A parallel distributed processing model of Wason’s selection task

Abstract

Architectural accounts of cognitive performance are important to explore because they provide the infrastructure for algorithmic theories of cognition [Dawson, M.R.W. (1998). Understanding cognitive science. Malden, MA: Blackwell]. Three parallel distributed processing (PDP) networks were trained to generate the ‘p’, the ‘p and not-q’ and the ‘p and q’ responses, respectively, to the conditional rule used in Wason’s selection task [Wason, P.C. (1966). Reasoning. In: Foss, B.M. (Ed.), New Horizons in Psychology, London, Penguin]. Afterward, each trained network was analyzed for the algorithm it developed to learn the desired response to the task. Analyses of each network’s solution to the task suggested a ‘specialized’ algorithm that focused on card location. For example, if the desired response to the task was found at card 1, then a specific set of hidden units detected the response. In addition, we did not find support that selecting the ‘p’ and ‘q’ response is less difficult than selecting the ‘p’ and ‘not-q’ response. Human studies of the selection task usually find that participants fail to generate the latter response, whereas most easily generate the former. We discuss how our findings can be used to (a) extend our understanding of selection task performance, (b) understand existing algorithmic theories of selection task performance, and (c) generate new avenues of study of the selection task.