Feature- and rule-based generalization in human associative learning.

Shanks, David R.; Darby, R. J.

doi:10.1037/0097-7403.24.4.405

Cited by 137 publications

(233 citation statements)

References 43 publications

(73 reference statements)

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“…Fragments can be thought of as object features. Thus, for example, when a new object is recognized as familiar because it is made of many familiar fragments, then this is a process of Similarity, consistently with the associative learning literature (e.g., Shanks & Darby 1998;cf. Tversky 1977).…”

Section: Similarity As Associative Knowledgementioning

confidence: 53%

“…Hence, ArO, BrO, ABrO is Similarity by feature overlap; but in ArO, BrO, ABrno O, the property "jointly predictive symptoms" overrides the salience of all the other relevant properties, therefore this is a Rule. In a sense, although the overall conclusion is not different from that of Shanks and Darby (1998), the present proposal allows us to provide a specific conception of what Rules and Similarity are and how they relate to each other.Thus, associative learning can lead equally to Rules and Similarity. …”

mentioning

confidence: 97%

“…If both perceptual and abstract properties can be recognized, then presumably both kinds of properties can be concurrently involved in learning processes (cf. negation or omission in associative learning; Shanks & Darby 1998;Skinner 1936), and hence perceptual and abstract knowledge can be developed in analogous ways. The fact that perceptual and abstract properties can be perceived and involved in learning processes in analogous ways is one of the central assumptions in this work.…”

Section: Rules As Abstractionmentioning

confidence: 99%

“…As an example, consider Shanks and Darby (1998), who trained participants to associate individual foods (A, B) with an allergy outcome (O), but not the combination of foods (ArO, BrO, ABrno O). Some participants generalized in a way consistent with their training, but others would associate individual foods plus their combination with the outcome (ArO, BrO, ABrO).…”

Section: Similarity As Associative Knowledgementioning

confidence: 99%

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Two types of thought: Evidence from aphasia

Davidoff

2005

Behav Brain Sci

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Abstract:The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be described on the basis of separate models. In the present article, I assume that the rules versus similarity distinction can be understood in the same way in learning, reasoning, categorization, and language, and that a unified model for rules and similarity is appropriate. A rules process is considered to be a similarity one where only a single or a small subset of an object's properties are involved. Hence, rules and overall similarity operations are extremes in a single continuum of similarity operations. It is argued that this viewpoint allows adequate coverage of theory and empirical findings in learning, reasoning, categorization, and language, and also a reassessment of the objectives in research on rules versus similarity.Keywords: categorization; cognitive explanation; language; learning; reasoning; rules; similarity Emmanuel Pothos received his D.Phil. in Experimental Psychology from the University of Oxford in 1998. Since then he has been a lecturer of psychology at the University of Wales at Bangor and at the University of Edinburgh; he is currently at the University of Crete. His research activity includes computational models in unsupervised categorization and statistical methods for disambiguating rules and similarity in learning. Additionally, he is looking at applications of learning models in the study of addictive behavior. A focal point of his work is the a priori comparability of theoretical accounts and explanatory concepts in cognitive psychology.partly because they uniquely have in common a particular set of properties or features, as research in basic level categorization and spontaneous classification shows (Pothos & Chater 2002;Rosch & Mervis 1975). I postulate that the object properties relevant in deciding how to categorize it amongst a number of candidate categories are the properties uniquely common to the instances of each category (cf. Aha & Goldstone 1992). For example, in order to decide whether my car keys are a member of the category 'things to take out of my house when it is on fire," I will have to consider only whether car keys match the uniquely common properties of the other members of that category ("credit cards," "my cat," "my university diploma") (Barsalou 1991). Note that the present proposal does not involve any commitment about the form of features or properties (cf. Marr 1982). We simply require that at some level it is possible to represent objects in terms of discrete entities (perceptual features, abstract proper...

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Section: Similarity As Associative Knowledgementioning

confidence: 53%

mentioning

confidence: 97%

Section: Rules As Abstractionmentioning

confidence: 99%

Section: Similarity As Associative Knowledgementioning

confidence: 99%

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Two types of thought: Evidence from aphasia

Davidoff

2005

Behav Brain Sci

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“…It is possible that participants learned that the One label 'goes with' a certain word and also that the One label 'does not go' with certain other words (cf. Shanks & Darby, 1998), creating a potential imbalance in the complexity of the associations between the two labels. Accordingly, with Condition 2 we presented only one instance of each negative association for each of the meaningless labels (Table 2).…”

Section: Discussionmentioning

confidence: 99%

Inducing a Stroop Effect

Pothos

Tapper

2009

Applied Cognitive Psychology

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Axial and Equatorial Hydrogen Bonds: Jet-Cooled Rotational Spectrum of the Pentamethylene Sulfide⋅⋅⋅Hydrogen Fluoride Complex

et al. 2002

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We examine the conditions that lead to Stroop interference for a meaningless linguistic label. Tiffany's cognitive model of drug abuse implies that individuals will respond more slowly to drug‐related words compared to neutral words in an emotional Stroop task, because the former have many automatic associations (e.g. positive expectancies). To examine this proposal, we trained participants to associate a meaningless label with either one other word or several other words and examined the induced Stroop interference for these meaningless labels. In two experiments, and contrary to expectations from Tiffany's work, we observed greatest Stroop interference for the meaningless label with just one association. These results are discussed in terms of associative learning theory. Copyright © 2009 John Wiley & Sons, Ltd.

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Feature- and rule-based generalization in human associative learning.

Cited by 137 publications

References 43 publications

Two types of thought: Evidence from aphasia

Two types of thought: Evidence from aphasia

Inducing a Stroop Effect

Axial and Equatorial Hydrogen Bonds: Jet-Cooled Rotational Spectrum of the Pentamethylene Sulfide⋅⋅⋅Hydrogen Fluoride Complex

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