocks, each involving two novel stimulus-response rules. Each miniblock included (a) instructions for the novel stimulus-response rules, (b) a NEXT phase involving a constant (familiar) intervening task (0–5 trials), (c) execution of the newly instructed rules (2 trials). The results show that including a NEXT phase (and hence, a prospective memory demand) led to relatively more robust abstract learning as indicated by increasingly faster responses with experiment progress. Multilevel modeling suggests that the prospective memory demand was just another aspect of the abstract task structure which has been learned.

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Learning the Abstract General Task Structure in a Rapidly Changing Task Content

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