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Fleischer, Pierson; Hélie, Sébastien; Pizlo, Zygmunt – Journal of Problem Solving, 2018
Gestalt psychologists pointed out about 100 years ago that a key to solving difficult insight problems is to change the mental representation of the problem, as is the case, for example, with solving the six matches problem in 2D vs. 3D space. In this study we ask a different question, namely what representation is used when subjects solve search,…
Descriptors: Problem Solving, Mathematics, Geometric Concepts, Mathematical Applications
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Levitin, Anany – Journal of Problem Solving, 2017
The paper concerns an important but underappreciated genre of algorithmic puzzles, explaining what these puzzles are, reviewing milestones in their long history, and giving two different ways to classify them. Also covered are major applications of algorithmic puzzles in cognitive science research, with an emphasis on insight problem solving, and…
Descriptors: Problem Solving, Puzzles, Mathematics, Cognitive Science
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Carruthers, Sarah; Masson, Michael E. J.; Stege, Ulrike – Journal of Problem Solving, 2012
Recent studies on a computationally hard visual optimization problem, the Traveling Salesperson Problem (TSP), indicate that humans are capable of finding close to optimal solutions in near-linear time. The current study is a preliminary step in investigating human performance on another hard problem, the Minimum Vertex Cover Problem, in which…
Descriptors: Performance, Problem Solving, Graphs, Mathematics
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Haxhimusa, Yll; Carpenter, Edward; Catrambone, Joseph; Foldes, David; Stefanov, Emil; Arns, Laura; Pizlo, Zygmunt – Journal of Problem Solving, 2011
When a two-dimensional (2D) traveling salesman problem (TSP) is presented on a computer screen, human subjects can produce near-optimal tours in linear time. In this study we tested human performance on a real and virtual floor, as well as in a three-dimensional (3D) virtual space. Human performance on the real floor is as good as that on a…
Descriptors: Problem Solving, Mathematical Applications, Graphs, Visual Aids
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Tak, Susanne; Plaisier, Marco; van Rooij, Iris – Journal of Problem Solving, 2008
To explain human performance on the "Traveling Salesperson" problem (TSP), MacGregor, Ormerod, and Chronicle (2000) proposed that humans construct solutions according to the steps described by their convex-hull algorithm. Focusing on tour length as the dependent variable, and using only random or semirandom point sets, the authors…
Descriptors: Problem Solving, Models, Mathematics, College Students
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Dry, Matthew; Lee, Michael D.; Vickers, Douglas; Hughes, Peter – Journal of Problem Solving, 2006
We investigated the properties of the distribution of human solution times for Traveling Salesperson Problems (TSPs) with increasing numbers of nodes. New experimental data are presented that measure solution times for carefully chosen representative problems with 10, 20, . . . 120 nodes. We compared the solution times predicted by the convex hull…
Descriptors: Problem Solving, Performance, Visual Perception, Time
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Pizlo, Zygmunt; Stefanov, Emil; Saalweachter, John; Li, Zheng; Haxhimusa, Yll; Kropatsch, Walter G. – Journal of Problem Solving, 2006
We tested human performance on the Euclidean Traveling Salesman Problem using problems with 6-50 cities. Results confirmed our earlier findings that: (a) the time of solving a problem is proportional to the number of cities, and (b) the solution error grows very slowly with the number of cities. We formulated a new version of a pyramid model. The…
Descriptors: Problem Solving, Models, Mathematics, Visual Perception