The multifaceted nature of unsupervised category learning

Love, Bradley C.

doi:10.3758/bf03196484

Cited by 31 publications

(33 citation statements)

References 40 publications

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“…In fact, the latter set of principles may play a larger role in determiningcategory learning performance than does the logical structure of categories (cf., Love, 2002Love, , 2003.…”

Section: Discussionmentioning

confidence: 99%

The nonindependence of stimulus properties in human category learning

Love

Markman

2003

Memory & Cognition

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Typically, models of category learning are verified through behavioral experiments with stimuli consisting of putatively independent dimensions such as shape, size, and color. The assumption of independence is critical in both the design of behavioral experiments and the development of models and theories of learning. Using the standard classification learning paradigm and a common stimulus set, the present work demonstrates that the assumption of independence is unwarranted. Systematic relations span stimulus dimensions and govern learning performance. For example, shape is not independent of size and color, because humans quantify size and color over shape when shape is relevant to the categorization. This quantification is reflected in natural language use (e.g., "blue triangle" as opposed to "triangle and blue"). In this example, color and size are predicates and shape is the argument. Across four experiments, the difficulty of mastering a classification rule can be predicted by the number of predicates that must be unbound in order to free rule-relevant stimulus dimensions.

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“…In fact, the latter set of principles may play a larger role in determiningcategory learning performance than does the logical structure of categories (cf., Love, 2002Love, , 2003.…”

Section: Discussionmentioning

confidence: 99%

The nonindependence of stimulus properties in human category learning

Love

Markman

2003

Memory & Cognition

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“…Barsalou, 1985Barsalou, , 1991 and how categories are used (Love, 2003;Markman & Makin,1998;Markman & Ross, in press;Ross, 1996Ross, , 1997. Categories are often organized around these goals and conceptual structures are optimized to serve these goals (Medin et al, 1997a).…”

Section: Multiple Goals and Functionsmentioning

confidence: 99%

SUSTAIN: A Network Model of Category Learning.

Love

Medin²,

Gureckis

2004

Psychological Review

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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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“…Research on action effects has concentrated on welllearned objects and affordances, whereas the current studies show effects from early learning of arbitrary actions. Research results on interaction effects during category learning (Love, 2003;Ross, 1996) may have been due to increased attention to particular features or relations, whereas the current studies suggest that interactions may also lead to specific action components' being incorporated. Actions are associated with an object, and those actions are activated when the object is recognized, perhaps as a result of some preparation to act.…”

Section: Discussionmentioning

confidence: 50%

“…First, how we interact with category members influences what we learn about the category (Love, 2003;Markman & Ross, 2003;Ross, 1996). Category learning research typically focuses on classification-"which category is this item in?…”

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confidence: 99%

Action information from classification learning

Ross

Wang

Kramer

et al. 2007

Psychonomic Bulletin & Review

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Much of our learning comes from interacting with objects. Two experiments investigated whether or not arbitrary actions used during category learning with objects might be incorporated into object representations and influence later recognition judgments. In a virtual-reality chamber, participants used distinct arm movements to make different classification responses. During a recognition test phase, these same objects required arm movements that were consistent or inconsistent with the classification movement. In both experiments, consistent movements were facilitated relative to inconsistent movements, suggesting that arbitrary action information is incorporated into the representations.

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The multifaceted nature of unsupervised category learning

Cited by 31 publications

References 40 publications

The nonindependence of stimulus properties in human category learning

The nonindependence of stimulus properties in human category learning

SUSTAIN: A Network Model of Category Learning.

Action information from classification learning

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