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Tuesday, September 14, 2021

Reconstructing the Einstellung effect

Binz, M., & Schulz, E. (2021, August 10).


The Einstellung effect was first described by Abraham Luchins in his doctoral thesis published in 1942. The effect occurs when a repeated solution to old problems is applied to a new problem even though a more appropriate response is available. In Luchins' so-called water jar task, participants had to measure a specific amount of water using three jars of different capacities. Luchins found that subjects kept using methods they had applied in previous trials, even if a more efficient solution for the current trial was available: an Einstellung effect. Moreover, Luchins studied the different conditions that could possibly mediate this effect, including telling participants to pay more attention, changing the number of tasks, alternating between different types of tasks, as well as putting participants under time pressure. In the current work, we reconstruct and reanalyze the data of the various experimental conditions published in Luchins' thesis. We furthermore show that a model of resource-rational decision-making can explain all of the observed effects. This model assumes that people transform prior preferences into a posterior policy to maximize rewards under time constraints. Taken together, our reconstructive and modeling results put the Einstellung effect under the lens of modern-day psychology and show how resource-rational models can explain effects that have historically been seen as deficiencies of human problem-solving.

From the Discussion

It is typically assumed that the best solution for any particular problem is necessarily the shortest, and thus previous research has largely characterized the Einstellung effect as maladaptive behavior.  In the present paper, we have challenged this assumption and provided a resource-rational interpretation of the effect. We did so with the help of an information-theoretic model of decision-making.  The central premise of this  model is to transform prior preferences into posterior policies in a way that trade of expected utility with the time it takes to make a decision. The resulting model incorporates three basic principles: (1) people prefer simple solutions, i.e.,they attempt to spend as little physical effort as possible, (2) they avoid costly computations, i.e., those that require high mental effort, and (3) they adapt to their environment,  i.e., they learn about statistics of the problem they interact with.We found that these simple principles are sufficient to capture the rich characteristics found in Luchins’ data. An additional ablation analysis  confirmed  that  all  of  these  principles  are necessary to reproduce the entire set of phenomena reported in Luchins’ thesis.