Between automatic and control processes: How relationships between problem elements interact to facilitate or impede...

Solving a problem requires relating the pieces of information available to each other and to the solution. We investigated how the strength of these relationships determines the likelihood of solving insight tasks based on remote associates. In these tasks, the solver is provided with several cues (e.g., drop, coat, summer) and has to find the solution that matches those cues (e.g., rain). We measured the semantic similarity between the cues and the solution (cue-solution similarity) as well as between cues (cue-cue similarity). We assume those relationships modulate two basic processes underlying insight problem-solving. First, there is an automatic activation process whereby conceptual activation spreads across a semantic network from each cue node to their associated nodes, potentially reaching the node of the solution. Thus, in general, the higher cue-solution similarity, the more likely the solution will be found (Prediction 1). Second, there is a controlled search process focused on an area in semantic space whose radius depends on competing cue-cue similarity. High cue-cue similarity will bias a search for the solution close to the provided cues because the associated nodes shared by both cues are highly coactivated. Therefore, high cue-cue similarity will have a beneficial effect when the cue-solution similarity is high but a detrimental effect when cue-solution similarity is low (Prediction 2). Our two predictions were confirmed using both verbal and pictorial remote association tasks, supporting the view that insight is dependent on an interaction of meaningful relationships between cues and solutions, and clarify the mechanisms of insight problem solving in remote associates.