Many theories of problem solving have assumed a role for implicit cognitive processes. For instance, implicit processes are thought to generate hypotheses that are explicitly tested until a problem is solved (Evans, 1984, 2006). Also, Wallas' (1926) stage decomposition of creative problem solving included an implicit stage called 'incubation'. As a result, implicit knowledge is thought to be responsible for many correct solutions when solving insight problems. In this presentation, we propose a two-level connectionist model composed of a regular two-layer network and a Hopfield-type network. The former is linear and represents information locally to model associations in explicit memory. In contrast, the Hopfield-type neural network is non-linear and uses randomly generated distributed representations to model implicit knowledge. This representational difference is believed to reflect the difference in accessibility of explicit and implicit knowledge. The networks are connected to form a bidirectional associative memory. The stimuli are processed in both networks simultaneously until convergence and their outputs are integrated using a Bayesian function. Insight is modeled by the crossing of a threshold by the integrated output's activation. If the integrated output's activation is not sufficient to produce insight, the output of the model is used as the input for another iteration of processing. This model setting was used to simulate the hypothesis generation process in insight problem solving and the effect of incubation in a lexical decision task. |