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Chen Tian – ProQuest LLC, 2023
The Q-diffusion model is a cognitive process model that considers decision making as an unobservable information accumulation process. Both item and person parameters decide the trace line of the cognitive process, which further decides observed response and response time. Because the likelihood function for the Q-diffusion model is intractable,…
Descriptors: Cognitive Processes, Item Response Theory, Reaction Time, Test Wiseness
Caitlin R. Bowman; Dagmar Zeithamova – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
A major question for the study of learning and memory is how to tailor learning experiences to promote knowledge that generalizes to new situations. In two experiments, we used category learning as a representative domain to test two factors thought to influence the acquisition of conceptual knowledge: the number of training examples (set size)…
Descriptors: Classification, Learning Processes, Generalization, Recognition (Psychology)
Nosofsky, Robert M.; Meagher, Brian J.; Kumar, Parhesh – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2022
A classic issue in the cognitive psychology of human category learning has involved the contrast between exemplar and prototype models. However, experimental tests to distinguish the models have relied almost solely on use of artificially-constructed categories composed of simplified stimuli. Here we contrast the predictions from the models in a…
Descriptors: Cognitive Psychology, Natural Sciences, Experimental Psychology, Prediction
Hu, Mingjia; Nosofsky, Robert M. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2022
In a novel version of the classic dot-pattern prototype-distortion paradigm of category learning, Homa et al. (2019) tested a condition in which individual training instances never repeated, and observed results that they claimed severely challenged exemplar models of classification and recognition. Among the results was a dissociation in which…
Descriptors: Classification, Recognition (Psychology), Computation, Models