Dynamics and Constraints in Insight Problem Solving Thomas C. Ormerod Lancaster University James N. MacGregor University of Victoria Edward P. Chronicle Lancaster University This article reports 2 experiments that investigated performance on a novel insight problem, the 8-coin problem. The authors hypothesized that participants would make certain initial moves (strategic moves) that seemed to make progress according to the problem instructions but that nonetheless would guarantee failure to solve the problem. Experiment 1 manipulated the starting state of the problem and showed that overall solution rates were lower when such strategic moves were available. Experiment 2 showed that failure to capitalize on visual hints about the correct first move was also associated with the availability of strategic moves. The results are interpreted in terms of an information-processing framework previously applied to the 9-dot problem. The authors argue that in addition to the operation of inappropriate constraints, a full account of insight problem solving must incorporate a dynamic that steers solution-seeking activity toward the constraints. The introduction of the study of insight into modern psychology is generally credited to the Gestalt psychologists: most impor- tantly, Kohler’s (1925) work on intelligent problem solving in chimpanzees, Wertheimer’s (1959) studies of the role of restruc- turing in productive thinking, and Duncker’s (1945) program of experiments on insight problems. This pioneering work was fol- lowed by a relatively long hiatus that, judging by the recent resurgence of interest, has now ended (e.g., Sternberg & Davidson, 1995). A common theoretical thread that runs through recent contributions is that insight requires the removal of one or more unnecessary constraints imposed by the solver on the actions that they take in attempting to solve the problem (e.g., Adams, 1974; Davidson, 1995; Gick & Lockhart, 1995; Ohlsson, 1992; Smith & Blankenship, 1991). For example, consider the nine-dot problem, a problem that is simple to state, yet notoriously difficult to solve. The task is to draw four straight lines that, together, intersect each dot of a regular 3  3 grid of dots, without retracing and without lifting the pen off the paper until the end of the final line. The traditional Gestalt explanation for the problem’s difficulty is that solvers impose an implicit constraint that lines may not violate the boundary of the square formed by the nine dots (e.g., Scheerer, 1963). More recent explanations (e.g., Lung & Dominowski, 1985; Weisberg & Alba, 1981) point to different constraints but share the view that the locus of problem difficulty is centered on the solver’s constrained representation of the problem. Despite widespread agreement that inappropriate constraints are the main source of problem difficulty and that their removal allows insight to occur, the mechanisms by which they are removed, and the processes that enable attention to be focused on more fruitful aspects of a problem, remain puzzling. This puzzling nature of the insight process has been expressed in the following, almost para- doxical, terms. If a problem is eventually solved, then the solver clearly has the knowledge or the competency to do so. Why, then, does the impasse arise in the first place? On the other hand, given that an impasse has arisen, what, then, makes it go away? (Knoblich, Ohlsson, Haider, & Rhenius, 1999; Ohlsson, 1984). The answer Knoblich et al. (1999) and Ohlsson (1992) proposed was that past experience biases the initial representation of the problem in a manner that hinders finding the solution and that to overcome this, a change in the problem representation is required. Ohlsson’s (1992) general insight framework has been developed by Knoblich et al. (1999) into a more precise and testable theory that proposes key roles for both constraint relaxation and chunk decomposition, a particular type of reencoding, as sources of insightful moves. Knoblich et al. argued that the probability of any particular problem constraint being relaxed is inversely related to its scope, that is, how much of the current problem representation is affected by the constraint. Similarly, they argued that the prob- ability of reencoding any particular piece of problem information by decomposition is an inverse function of the tightness with which that information is chunked in the current representation. Chunks are loose if they can be decomposed into constituent elements that themselves are recognizable chunks, whereas they Thomas C. Ormerod and Edward P. Chronicle, Department of Psychol- ogy, Lancaster University, Lancaster, United Kingdom; James N. MacGre- gor, Department of Public Administration, University of Victoria, Victoria, British Columbia, Canada. This research was partially supported by a research grant to James N. MacGregor from the Natural Science and Engineering Research Council of Canada. We gratefully acknowledge the statistical help of Mike Hunter, Department of Psychology, University of Victoria, and Damon Berridge, Centre for Applied Statistics, University of Lancaster, who advised us on the analysis of binary data. Any errors of understanding are ours alone. Correspondence concerning this article should be addressed to Edward P. Chronicle, Department of Psychology, Lancaster University, Lancaster LA1 4YF, United Kingdom. E-mail: E.Chronicle@lancaster.ac.uk Journal of Experimental Psychology: Copyright 2002 by the American Psychological Association, Inc. Learning, Memory, and Cognition 2002, Vol. 28, No. 4, 791–799 0278-7393/02/$5.00 DOI: 10.1037//0278-7393.28.4.791 791