Robert M. Nosofsky

Affiliations: 
Indiana University, Bloomington, Bloomington, IN, United States 
Area:
categorization, mathematical psychology
Google:
"Robert Nosofsky"
Mean distance: 13.88 (cluster 15)
 
SNBCP
Cross-listing: PsychTree - MathTree

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Publications

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Nosofsky RM, Meagher BJ, Kumar P. (2022) Contrasting exemplar and prototype models in a natural-science category domain. Journal of Experimental Psychology. Learning, Memory, and Cognition
Hu M, Nosofsky RM. (2021) Exemplar-model account of categorization and recognition when training instances never repeat. Journal of Experimental Psychology. Learning, Memory, and Cognition
Nosofsky RM, Cao R, Harding SM, et al. (2020) Modeling short- and long-term memory contributions to recent event recognition. Journal of Experimental Psychology. Learning, Memory, and Cognition
Sanders CA, Nosofsky RM. (2020) Training Deep Networks to Construct a Psychological Feature Space for a Natural-Object Category Domain Computational Brain & Behavior. 3: 229-251
Nosofsky RM, Slaughter C, McDaniel MA. (2019) Learning hierarchically organized science categories: simultaneous instruction at the high and subtype levels. Cognitive Research: Principles and Implications. 4: 48
Miyatsu T, Nosofsky RM, McDaniel MA. (2019) Effects of specific-level versus broad-level training for broad-level category learning in a complex natural science domain. Journal of Experimental Psychology. Applied
Le Pelley ME, Newell BR, Nosofsky RM. (2019) Deferred Feedback Does Not Dissociate Implicit and Explicit Category-Learning Systems: Commentary on Smith et al. (2014). Psychological Science. 956797619841264
Nosofsky R, Zaki S. (2019) Math modeling, neuropsychology, and category learning: Trends in Cognitive Sciences. 3: 125-126
Nosofsky RM, McDaniel MA. (2019) Recommendations From Cognitive Psychology for Enhancing the Teaching of Natural-Science Categories Policy Insights From the Behavioral and Brain Sciences. 6: 21-28
Nosofsky RM, Sanders CA, Meagher BJ, et al. (2019) Search for the Missing Dimensions: Building a Feature-Space Representation for a Natural-Science Category Domain Computational Brain & Behavior. 3: 13-33
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