Most psychological theories of problem solving have focused on modeling explicit processes that gradually bring the solver closer to the solution in a mostly explicit and deliberative way. This approach to problem solving is typically inefficient when the problem is too complex, ill-understood, or ambiguous. In such a case, a ‘creative’ approach to problem solving might be more appropriate. In the present paper, we propose a neural-network-based computational model implementing the Explicit-Implicit Interaction theory of creative problem solving that involves alternating between implicit and explicit processing. In the present, the new model is used to simulate the incubation process in lexical decision and free recall.
['Sébastien Hélie', 'Ron Sun']
Simulating incubation effects using the Explicit Implicit Interaction with Bayes Factor (EII-BF) model