Abstract
A hallmark of Cognitive Science is its interdisciplinary approach to the study of the structures and mechanisms of human and artificial cognitive systems. Over the past decade, sequential pattern acquisition has attracted the attention of researchers from Computer Science, Cognitive Psychology and the Neurosciences. Methodologies for the investigation of sequence learning processes range from the exploration of computational mechanisms to the conduct of experimental studies and the use of event-related brain potentials, functional magnetic resonance imaging and positron emission tomography (Clegg, DiGirolamo, & Keele 1998; Curran 1998). This interest in sequential pattern acquisition is also understandable from an evolutionary perspective: sequencing information and learning of event contingencies are fundamentally important processes without which adaptive behavior in dynamic environments would hardly be possible. From sequencing continuous speech to learning operating sequences of technical devices or acquiring the skill to play a musical instrument, learning of event sequences seems to be an essential capability of human (and artificial) cognition. It is therefore not surprising that we are witnessing a tremendous interest in sequence learning in the disciplines contributing to Cognitive Science. This chapter presents and analyses a model of sequence learning based on the ACT-R theory (Anderson 1993; Anderson & Lebiere 1998). The model is rooted in, and has been successfully applied to, empirical studies using the serial reaction task that was introduced by (1987).
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Lebiere, C., Wallach, D. (2000). Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model. In: Sun, R., Giles, C.L. (eds) Sequence Learning. Lecture Notes in Computer Science(), vol 1828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44565-X_9
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DOI: https://doi.org/10.1007/3-540-44565-X_9
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