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Carruthers, Sarah; Stege, Ulrike; Masson, Michael E. J. – Journal of Problem Solving, 2018
The role that the mental, or internal, representation plays when people are solving hard computational problems has largely been overlooked to date, despite the reality that this internal representation drives problem solving. In this work we investigate how performance on versions of two hard computational problems differs based on what internal…
Descriptors: Problem Solving, Goal Orientation, Computation, Difficulty Level
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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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Chan, Joel; Nokes-Malach, Timothy J. – Journal of Problem Solving, 2016
People often use spatial metaphors (e.g., think "laterally," "outside the box") to describe exploration of the problem space during creative problem solving. In this paper, we probe the potential cognitive underpinnings of these spatial metaphors. Drawing on theories of situative cognition, semantic foraging theory, and…
Descriptors: Creativity, Physical Environment, Novelty (Stimulus Dimension), Creative Thinking
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Dry, Matthew J.; Fontaine, Elizabeth L. – Journal of Problem Solving, 2014
The Traveling Salesperson Problem (TSP) is a computationally difficult combinatorial optimization problem. In spite of its relative difficulty, human solvers are able to generate close-to-optimal solutions in a close-to-linear time frame, and it has been suggested that this is due to the visual system's inherent sensitivity to certain geometric…
Descriptors: Problem Solving, Geographic Location, Computation, Visual Stimuli
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Carruthers, Sarah; Stege, Ulrike – Journal of Problem Solving, 2013
This article is concerned with how computer science, and more exactly computational complexity theory, can inform cognitive science. In particular, we suggest factors to be taken into account when investigating how people deal with computational hardness. This discussion will address the two upper levels of Marr's Level Theory: the computational…
Descriptors: Problem Solving, Computation, Difficulty Level, Computer Science
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Kwisthout, Johan – Journal of Problem Solving, 2012
When computer scientists discuss the computational complexity of, for example, finding the shortest path from building A to building B in some town or city, their starting point typically is a formal description of the problem at hand, e.g., a graph with weights on every edge where buildings correspond to vertices, routes between buildings to…
Descriptors: Problem Solving, Computation, Abstract Reasoning, Difficulty Level
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Fischer, Andreas; Greiff, Samuel; Funke, Joachim – Journal of Problem Solving, 2012
This article is about Complex Problem Solving (CPS), its history in a variety of research domains (e.g., human problem solving, expertise, decision making, and intelligence), a formal definition and a process theory of CPS applicable to the interdisciplinary field. CPS is portrayed as (a) knowledge acquisition and (b) knowledge application…
Descriptors: Problem Solving, Difficulty Level, Expertise, Decision Making
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Ash, Ivan K.; Jee, Benjamin D.; Wiley, Jennifer – Journal of Problem Solving, 2012
Gestalt psychologists proposed two distinct learning mechanisms. Associative learning occurs gradually through the repeated co-occurrence of external stimuli or memories. Insight learning occurs suddenly when people discover new relationships within their prior knowledge as a result of reasoning or problem solving processes that re-organize or…
Descriptors: Intuition, Learning Processes, Metacognition, Associative Learning
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Wareham, Todd; Evans, Patricia; van Rooij, Iris – Journal of Problem Solving, 2011
Solving new problems can be made easier if one can build on experiences with other problems one has already successfully solved. The ability to exploit earlier problem-solving experiences in solving new problems seems to require several cognitive sub-abilities. Minimally, one needs to be able to retrieve relevant knowledge of earlier solved…
Descriptors: Logical Thinking, Problem Solving, Difficulty Level, Computation
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Taatgen, Niels A. – Journal of Problem Solving, 2011
The minimal control principle (Taatgen, 2007) predicts that people strive for problem-solving strategies that require as few internal control states as possible. In an experiment with the Abstract Decision Making task (ADM task; Joslyn & Hunt, 1998) the reward structure was manipulated to make either a low-control strategy or a high-strategy…
Descriptors: Problem Solving, Abstract Reasoning, Decision Making, Learning Strategies
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Chu, Yun; Li, Zheng; Su, Yong; Pizlo, Zygmunt – Journal of Problem Solving, 2010
Isomorphs of a puzzle called m+m resulted in faster solution times and an easily reproduced solution path in a labeled version of the problem compared to a more difficult binary version. We conjecture that performance is related to a type of heuristic called direction that not only constrains search space in the labeled version, but also…
Descriptors: Heuristics, Problem Solving, Puzzles, Navigation
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MacGregor, James N.; Cunningham, John B. – Journal of Problem Solving, 2009
Insight problem solving is characterized by restructuring. We hypothesized that the difficulty of rebus puzzles could be manipulated by systematically varying the restructurings required to solve them. An experiment using rebus puzzles varied the number of restructurings (one or two) required to solve a problem and the level at which the…
Descriptors: Problem Solving, Numbers, Difficulty Level, Puzzles
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Chronicle, Edward P.; MacGregor, James N.; Lee, Michael; Ormerod, Thomas C.; Hughes, Peter – Journal of Problem Solving, 2008
Results on human performance on the Traveling Salesman Problem (TSP) from different laboratories show high consistency. However, one exception is in the area of individual differences. While one research group has consistently failed to find systematic individual differences across instances of TSPs (Chronicle, MacGregor and Ormerod), another…
Descriptors: Individual Differences, Problem Solving, Performance, Research Problems