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Zheng, Rong; Busemeyer, Jerome R.; Nosofsky, Robert M. – Cognitive Science, 2023
Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were…
Descriptors: Classification, Decision Making, Task Analysis, Feedback (Response)
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
Miyatsu, Toshiya; Gouravajhala, Reshma; Nosofsky, Robert M.; McDaniel, Mark A. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
Learning naturalistic categories, which tend to have fuzzy boundaries and vary on many dimensions, can often be harder than learning well defined categories. One method for facilitating the category learning of naturalistic stimuli may be to provide explicit feature descriptions that highlight the characteristic features of each category. Although…
Descriptors: Undergraduate Students, Feedback (Response), Experiments, Generalization
Meagher, Brian J.; Cataldo, Kirstyn; Douglas, Bruce J.; McDaniel, Mark A.; Nosofsky, Robert M. – Journal of Geoscience Education, 2018
A highly controlled laboratory experiment was conducted that suggested computer-based image training of rock classifications can provide a useful supplement to physical rock training. Two groups of participants learned to classify samples of 12 major types of rocks during a training phase. One group was trained using computer images of the rock…
Descriptors: Science Laboratories, Laboratory Experiments, Geology, Educational Technology
Cao, Rui; Nosofsky, Robert M.; Shiffrin, Richard M. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
In short-term-memory (STM)-search tasks, observers judge whether a test probe was present in a short list of study items. Here we investigated the long-term learning mechanisms that lead to the highly efficient STM-search performance observed under conditions of consistent-mapping (CM) training, in which targets and foils never switch roles across…
Descriptors: Short Term Memory, Recall (Psychology), Item Response Theory, Learning Processes
Donkin, Chris; Newell, Ben R.; Kalish, Mike; Dunn, John C.; Nosofsky, Robert M. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
The strength of conclusions about the adoption of different categorization strategies--and their implications for theories about the cognitive and neural bases of category learning--depend heavily on the techniques for identifying strategy use. We examine performance in an often-used "information-integration" category structure and…
Descriptors: Classification, Learning, Learning Strategies, Identification
Nosofsky, Robert M. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
In a highly systematic literature, researchers have investigated the manner in which people make feature inferences in paradigms involving uncertain categorizations (e.g., Griffiths, Hayes, & Newell, 2012; Murphy & Ross, 1994, 2007, 2010a). Although researchers have discussed the implications of the results for models of categorization and…
Descriptors: Models, Classification, Inferences, Cognitive Psychology
Little, Daniel R.; Nosofsky, Robert M.; Donkin, Christopher; Denton, Stephen E. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2013
A classic distinction in perceptual information processing is whether stimuli are composed of separable dimensions, which are highly analyzable, or integral dimensions, which are processed holistically. Previous tests of a set of logical-rule models of classification have shown that separable-dimension stimuli are processed serially if the…
Descriptors: Classification, Stimuli, Reaction Time, Models
Nosofsky, Robert M.; Denton, Stephen E.; Zaki, Safa R.; Murphy-Knudsen, Anne F.; Unverzagt, Frederick W. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2012
Studies of incidental category learning support the hypothesis of an implicit prototype-extraction system that is distinct from explicit memory (Smith, 2008). In those studies, patients with explicit-memory impairments due to damage to the medial-temporal lobe performed normally in implicit categorization tasks (Bozoki, Grossman, & Smith, 2006;…
Descriptors: Alzheimers Disease, Classification, Patients, Short Term Memory
Stanton, Roger D.; Nosofsky, Robert M. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2013
Researchers have proposed that an explicit reasoning system is responsible for learning rule-based category structures and that a separate implicit, procedural-learning system is responsible for learning information-integration category structures. As evidence for this multiple-system hypothesis, researchers report a dissociation based on…
Descriptors: Classification, Psychological Studies, Learning Strategies, Cognitive Processes
Gureckis, Todd M.; James, Thomas W.; Nosofsky, Robert M. – Journal of Cognitive Neuroscience, 2011
Recent fMRI studies have found that distinct neural systems may mediate perceptual category learning under implicit and explicit learning conditions. In these previous studies, however, different stimulus-encoding processes may have been associated with implicit versus explicit learning. The present design was aimed at decoupling the influence of…
Descriptors: Incidental Learning, Learning Processes, Diagnostic Tests, Brain Hemisphere Functions
Little, Daniel R.; Nosofsky, Robert M.; Denton, Stephen E. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2011
A recent resurgence in logical-rule theories of categorization has motivated the development of a class of models that predict not only choice probabilities but also categorization response times (RTs; Fific, Little, & Nosofsky, 2010). The new models combine mental-architecture and random-walk approaches within an integrated framework and…
Descriptors: Classification, Reaction Time, Stimuli, College Students
Nosofsky, Robert M.; Little, Daniel R.; Donkin, Christopher; Fific, Mario – Psychological Review, 2011
Exemplar-similarity models such as the exemplar-based random walk (EBRW) model (Nosofsky & Palmeri, 1997b) were designed to provide a formal account of multidimensional classification choice probabilities and response times (RTs). At the same time, a recurring theme has been to use exemplar models to account for old-new item recognition and to…
Descriptors: Short Term Memory, Classification, Probability, Cognitive Development
Fific, Mario; Little, Daniel R.; Nosofsky, Robert M. – Psychological Review, 2010
We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli…
Descriptors: Visual Stimuli, Models, Classification, Probability
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