One or two dimensions in spontaneous classification: A simplicity approach

Pothos, Emmanuel M.; Close, James

doi:10.1016/j.cognition.2007.11.007

Cited by 49 publications

(65 citation statements)

References 43 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Posner & Keele, 1968), storing exemplars (e.g. Medin & Schaffer, 1978;Nosofsky, 1984Nosofsky, , 1986Kruschke, 1992), and simplicity-based approaches (Pothos & Chater, 2002;Pothos & Close, 2008), as well as many which interpolate between approaches (e.g. Anderson, 1991;Love et al, 2004).…”

Section: Previous Approaches To Category Learningmentioning

confidence: 99%

“…These approaches differ in many ways, including whether they focus on supervised or unsupervised learning (see Love, 2002; T. L. Griffiths, Canini, Sanborn, & Navarro, 2007). However, these models are similar in that they focus on learning a single system of categories (but see Pothos & Close, 2008), limiting their applicability to modeling learning in real world domains.…”

Section: Previous Approaches To Category Learningmentioning

confidence: 99%

“…By combining these terms, we arrive at a model that attempts to find the simplest solution that adequately accounts for the data. This approach has strong relations to simplicity-based approaches (Pothos & Chater, 2002;Pothos & Close, 2008), as well as prototype and exemplar models (see Nosofsky, 1991, for a detailed discussion). Figure 2 shows how the tradeoff between these factors leads the model to group animals in Figure 1 into a small number of familiar large-scale taxonomic categories: mammals, birds, reptiles and amphibians, and insects and invertebrates.…”

Section: The Single System Modelmentioning

confidence: 99%

“…Our approach is also related to simplicity-based approaches (Pothos & Chater, 2002;Pothos & Close, 2008). The simplicity-based approach attempts to learn the system of categories that (almost) allows within-category similarity to be greater than between category similarity for all pairs of objects.…”

Section: Implications For Models Of Category Learningmentioning

confidence: 99%

“…Indeed, CrossCat can account for many of the results presented by Pothos & Close (2008). In one experiment, the researchers presented participants with a category learning task in which participants observed artificial stimuli that varied on two continuous dimensions.…”

Section: Implications For Models Of Category Learningmentioning

confidence: 99%

See 4 more Smart Citations

A probabilistic model of cross-categorization

et al. 2011

View full text Add to dashboard Cite

Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that crosscategorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models' predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities.People explain different aspects of everyday objects in different ways. For example, steak is high in iron because it is a meat; however, it is often served with wine because it is a dinner food. The different ways of thinking about steak underscore different ways of thinking about the domain of foods: as a system of taxonomic categories including meats and vegetables, or as a system of

show abstract

Section: Previous Approaches To Category Learningmentioning

confidence: 99%

Section: Previous Approaches To Category Learningmentioning

confidence: 99%

Section: The Single System Modelmentioning

confidence: 99%

Section: Implications For Models Of Category Learningmentioning

confidence: 99%

Section: Implications For Models Of Category Learningmentioning

confidence: 99%

See 3 more Smart Citations

A probabilistic model of cross-categorization

et al. 2011

View full text Add to dashboard Cite

show abstract

A Comparison of the neural correlates that underlie rule‐based and information‐integration category learning

Carpenter

Wills

Benattayallah

et al. 2016

Human Brain Mapping

View full text Add to dashboard Cite

The influential competition between verbal and implicit systems (COVIS) model proposes that category learning is driven by two competing neural systems-an explicit, verbal, system, and a procedural-based, implicit, system. In the current fMRI study, participants learned either a conjunctive, rule-based (RB), category structure that is believed to engage the explicit system, or an information-integration category structure that is thought to preferentially recruit the implicit system. The RB and information-integration category structures were matched for participant error rate, the number of relevant stimulus dimensions, and category separation. Under these conditions, considerable overlap in brain activation, including the prefrontal cortex, basal ganglia, and the hippocampus, was found between the RB and information-integration category structures. Contrary to the predictions of COVIS, the medial temporal lobes and in particular the hippocampus, key regions for explicit memory, were found to be more active in the information-integration condition than in the RB condition. No regions were more activated in RB than information-integration category learning. The implications of these results for theories of category learning are discussed. Hum Brain Mapp 37:3557-3574, 2016. © 2016 Wiley Periodicals, Inc.

show abstract

Summary Measurement: The Role of Categories or Domains

Barofsky¹

2011

Quality

View full text Add to dashboard Cite

One or two dimensions in spontaneous classification: A simplicity approach

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link

Cited by 49 publications

References 43 publications

A probabilistic model of cross-categorization

A probabilistic model of cross-categorization

A Comparison of the neural correlates that underlie rule‐based and information‐integration category learning

Summary Measurement: The Role of Categories or Domains

Contact Info

Product

Resources

About