Extending the Influence of Contextual Information in ACT-R using Buffer Decay

In this paper, we describe an extension of the theory of shortterm 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 (e.g., positional indices) or fixed-length windows of prior elements (e.g., a lag-based representation). While markers and fixedlength 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.