Why are some dimensions integral? Testing two hypotheses through causal learning experiments

Compound generalization and dimensional generalization are traditionally studied independently by different groups of researchers, who have proposed separate theories to explain results from each area. A recent extension of Shepard's rational theory of dimensional generalization allows an expla...

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Autores:
Tipo de recurso:
Fecha de publicación:
2015
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/23702
Acceso en línea:
https://doi.org/10.1016/j.cognition.2015.07.001
https://repository.urosario.edu.co/handle/10336/23702
Palabra clave:
Adult
Article
Experimental design
Feedback system
Group dynamics
Human
Human experiment
Instrumental conditioning
Learning
Prediction
Priority journal
Simulation
State dependent learning
Stimulus
Stimulus generalization
Undergraduate student
Young adult
Adolescent
Causality
Concept formation
Female
Learning
Male
Physiology
Psychological model
Adolescent
Causality
Concept formation
Female
Generalization (psychology)
Humans
Learning
Male
Young adult
Causal learning
Configural and elemental processing
Generalization
Rational theory
Separable and integral dimensions
psychological
Models
Rights
License
Abierto (Texto Completo)
Description
Summary:Compound generalization and dimensional generalization are traditionally studied independently by different groups of researchers, who have proposed separate theories to explain results from each area. A recent extension of Shepard's rational theory of dimensional generalization allows an explanation of data from both areas within a single framework. However, the conceptualization of dimensional integrality in this theory (the direction hypothesis) is different from that favored by Shepard in his original theory (the correlation hypothesis). Here, we report two experiments that test differential predictions of these two notions of integrality. Each experiment takes a design from compound generalization and translates it into a design for dimensional generalization by replacing discrete stimulus components with dimensional values. Experiment 1 showed that an effect analogous to summation is found in dimensional generalization with separable dimensions, but the opposite effect is found with integral dimensions. Experiment 2 showed that the analogue of a biconditional discrimination is solved faster when stimuli vary in integral dimensions than when stimuli vary in separable dimensions. These results, which are analogous to more 'non-linear' processing with integral than with separable dimensions, were predicted by the direction hypothesis, but not by the correlation hypothesis. This confirms the assumptions of the unified rational theory of stimulus generalization and reveals interesting links between compound and dimensional generalization phenomena. © 2015 Elsevier B.V.