Prior knowledge enhances the category dimensionality effect

Memory and Cognition

Volumn 36, Issue 2, 2008, Pages 256-270

  Hoffman, Aaron B ^a,b   Harris, Harlan D ^a   Murphy, Gregory L ^a  

^a NEW YORK UNIVERSITY   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CATEGORY DIMENSIONALITY EFFECT; CATEGORY LEARNING; CONCEPT ANALYSIS; CONTROLLED STUDY; FUZZY LOGIC; FUZZY LOGICAL MODEL OF PERCEPTION; HIGH DIMENSIONAL LEARNING; HUMAN; HUMAN EXPERIMENT; INTERMETHOD COMPARISON; KNOWLEDGE MANAGEMENT; KNOWLEDGE MEASUREMENT; KNOWLEDGE RESONANCE MODEL; LEARNING; MINIMAL LEARNING MODEL; NORMAL HUMAN; PERCEPTION LEARNING; PRIOR KNOWLEDGE; REACTION TIME; SIMULATION; STATE DEPENDENT LEARNING; STIMULUS RESPONSE; THEORETICAL MODEL; ASSOCIATION; CONCEPT FORMATION; PROBLEM SOLVING; RECALL; REINFORCEMENT; SEMANTICS;

ASSOCIATION LEARNING; CONCEPT FORMATION; CUES; HUMANS; KNOWLEDGE OF RESULTS (PSYCHOLOGY); MENTAL RECALL; PROBLEM SOLVING; REACTION TIME; SEMANTICS; VERBAL LEARNING;

EID: 44649182361     PISSN: 0090502X     EISSN: None     Source Type: Journal    
DOI: 10.3758/MC.36.2.256     Document Type: Article

References (39)

1. ASHBY, G. F., ALFONSO-REESE, L. A., TURKEN, A. U., & WALDRON, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442-481.
2. BLOOM, P. (2000). How children learn the meanings of words. Cambridge, MA: MIT Press.
3. BOTT, L., HOFFMAN, A. B., & MURPHY, G. L. (2007). Blocking in category learning. Journal of Experimental Psychology: General, 136, 685-699.
4. CAREY, S. (1978). The child as word learner. In M. Halle, J. Bresnan, & G. A. Miller (Eds.), Linguistic theory and psychological reality (pp. 264-293). Cambridge, MA: MIT Press.
5. COHEN, M. M., & MASSARO, D. W. (1992). On the similarity of categorization models. In F. H. Ashby (Ed.), Multidimensional models of perception and cognition (pp. 395-448). Hillsdale, NJ: Erlbaum.
6. EDGELL, S. E., BRIGHT, R. D., NG, P. C., NOONAN, T. K., & FORD, L. A. (1992). The effect of representation of the processing of probabilistic information. In B. Burns (Ed.), Percepts, concepts and categories: The representation and processing of information (pp. 569-601). Amsterdam: Elsevier.
7. EDGELL, S. E., CASTELLAN, N. J., ROE, R. M., BARNES, J. M., NG, P. C., BRIGHT, R. D., & FORD, L. A. (1996). Irrelevant information in probabilistic categorization. Journal of Experimental Psychology: Learning, Memory, & Cognition, 22, 1463-1481.
8. ERICKSON, M. A., & KRUSCHKE, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107-140.
9. ERICKSON, M. A., & KRUSCHKE, J. K. (2002). Rule-based extrapolation in perceptual categorization. Psychonomic Bulletin & Review, 9, 160-168.
10. GLUCK, M. A., & BOWER, G. H. (1988). From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General, 117, 227-247.
11. HEIT, E., & BOTT, L. (2000). Knowledge selection in category learning. In D. L. Medin (Ed.), The psychology of learning and motivation (Vol. 39, pp. 163-199). San Diego: Academic Press.
12. HERTZ, J., KROGH, A., & PALMER, R. G. (1991). Introduction to the theory of neural computation. Redwood City, CA: Addison-Wesley.
13. HOFFMAN, A. B., & MURPHY, G. L. (2006). Category dimensionality and feature knowledge: When more features are learned as easily as fewer. Journal of Experimental Psychology: Learning, Memory, & Cognition, 32, 301-315.
14. HOPFIELD, J. J. (1987). Learning algorithms and probability distributions in feed-forward and feed-back networks. Proceedings of the National Academy of Sciences, 84, 8429-8433.
15. KAMIN, L. J. (1969). Predictability surprise, attention, and conditioning. In B. A. Campbell & R. M. Church (Eds.), Punishment and aversive behavior (pp. 279-296). New York: Appleton-Century-Crofts.
16. KAPLAN, A. S., & MURPHY, G. L. (2000). Category learning with minimal prior knowledge. Journal of Experimental Psychology: Learning, Memory, & Cognition, 26, 829-846.
17. KRUSCHKE, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44.
18. KRUSCHKE, J. K. (1993). Three principles for models of category learning. In G. V. Nakamura, R. Taraban, & D. L. Medin (Eds.), The psychology of learning and motivation: Vol. 29. Categorization by humans and machines (pp. 57-90). San Diego: Academic Press.
19. KRUSCHKE, J. K., & JOHANSEN, M. K. (1999). A model of probabilistic category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 25, 1083-1119.
20. LOVE, B. C., MEDIN, D. L., & GURECKIS, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309-332.
21. MARKMAN, A. B., & ROSS, B. H. (2003). Category use and category learning. Psychological Bulletin, 129, 592-613.
22. MASSARO, D. W., & FRIEDMAN, D. (1990). Models of integration given multiple sources of information. Psychological Review, 97, 225-252.
23. MEDIN, D. L., & ORTONY, A. (1989). Psychological essentialism. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 179-195). Cambridge: Cambridge University Press.
24. MOVELLAN, J. R., & MCCLELLAND, J. L. (2001). The Morton-Massaro law of information integration: Implications for models of perception. Psychological Review, 108, 113-148.
25. MURPHY, G. L. (2001). Fast-mapping children vs. slow-mapping adults: Assumptions about words and concepts in two literatures. Behavioral & Brain Sciences, 24, 1112-1113.
26. MURPHY, G. L. (2002). Anti-summary and conclusions. In The big book of concepts (pp. 477-498). Cambridge, MA: MIT Press.
27. MURPHY, G. L., & ALLOPENNA, P. D. (1994). The locus of knowledge effects in concept learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 20, 904-919.
28. NOSOFSKY, R. M., PALMERI, T. J., & MCKINLEY, S. C (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 53-79.
29. ODEN, G. C., & MASSARO, D. W. (1978). Integration of featural information in speech perception. Psychological Review, 85, 172-191.
30. O'REILLY, R. C. (1996). Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm. Neural Computation, 8, 895-938.
31. PAZZANI, M. J. (1991). Influence of prior knowledge on concept acquisition: Experimental and computational results. Journal of Experimental Psychology: Learning, Memory, & Cognition, 17, 416-432.
32. PEARCE, J. M., & BOUTON, M. E. (2001). Theories of associative learning in animals. Annual Review of Psychology, 52, 111-139.
33. PITT, M. A., KIM, W., NAVARRO, D., & MYUNG, J. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57-83.
34. REDER, L. M., & ROSS, B. H. (1983). Integrating knowledge in different tasks: The role of retrieval strategy on fan effects. Journal of Experimental Psychology: Learning, Memory, & Cognition, 9, 55-72.
35. REHDER, B., & MURPHY, G. L. (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759-784.
36. REHDER, B., & ROSS, B. H., (2001). Abstract coherent categories. Journal of Experimental Psychology: Learning, Memory, & Cognition, 27, 1261-1275.
37. RESCORLA, R. A., & WAGNER, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II (pp. 64-99). New York: Appleton-Century-Crofts.
38. SMITH, E. E., ADAMS, N., & SCHORR, D. (1978). Fact retrieval and the paradox of interference. Cognitive Psychology, 10, 438-464.
39. WIDROW, G., & HOFF, M. (1960). Adaptive switching circuits. In Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record (Vol. 4, pp. 96-104). New York: Institute of Radio Engineers.