Role of feedback, category size, and stimulus distortion on the acquisition and utilization of ill-defined categories.

Homa, Donald; Cultice, Joan C.

doi:10.1037/0278-7393.10.1.83

Cited by 88 publications

(97 citation statements)

References 22 publications

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“…That is, an unsupervised form of category learning seems to be overlaid on the representation that evolved during supervised training. These results, therefore, corroborate findings from the unsupervised category literature that suggest that people make use of inherent category structure without the aid of feedback (Ahn & Medin, 1992;Clapper & Bower, 1994;Gureckis & Love, 2003;Homa & Cultice, 1984;Wills & McLaren, 1998). The novelty in these results is that they demonstrate that the unsupervised learning occurs even after the formal training stage in this paradigm.…”

Section: Discussionsupporting

confidence: 81%

False prototype enhancement effects in dot pattern categorization

Zaki

Nosofsky

2004

Memory & Cognition

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Results from the classic dot pattern distortion paradigm have sometimes yielded prototype enhancement effects that could not be accounted for by exemplar models of categorization. However, in these experiments the status of the prototype was confounded with certain stimulus-specific properties as well as with the frequency of presentation of the prototype during testing. In two mock-subliminal experiments, participants made categorization judgments to patterns that were generated as prototypes, low-level distortions, or high-level distortions. The participants rated the prototypes as being more likely to be members of a category, although no patterns were presented during training, and there was no objective category structure. In two other experiments, greater prototype enhancement effects were observed when the prototype and low-level distortions were presented with greater frequency during transfer. These results suggest that classic prototype enhancement effects may not be due to the abstraction of a prototype at time of original learning, but rather to other factors not formalized in extant models.

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Section: Discussionsupporting

confidence: 81%

False prototype enhancement effects in dot pattern categorization

Zaki

Nosofsky

2004

Memory & Cognition

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“…Ashby, Queller, and Berretty (1999) showed that participants initially opt for unidimensional solutions (ignoring every dimension of variation but one) but can be brought to entertain multidimensional solutions with the aid of supervision. Several other studies also have shown the preference for the use of one dimension (Love, 2002) or of category structures with minor prototype distortions (Homa & Cultice, 1984). As stated previously, most of the evidence supporting these generalizations derives from experiments testing simple visual categories in which the dimensions of variation are readily identifiable to participants.…”

mentioning

confidence: 80%

Supervised and unsupervised learning of multidimensionally varying non-native speech categories

Goudbeek

Cutler

Smits

2008

Speech Communication

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Learning to recognize the contrasts of a language-specific phonemic repertoire can be viewed as forming categories in a multidimensional psychophysical space. Research on the learning of distributionally defined visual categories has shown that categories defined over 1 dimension are easy to learn and that learning multidimensional categories is more difficult but tractable under specific task conditions. In 2 experiments, adult participants learned either a unidimensional or a multidimensional category distinction with or without supervision (feedback) during learning. The unidimensional distinctions were readily learned and supervision proved beneficial, especially in maintaining category learning beyond the learning phase. Learning the multidimensional category distinction proved to be much more difficult and supervision was not nearly as beneficial as with unidimensionally defined categories. Maintaining a learned multidimensional category distinction was only possible when the distributional information that identified the categories remained present throughout the testing phase. We conclude that listeners are sensitive to both trial-by-trial feedback and the distributional information in the stimuli. Even given limited exposure, listeners learned to use 2 relevant dimensions, albeit with considerable difficulty.

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“…Although unsupervised learning has not been as extensively studied as supervised learning, people can learn without external feedback (Homa & Cultice, 1984). One important challenge is to characterize how humans build internal representations in the absence of explicit feedback.…”

Section: Modeling Unsupervised Learningmentioning

confidence: 99%

SUSTAIN: A Network Model of Category Learning.

Love

Medin²,

Gureckis

2004

Psychological Review

696

880

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SUSTAIN (Supervised and Unsupervised STratified Adaptive IncrementalNetwork) is a model of how humans learn categories from examples. SUS-TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. Importantly, SUSTAIN's discovery of category substructure is affected not only by the structure of the world, but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts where identification learning is faster than classification learning.

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Role of feedback, category size, and stimulus distortion on the acquisition and utilization of ill-defined categories.

Cited by 88 publications

References 22 publications

False prototype enhancement effects in dot pattern categorization

False prototype enhancement effects in dot pattern categorization

Supervised and unsupervised learning of multidimensionally varying non-native speech categories

SUSTAIN: A Network Model of Category Learning.

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