Multiple paths to transfer and constraint relaxation in insight problem solving

Kershaw, Trina C.; Flynn, Christopher K.; Gordon, Leamarie T.

doi:10.1080/13546783.2012.742852

Cited by 29 publications

(35 citation statements)

References 46 publications

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Section: Nine-dot Problem 12mentioning

confidence: 99%

“…Second, there is no particular class of insight problems that necessarily requires a representational change; each problem can be solved without insight if the initial problem representation is adequate and the appropriate heuristics are available. Third, the difficulty related to attaining insight can have different causes (Kershaw, Flynn, & Gordon, 2013;Kershaw & Ohlsson, 2004). Whereas previous models have highlighted the role of perception, memory, and heuristics, the present model tries to capture the interplay between these factors.…”

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confidence: 96%

“…BLR provides a method of analysing the influence of dichotomous, discrete, or continuous predictors on a binary outcome variable, and has already been successfully applied to the analysis of insight problem solving experiments (e.g. Kershaw et al, 2013;Öllinger et al, 2012). BLR produces B-values, and odds ratios.…”

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confidence: 99%

“…BLR produces B-values, and odds ratios. B-values indicate the direction of the relationship; odds ratios indicate the likelihood that a participant in a particular group can be categorized as a solver e.g., an oddsratio of 2 for a participant in a particular condition illustrates that the participant is 2 times more likely to solve the problem than for the baseline (CG) condition (see Kershaw et al, 2013).Entering the three experimental conditions and the CG as baseline resulted in a significant model,  2 (3, 135) = 40.61 that classified 74.8% of solvers correctly. Table 2 shows the BLR coefficient B, the Wald  2 , and the odds ratio for each of the three conditions.…”

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confidence: 99%

“…insight), since this is not always the case (Jones, 2003). Equally, whether insight is achieved and if so how quickly it is achieved depends on multiple factors, such as the difficulty of a change to the problem representation and how large the problem space is (Knoblich et al, 1999;Kershaw et al, 2013).…”

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confidence: 99%

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The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem

Öllinger

Jones

Knoblich³

2013

Psychological Research

112

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The nine-dot problem is often used to demonstrate and explain mental impasse, creativity, and out of the box thinking. The present study investigated the interplay of a restricted initial search space, the likelihood of invoking a representational change, and the subsequent constraining of an unrestricted search space. In three experimental conditions, participants worked on different versions of the nine-dot problem that hinted at removing particular sources of difficulty from the standard problem. The hints were incremental such that the first suggested a possible route for a solution attempt; the second additionally indicated the dot at which lines meet on the solution path; and the final condition also provided non-dot locations that appear in the solution path. The results showed that in the experimental conditions, representational change is encountered more quickly and problems are solved more often than for the control group. We propose a cognitive model that focuses on general problem solving heuristics and representational change to explain problem difficulty.Keywords: Nine-Dot Problem, Insight, Heuristics, Search, Representational Change Nine-dot problem 3There is growing evidence that an insight to the solution of a problem can be characterized by a representational change (Knoblich, Ohlsson, Haider, & Rhenius, 1999;Ohlsson, 1984aOhlsson, , 1992Öllinger, Jones, & Knoblich, 2008;Thevenot & Oakhill, 2008). This evidence makes it difficult to explain insight problem solving within the classical information-processing account (Newell & Simon, 1972), where problem solving is understood as search within a well-defined problem space (problem representation). The problem space account has no mechanism to implement a representational change for instances when the current search gets stuck, is insufficient, or does not reduce the distance to the desired goal.There are a few accounts that attempt to remedy this omission. One suggestion is to claim that insight problems are nothing special and therefore representational change plays only a marginal role. For such explanations, problem difficulty relates either to the size of the problem space being overly large and preventing exhaustive search (Kaplan & Simon, 1990), or that problem solvers apply inappropriate heuristics when searching the problem space Ormerod, MacGregor, & Chronicle, 2002). Both accounts miss a cognitive process that addresses the change of the search space. In the first, an additional process is necessary that re-focuses on particular areas of the search space by changing the problem representation; in the second an additional cognitive process is required that changes the search space after repeated failures of the problem solving process.Ohlsson (Knoblich et al., 1999;Ohlsson, 1984aOhlsson, , 1984bOhlsson, , 1992) provided a detailed framework that stressed the importance of a representational change for insight problem solving, and identified impasse as a crucial pre-condition. Moreover, Ohlsson (1992) identified at least three ...

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Section: Nine-dot Problem 12mentioning

confidence: 99%

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confidence: 96%

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confidence: 99%

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confidence: 99%

mentioning

confidence: 99%

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The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem

Öllinger

Jones

Knoblich³

2013

Psychological Research

112

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Insight into the ten-penny problem: guiding search by constraints and maximization

Öllinger

Fedor

Brodt

et al. 2016

Psychological Research

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For a long time, insight problem solving has been either understood as nothing special or as a particular class of problem solving. The first view implicates the necessity to find efficient heuristics that restrict the search space, the second, the necessity to overcome self-imposed constraints. Recently, promising hybrid cognitive models attempt to merge both approaches. In this vein, we were interested in the interplay of constraints and heuristic search, when problem solvers were asked to solve a difficult multi-step problem, the ten-penny problem. In three experimental groups and one control group (N = 4 × 30) we aimed at revealing, what constraints drive problem difficulty in this problem, and how relaxing constraints, and providing an efficient search criterion facilitates the solution. We also investigated how the search behavior of successful problem solvers and non-solvers differ. We found that relaxing constraints was necessary but not sufficient to solve the problem. Without efficient heuristics that facilitate the restriction of the search space, and testing the progress of the problem solving process, the relaxation of constraints was not effective. Relaxing constraints and applying the search criterion are both necessary to effectively increase solution rates. We also found that successful solvers showed promising moves earlier and had a higher maximization and variation rate across solution attempts. We propose that this finding sheds light on how different strategies contribute to solving difficult problems. Finally, we speculate about the implications of our findings for insight problem solving.

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