How many heuristics are enough? Testing a parsimonious theory of possibility generation in problem-solving

Abstract

Despite widespread recognition of the need for general-purpose mechanisms in unified theories of cognition, accounts of human thinking often propose that performance is best explained by task- or problem-specific knowledge and heuristics, elements found even in theories proposing general-purpose mechanisms. Here, we argue that many of the effects explained by task- or problem-specific knowledge or heuristics can be better explained by two general mechanisms: maximisation of progress and learning from experience. In Experiment 1, performance on the Taxicab problem, variously described as requiring or not requiring insight, was unaffected by removal of content presumed to trigger a problem-specific heuristic. In Experiment 2, the order of move selection in the Hobbits and Orcs problem, usually described as a transformation problem that does not require insight to solve, was successfully modelled by a maximisation heuristic with conceptual encoding of intermediate states, irrespective of problem context. In Experiment 3, solution rates to the eight-coin puzzle, a problem generally accepted as requiring insight to solve, were increased by cueing moves. A parsimonious theory, based on progress maximisation plus conceptual learning during solution, appears sufficient to model performance across a wide range of problems.