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Fangli Xia; Mitchell J. Nathan; Kelsey E. Schenck; Michael I. Swart – Cognitive Science, 2025
Task-relevant actions can facilitate mathematical thinking, even for complex topics, such as mathematical proof. We investigated whether such cognitive benefits also occur for action predictions. The action-cognition transduction (ACT) model posits a reciprocal relationship between movements and reasoning. Movements--imagined as well as real ones…
Descriptors: Undergraduate Students, Geometry, Mathematical Concepts, Mathematics Instruction
Okada, Takeshi; Ishibashi, Kentaro – Cognitive Science, 2017
To investigate the cognitive processes underlying creative inspiration, we tested the extent to which viewing or copying prior examples impacted creative output in art. In Experiment 1, undergraduates made drawings under three conditions: (a) copying an artist's drawing, then producing an original drawing; (b) producing an original drawing without…
Descriptors: Cognitive Processes, Creative Activities, Art Activities, Freehand Drawing
Lovett, Marsha C. – Cognitive Science, 2005
Most accounts of the Stroop effect (Stroop, 1935) emphasize its negative aspect, namely, that in particular situations, processing of an irrelevant stimulus dimension interferes with participants' performance of the instructed task. In contrast, this paper emphasizes the fact that, even with that interference, participants actually can (and…
Descriptors: Stimuli, Models, Cognitive Processes, Prediction
Taatgen, Niels – Cognitive Science, 2005
Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule-based models that often produce brittle behavior. Rule-based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task-specific rules from instructions…
Descriptors: Thinking Skills, Cognitive Processes, Models, Skill Development

Rumelhart, David E.; Zipser, David – Cognitive Science, 1985
Reports results of studies with an unsupervised learning paradigm called competitive learning which is examined using computer simulation and formal analysis. When competitive learning is applied to parallel networks of neuron-like elements, many potentially useful learning tasks can be accomplished. (Author)
Descriptors: Artificial Intelligence, Cognitive Processes, Computer Simulation, Input Output