Learning abstract relations from using categories

Memory and Cognition

Volumn 30, Issue 5, 2002, Pages 657-665

  Ross, Brian H ^a   Warren, Justin L ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COGNITION; HUMAN; INTELLIGENCE; LEARNING; PERCEPTION;

EID: 0036637922     PISSN: 0090502X     EISSN: None     Source Type: Journal    
DOI: 10.3758/BF03196422     Document Type: Article

References (32)

1. Perceptual symbol systems
2. Categorization and representation of physics problems by experts and novices
3. Knowledge and concept learning
4. ALCOVE: An exemplar-based connectionist model of category learning
5. Flexible tuning of similarity in exemplar-based categorization
6. Correlated properties in natural categories
7. Relationships between item and category learning: Evidence that abstraction is not automatic
8. Context theory of classification learning
9. Concepts and concept formation
10. The locus of knowledge effects in concept learning
11. The role of theories in conceptual coherence
12. Predictions from uncertain categorizations
13. Rule-plus-exception model of classification learning
14. Pattern recognition and categorization
15. Abstract coherent categories
16. Principles of categorization
17. Category representations and the effects of interacting with instances
18. The use of categories affects classification
19. Postclassification category use: The effects of learning to use categories after learning to classify
20. The effects of category use on learned categories
21. Concepts and categories
22. The development of features in object concepts
23. Categorization creates functional features
24. Concepts do more than categorize
25. What is learned in knowledge-related categories? Evidence from typicality and feature frequency judgments
26. Comparison-based learning: Effects of comparing instances during category learning
27. On the interaction of theory and data in concept learning
28. Category-learning by inference and classification
29. Inference using categories
30. Learning categories composed of varying instances: The effect of classification, inference, and structural alignment