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Wareham, Todd – Journal of Problem Solving, 2017
In human problem solving, there is a wide variation between individuals in problem solution time and success rate, regardless of whether or not this problem solving involves insight. In this paper, we apply computational and parameterized analysis to a plausible formalization of extended representation change theory (eRCT), an integration of…
Descriptors: Problem Solving, Schemata (Cognition), Intuition, Computation
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Steingroever, Helen; Wetzels, Ruud; Wagenmakers, Eric-Jan – Journal of Problem Solving, 2013
The Iowa gambling task (IGT) is one of the most popular tasks used to study decision-making deficits in clinical populations. In order to decompose performance on the IGT in its constituent psychological processes, several cognitive models have been proposed (e.g., the Expectancy Valence (EV) and Prospect Valence Learning (PVL) models). Here we…
Descriptors: Decision Making, Reinforcement, Comparative Analysis, Models
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Pizlo, Zygmunt; Stefanov, Emil – Journal of Problem Solving, 2013
We describe an important elaboration of our multiscale/multiresolution model for solving the Traveling Salesman Problem (TSP). Our previous model emulated the non-uniform distribution of receptors on the human retina and the shifts of visual attention. This model produced near-optimal solutions of TSP in linear time by performing hierarchical…
Descriptors: Problem Solving, Short Term Memory, Models, Visual Perception
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Ohlsson, Stellan – Journal of Problem Solving, 2012
The research paradigm invented by Allen Newell and Herbert A. Simon in the late 1950s dominated the study of problem solving for more than three decades. But in the early 1990s, problem solving ceased to drive research on complex cognition. As part of this decline, Newell and Simon's most innovative research practices--especially their method for…
Descriptors: Problem Solving, Heuristics, Search Strategies, Cognitive Processes
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Hemmati, Mehdi; Smith, J. Cole – Journal of Problem Solving, 2011
We consider a version of an optimal stopping problem, in which a customer is presented with a finite set of items, one by one. The customer is aware of the number of items in the finite set and the minimum and maximum possible value of each item, and must purchase exactly one item. When an item is presented to the customer, she or he observes its…
Descriptors: Consumer Economics, Problem Solving, Decision Making, Purchasing
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Yi, Sheng Kung M.; Steyvers, Mark; Lee, Michael – Journal of Problem Solving, 2009
Bandit problems provide an interesting and widely-used setting for the study of sequential decision-making. In their most basic form, bandit problems require people to choose repeatedly between a small number of alternatives, each of which has an unknown rate of providing reward. We investigate restless bandit problems, where the distributions of…
Descriptors: Performance, Decision Making, Problem Solving, Rewards
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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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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