Category Learning in Rhesus Monkeys: A Study of the Shepard, Hovland, and Jenkins (1961) Tasks.

Smith, J. David; Minda, John Paul; Washburn, David A.

doi:10.1037/0096-3445.133.3.398

Cited by 94 publications

(143 citation statements)

References 67 publications

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“…2.2 of Section 2) and predicts the order of learning difficulty experienced by the monkeys in Smith et al (2004). This can be easily confirmed by examining the lexicographical order of the logical manifolds corresponding to the SHJ category types (see Fig.…”

Section: Generalization To Non-human Animalsmentioning

confidence: 64%

“…In addition, we showed how the CIM may be extended in a simple and natural fashion (by adding a dimensional saliency variable) in order to account for the learning difficulty ordering of the SHJ category types as observed in Rhesus monkeys (Smith et al, 2004). This extension is far from arbitrary as it is motivated by the limits of working memory in different types of non-human animals and in small children.…”

Section: Resultsmentioning

confidence: 99%

“…For example, in one recent study by Smith, Minda, and Washburn (2004), four Rhesus monkeys were trained for 36 days to respond to a monitor displaying visual instantiations of the SHJ category types using a joystick with fruit pellets as rewards. Each solved all six SHJ problems in multiple orders.…”

Section: Generalization To Non-human Animalsmentioning

confidence: 99%

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Categorical invariance and structural complexity in human concept learning

Vigo

2009

Journal of Mathematical Psychology

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a b s t r a c tAn alternative account of human concept learning based on an invariance measure of the categorical stimulus is proposed. The categorical invariance model (CIM) characterizes the degree of structural complexity of a Boolean category as a function of its inherent degree of invariance and its cardinality or size. To do this we introduce a mathematical framework based on the notion of a Boolean differential operator on Boolean categories that generates the degrees of invariance (i.e., logical manifold) of the category in respect to its dimensions. Using this framework, we propose that the structural complexity of a Boolean category is indirectly proportional to its degree of categorical invariance and directly proportional to its cardinality or size. Consequently, complexity and invariance notions are formally unified to account for concept learning difficulty. Beyond developing the above unifying mathematical framework, the CIM is significant in that: (1) (2) it is, in general, a good quantitative predictor of the degree of learning difficulty of a large class of categories (in particular, the 41 category types studied by Feldman [Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630-633]); (3) it is, in general, a good quantitative predictor of parity effects for this large class of categories; (4) it does all of the above without free parameters; and (5) it is cognitively plausible (e.g., cognitively tractable).

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Section: Generalization To Non-human Animalsmentioning

confidence: 64%

Section: Resultsmentioning

confidence: 99%

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Categorical invariance and structural complexity in human concept learning

Vigo

2009

Journal of Mathematical Psychology

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“…The number of errors, trials to criterion, and other measures, showed a partial trend in increasing difficulty: I < II < III, IV, V < VI. This trend, which we refer to as the ''Shepard'' trend, has been well replicated (e.g., Feldman, 2000;Love, 2002;Nosofsky, Gluck, Palmeri, & McKinley, 1994;Smith, Minda, & Washburn, 2004), and it poses a puzzle for researchers to solve. Why should the Shepard trend hold?…”

Section: Studies Of Boolean Conceptsmentioning

confidence: 96%

Mental models of Boolean concepts

Goodwin

Johnson‐Laird

2011

Cognitive Psychology

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a b s t r a c tNegation, conjunction, and disjunction are major building blocks in the formation of concepts. This article presents a new model-based theory of these Boolean components. It predicts that individuals simplify the models of instances of concepts. Evidence corroborates the theory and challenges alternative accounts, such as those based on minimal descriptions, algebraic complexity, or structural invariance. A computer program implementing the theory yields more accurate predictions than these rival accounts. Two experiments showed that the numbers of models of a Boolean concept predict the difficulty of formulating a description of it. As mental models may also underlie deductive reasoning, the present theory integrates two hitherto separate areas of investigation.

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“…The triconditional discrimination requires participants to learn that configurations of three co-occurring stimuli predict the outcome. In general, the uniconditional discrimination is easiest to acquire, followed by the biconditional discrimination and the triconditional discrimination is the hardest to acquire (Love, 2002;Nosofsky, 1984;Smith, Minda, & Washburn, 2004).…”

Section: Introductionmentioning

confidence: 99%

Individual difference in configural associative learning.

Byrom¹,

Murphy

2016

Journal of Experimental Psychology: Animal Learning and Cogniti

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Individuals differ in their ability to acquire associations between stimuli and paired outcomes, an ability that has been proposed to be independent of general metrics of intelligence or memory (e.g., Kaufman, DeYoung, Gray, Brown, & Mackintosh, 2009). The nature of these differences may reflect the type of associative structures acquired during learning, for instance configuring stimuli to facilitate flexible learning and memory. We test the hypothesis that individuals differ in configural associative learning as distinct from simpler (elemental) stimulusoutcome learning. In Experiment 1 participants were screened for attentional scope and we found that attentional scope predicted configural associative learning that could not be explained simply in terms of differences in strength of associative learning. In Experiment 2, attentional scope was trained resulting in a shift in participants' ability to learn about subsequent configurations unrelated to the training material. We discuss how the differences between individual learners reflect differences in configuring rather than simply differences in strength or speed of learning.3

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Category Learning in Rhesus Monkeys: A Study of the Shepard, Hovland, and Jenkins (1961) Tasks.

Cited by 94 publications

References 67 publications

Categorical invariance and structural complexity in human concept learning

Categorical invariance and structural complexity in human concept learning

Mental models of Boolean concepts

Individual difference in configural associative learning.

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