Cognitive Flexibility in Cognitive Architecture: Simulating using Contextual Learning in PRIMs

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The universal flexibility of biological systems needs to be reflected in cognitive architecture. In PRIMs, we attempt to achieve flexibility through a bottom-up approach. Using contextual learning, randomly firing of a set of instantiated primitive operators are gradually organized into context-sensitive operator firing sequences (i.e., primordial “skills”). Based on this implementation, the preliminary results of the model simulated the averaged single-pattern processing latency that is consistent with infants’ differential focusing time in three theoretically controversial artificial language studies, namely Saffran, Aslin, and Newport (1996), Marcus, Vijayan, Rao, and Vishton (1999), and Gomez (2002). In our ongoing work, we are analyzing (a) whether the model can arrive at primordial “skills” adaptive to the trained tasks, and (b) whether the learned chunks mirror the trained patterns.
Original languageEnglish
Title of host publicationPoster session presented at 18th International Conference on Cognitive Modeling
Publication statusE-pub ahead of print - 2020
EventThe 18th Annual Meeting of the International Conference on Cognitive Modelling - Toronto, Canada
Duration: 22-Jul-202031-Jul-2020


ConferenceThe 18th Annual Meeting of the International Conference on Cognitive Modelling

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