Extending the Influence of Contextual Information in ACT-R using Buffer Decay
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Abstract
In this paper, we describe an extension of the theory of short term memory decay for the ACT-R cognitive architecture. By including a short-term decay for elements recently cleared from active memory, we have extended the functionality of spreading activation as a source of implicit contextual information for the model. In ACT-R models of serial memory and decision-making, contextual information has generally been modeled using either explicit markers (eg, positional indices) or fixed-length windows of prior elements (eg, a lag-based representation). While markers and fixed length windows do capture some patterns of human errors, they are inflexible, are set by the modeler and not the model, and are not psychologically-plausible representations of contextual information. In conjunction with our associative learning mechanism (Thomson & Lebiere, 2013), we show how buffer decay can provide more flexible and implicit contextual information which explains refraction, positional confusion errors, and repetition facilitation and inhibition.