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Automaticity is extremely common in our daily lives: we perform many routine tasks (e.g. reading) effortlessly with little thought or conscious awareness. In one of the most famous studies published in the field of cognitive psychology, Shiffrin and Schneider (1977) demonstrated how automaticity could be achieved with long training that mapped stimuli to responses consistently (denoted CM). Their demonstrations used visual and memory search for small numbers of items. The many years since those reports notwithstanding, the precise cognitive and neurological mechanisms that underlie the development of automaticity remain elusive. My thesis aims to explore memory search with empirical studies and in particular with quantitative modeling to specify the way that automaticity develops, the rate at which it does so, and the degree to which its development is an automatic consequence of training. To address this issue with computational modeling, I adapted the Exemplar-Based-Random-Walk (EBRW) model. This model has provided excellent accounts of accuracy data and response time data in categorization learning. I extended EBRW to incorporate well-established theories about automaticity learning, specifically, learning of item-response associations in long-term memory. The resultant models were applied to tasks mixing items that were and were not trained consistently, and were compared to alternatives that produced behavior as a consequence of other well-known processes such as decisions based on familiarity. The results demonstrate that the development and use of automaticity is not simply a matter of consistent training, and shows the importance of strategies. A study with measures from an electroencephalogram provided further insights into the processes used to carry out memory search. Both the empirical studies and the modeling suggest that the development of automaticity is a result of a complex interaction of attention, strategy, memory, and learning. |
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