Abstract
A study of the combined influence of prior knowledge and stimulus dimensionality on category learning was conducted. Subjects learned category structures with the same number of necessary dimensions but with more or fewer additional, redundant dimensions and with either knowledge-related or knowledge-unrelated features. Minimal-learning models predict that all subjects, regardless of condition, either should learn the same number of dimensions or should respond more slowly to each dimension. Despite similar learning rates and response times, subjects learned more features in the high-dimensional than in the low-dimensional condition. Furthermore, prior knowledge interacted with dimensionality, increasing what was learned, especially in the high-dimensional case. A second experiment confirmed that the participants did, in fact, learn more features during the training phase, rather than simply inferring them at test. These effects can be explained by direct associations among features (representing prior knowledge), combined with feedback between features and the category label, as was shown by simulations of the knowledge resonance, or KRES, model of category learning.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
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.
Bloom, P. (2000). How children learn the meanings of words. Cambridge, MA: MIT Press.
Bott, L., Hoffman, A. B., & Murphy, G. L. (2007). Blocking in category learning. Journal of Experimental Psychology: General, 136, 685–699.
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.
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.
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.
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.
Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140.
Erickson, M. A., & Kruschke, J. K. (2002). Rule-based extrapolation in perceptual categorization. Psychonomic Bulletin & Review, 9, 160–168.
Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General, 117, 227–247.
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.
Hertz, J., Krogh, A., & Palmer, R. G. (1991). Introduction to the theory of neural computation. Redwood City, CA: Addison-Wesley.
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.
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.
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.
Kaplan, A. S., & Murphy, G. L. (2000). Category learning with minimal prior knowledge. Journal of Experimental Psychology: Learning, Memory, & Cognition, 26, 829–846.
Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.
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.
Kruschke, J. K., & Johansen, M. K. (1999). A model of probabilistic category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 25, 1083–1119.
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309–332.
Markman, A. B., & Ross, B. H. (2003). Category use and category learning. Psychological Bulletin, 129, 592–613.
Massaro, D. W., & Friedman, D. (1990). Models of integration given multiple sources of information. Psychological Review, 97, 225–252.
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.
Movellan, J. R., & McClelland, J. L. (2001). The Morton-Massaro law of information integration: Implications for models of perception. Psychological Review, 108, 113–148.
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.
Murphy, G. L. (2002). Anti-summary and conclusions. In The big book of concepts (pp. 477–498). Cambridge, MA: MIT Press.
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.
Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994). Ruleplus-exception model of classification learning. Psychological Review, 101, 53–79.
Oden, G. C., & Massaro, D. W. (1978). Integration of featural information in speech perception. Psychological Review, 85, 172–191.
O’Reilly, R. C. (1996). Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm. Neural Computation, 8, 895–938.
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.
Pearce, J. M., & Bouton, M. E. (2001). Theories of associative learning in animals. Annual Review of Psychology, 52, 111–139.
Pitt, M. A., Kim, W., Navarro, D., & Myung, J. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57–83.
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.
Rehder, B., & Murphy, G. L. (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759–784.
Rehder, B., & Ross, B. H., (2001). Abstract coherent categories. Journal of Experimental Psychology: Learning, Memory, & Cognition, 27, 1261–1275.
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonrein-forcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II (pp. 64–99). New York: Appleton-Century-Crofts.
Smith, E. E., Adams, N., & Schorr, D. (1978). Fact retrieval and the paradox of interference. Cognitive Psychology, 10, 438–464.
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by NIMH Grant MH41704.
Rights and permissions
About this article
Cite this article
Hoffman, A.B., Harris, H.D. & Murphy, G.L. Prior knowledge enhances the category dimensionality effect. Memory & Cognition 36, 256–270 (2008). https://doi.org/10.3758/MC.36.2.256
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.3758/MC.36.2.256