Dataset Information

A category-overshadowing effect in pigeons: support for the Common Elements Model of object categorization learning.

ABSTRACT: A model proposing error-driven learning of associations between representations of stimulus properties and responses can account for many findings in the literature on object categorization by nonhuman animals. Furthermore, the model generates predictions that have been confirmed in both pigeons and people, suggesting that these learning processes are widespread across distantly related species. The present work reports evidence of a category-overshadowing effect in pigeons' categorization of natural objects, a novel behavioral phenomenon predicted by the model. Object categorization learning was impaired when a second category of objects provided redundant information about correct responses. The same impairment was not observed when single objects provided redundant information, but the category to which they belonged was uninformative, suggesting that this effect is different from simple overshadowing, arising from competition among stimulus categories rather than individual stimuli during learning.

SUBMITTER: Soto FA

PROVIDER: S-EPMC3513276 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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A category-overshadowing effect in pigeons: support for the Common Elements Model of object categorization learning.

Soto Fabian A FA Wasserman Edward A EA

Journal of experimental psychology. Animal behavior processes 20120701 3

A model proposing error-driven learning of associations between representations of stimulus properties and responses can account for many findings in the literature on object categorization by nonhuman animals. Furthermore, the model generates predictions that have been confirmed in both pigeons and people, suggesting that these learning processes are widespread across distantly related species. The present work reports evidence of a category-overshadowing effect in pigeons' categorization of na ...[more]

PMID: 22823423

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Project description:Relational categories are structure-based categories, defined not only by their internal properties but also by their extrinsic relations with other categories. For example, predator could not be defined without referring to hunt and prey. Even though they are commonly used, there are few models taking into account any relational information. A category learning and categorization model aiming to fill this gap is presented. Previous research addresses the hypothesis that the acquisition and the use of relational categories are underlined by structural alignment. That is why the proposed RoleMap model is based on mechanisms often studied as the analogy-making sub-processes, developed on a suitable for this cognitive architecture. RoleMap is conceived in such a way that relation-based category learning and categorization emerge while other tasks are performed. The assumption it steps on is that people constantly make structural alignments between what they experience and what they know. During these alignments various mappings and anticipations emerge. The mappings capture commonalities between the target (the representation of the current situation) and the memory, while the anticipations try to fill the missing information in the target, based on the conceptual system. Because some of the mappings are highly important, they are transformed into a distributed representation of a new concept for further use, which denotes the category learning. When some knowledge is missing in the target, meaning it is uncategorized, that knowledge is transferred from memory in the form of anticipations. The wining anticipation is transformed into a category member, denoting the act of categorization. The model's behavior emerges from the competition between these two pressures - to categorize and to create new categories. Several groups of simulations demonstrate that the model can deal with relational categories in a context-dependent manner and to account for single-shot learning, challenging most of the existing approaches to category learning. The model also simulates previous empirical data pointing to the thematic categories and to the puzzling inverse base-rate effect. Finally, the model's strengths and limitations are evaluated.

Dataset Information

A category-overshadowing effect in pigeons: support for the Common Elements Model of object categorization learning.

Publications

A category-overshadowing effect in pigeons: support for the Common Elements Model of object categorization learning.

Similar Datasets

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