Computational modeling of individual differences in short term memory search

Modeling of individual or group differences, believed to be a powerful test for computational models, is still rare in current cognitive science. In this paper, we discuss alternative approaches to the computational modeling of both qualitative and quantitative differences among individuals as well as groups of individuals. Then, an example is presented of how accounting for individual differences in short term memory (STM) search can bring us insight into cognitive processes underlying this phenomenon, insight that otherways would be impossible. The two-phase computational model of memory search implements the idea of working memory (WM) focus of attention (FA): due to updating process a few items may be actively kept and easily accessed in ACT-R goal buffer. FA is being scanned serially first, and if the scan result is negative, a parallel chunk retrieval from active part of declarative memory outside the FA may run with certain probability. The model aptly simulates steep decrease in accuracy as well as steep increase in latency for responses to five most recent stimuli. The model also predicts the observed effect of faster negative responses than positive responses to less recent stimuli. Most important, with manipulation to only one of its parameters (i.e., the capacity of FA) our model is able to predict 94% of variance for two groups of participants that differed in latency patterns (i.e., ‘serial-like’ vs. ‘parallel-like’ groups) of the search process