The effect of interactivity and transfer in insight problem solving

This research aims to investigate whether manipulating interactivity levels would have an effect on performance in an insight problem.

The information-processing model has traditionally described problem solving as an activity where progressive moves are made towards a solution by journeying through a problem space (Newell & Simon, 1972). More recently, Ohlsson (2011) suggested that working memory played a key role in this activity, assuming people solve problems by mentally restructuring the problem information. By contrast, the theoretical framework provided by distributed cognition suggests a complex co-occurrence between the problem solvers internal cognitive processes and their immediate environment (Kirsh, 2009). Cognition may be distributed across three dimensions: across the mind and its physical environment, through time where earlier events alter later events, and across members of social groups (Hollan, Hutchins & Kirsh, 2000).

When a problem solver is presented with a physical representation of a task, interacting with a physical representation, even with arbitrary moves, may offer cues to new strategies, enable better planning and increase efficiency in progressing towards a goal. Accordingly, when problem solvers are able to interact and restructure their environment, their ability to solve problems should be enhanced. Time distribution is also important and may impact upon problem solving performance, where previously learnt solutions may foster directed behavior towards a goal (e.g., Fioratou, Flin & Galvin, 2010).

So far, we found that increasing the interactivity of problem solvers’ immediate environment can facilitate insight as did incubation. Perhaps surprisingly, the highest proportion of insightful answers was observed when interactivity levels increased after incubation. This shows that insight depends not only on problem solvers’ internal resources but also on the features of the immediate environment, as anticipated by the distributed cognition framework.


Fioratou, E., Flin, R., & Glavin. (2010). No simple fix for fixation errors: Cognitive processes and their clinical applications. Journal of the Associations of Anaesthetists of Great Britain and Ireland, 65, 61-69.

Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interactions, 7(2), 174-196.

Kirsh, D. (2009). Interaction, external representation and sense making. In N. A. Taatgen, & H. v. Rijn (Ed.), Proceedings of the Thirty First Annual Conference of the Cognitive Science Society (pp. 1103-1108). Austin, TX: Cognitive Science Society.

Newell, A., & Simon, H. A. (1972). Human problem solving. Engelwood Ciffs, NJ: Prentice-Hall.
Ohlsson, S. (2011). Deep learning: How the mind overrides experience. New York: Cambridge University Press.

Weller, A., Villejoubert, G., & Vallée-Tourangeau, F. (2011). Interactive Insight Problem Solving. Thinking & Reasoning, 17, 424–439. doi:10.1080/13546783.2011.629081

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