Skip to main content

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
Cancel
×
×
Home
  • Home
Chapter
  • Get access
    Buy the print book
    Check if you have access via personal or institutional login
    Log in Register Recommend to librarian
  • Cited by 7
  • Cited by
    Crossref Citations
    Crossref logo
    This (lowercase (translateProductType product.productType)) has been cited by the following publications. This list is generated based on data provided by CrossRef.
    Paul, Marcus Fellner, Marie-Christin Waldhauser, Gerd T. Minda, John Paul Axmacher, Nikolai Suchan, Boris and Wolf, Oliver T. 2018. Stress Elevates Frontal Midline Theta in Feedback-based Category Learning of Exceptions. Journal of Cognitive Neuroscience, Vol. 30, Issue. 6, p. 799.
    Barsalou, Lawrence W. 2017. Compositionality and Concepts in Linguistics and Psychology. Vol. 3, Issue. , p. 9.
    Graulich, N. and Bhattacharyya, G. 2017. Investigating students' similarity judgments in organic chemistry. Chemistry Education Research and Practice, Vol. 18, Issue. 4, p. 774.
    Steegen, Sara Tuerlinckx, Francis and Vanpaemel, Wolf 2017. Using parameter space partitioning to evaluate a model’s qualitative fit. Psychonomic Bulletin & Review, Vol. 24, Issue. 2, p. 617.
    Liu, Zhen Yang, Jun-An Liu, Hui and Wang, Wei 2015. Behavioral Learning for Data Adjacent Graph Construction in Semi-supervised Learning. p. 125.
    Voorspoels, Wouter Storms, Gert and Vanpaemel, Wolf 2013. Idealness and similarity in goal-derived categories: A computational examination. Memory & Cognition, Vol. 41, Issue. 2, p. 312.
    Gibson, Bryan R. Rogers, Timothy T. and Zhu, Xiaojin 2013. Human Semi-Supervised Learning. Topics in Cognitive Science, Vol. 5, Issue. 1, p. 132.
    ×
  • Print publication year: 2011
  • Online publication date: June 2012

3 - Prototype models of categorization: basic formulation, predictions, and limitations

Export citation
Summary

Summary

The prototype model has had a long history in cognitive psychology, and prototype theory posed an early challenge to the classical view of concepts. Prototype models assume that categories are represented by a summary representation of a category (i.e., a prototype) that might represent information about the most common features, the average feature values, or even the ideal features of a category. Prototype models assume that classification decisions are made on the basis of how similar an object is to a category prototype. This chapter presents a formal description of the model, the motivation and theoretical history of the model, as well as several simulations that illustrate the model's properties. In general, the prototype model is well suited to explain the learning of many visual categories (e.g. dot patterns) and categories with a strong family-resemblance structure.

Prototype models of categorization: basic formulation, predictions, and limitations

Categories are fundamental to cognition, and the ability to learn and use categories is present in all humans and animals. An important theoretical account of categorization is the prototype view (Homa & Cultice, 1984; Homa et al., 1973; Minda & Smith, 2001, 2002; Posner & Keele, 1968; J. D. Smith & Minda, 1998, 2000, 2001; J. D. Smith, Redford, & Haas, 2008). The prototype view assumes that a category of things in the world (objects, animals, shapes, etc.) can be represented in the mind by a prototype.

Recommend this book

Email your librarian or administrator to recommend adding this book to your organisation's collection.

Formal Approaches in Categorization
  • Online ISBN: 9780511921322
  • Book DOI: https://doi.org/10.1017/CBO9780511921322
Please enter your name
Please enter a valid email address
Who would you like to send this to *

CAPTCHA *

Skip to the audio challenge
×
REFERENCES
Ashby, F.G., & Maddox, W.T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178.
Blair, M., & Homa, D. (2001). Expanding the search for a linear separability constraint on category learning. Memory & Cognition, 29, 1153–1164.
Blair, M., (2004). As easy to memorize as they are to classify: the 5-4 categories and the category advantage. Memory & Cognition, 31, 1293–1301.
Chin-Parker, S., & Ross, B.H. (2004). Diagnosticity and prototypicality in category learning: a comparison of inference learning and classification learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 216–226.
Homa, D., Cross, J., Cornell, D., & Shwartz, S. (1973). Prototype abstraction and classification of new instances as a function of number of instances defining the prototype. Journal of Experimental Psychology, 101, 116–122.
Homa, D., & Cultice, J.C. (1984). Role of feedback, category size, and stimulus distortion on the acquisition and utilization of ill-defined categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 83–94.
Knowlton, B.J., & Squire, L.R. (1993). The learning of categories: parallel brain systems for item memory and category knowledge. Science, 262, 1747–1749.
Love, B.C., & Gureckis, T.M. (2007). Models in search of a brain. Cognitive, Affective, & Behavioral Neuroscience, 7, 90–108.
Love, B.C., Medin, D.L., & Gureckis, T.M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111, 309–332.
Medin, D.L., & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Medin, D.L., & Schwanenflugel, P.J. (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7, 355–368.
Minda, J.P., & Ross, B.H. (2004). Learning categories by making predictions: an investigation of indirect category learning. Memory & Cognition, 32, 1355–1368.
Minda, J.P., & Smith, J.D. (2001). Prototypes in category learning: the effects of category size, category structure, and stimulus complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 775–799.
Minda, J.P., & Smith, J.D., (2002). Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 275–292.
Myung, I.J. (2000). The importance of complexity in model selection. Journal of Mathematical Psychology, 44, 190–204.
Nosofsky, R.M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.
Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87–108.
Nosofsky, R. M. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700–708.
Nosofsky, R. M. (1992). Exemplars, prototypes, and similarity rules. In Healy, A.F., Kosslyn, S.M., & Shiffrin, R.M. (eds.), From Learning Theory to Connectionist Theory: Essays in Honor of William K. Estes (Vol. 1, pp. 149–167). Hillsdale, NJ: Lawrence Erlbaum.
Nosofsky, R.M., & Zaki, S.R. (2002). Exemplar and prototype models revisited: response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 924–940.
Posner, M.I., & Keele, S.W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.
Pothos, E., & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.
Reber, P., Stark, C., & Squire, L. (1998a). Contrasting cortical activity associated with category memory and recognition memory. Learning & Memory, 5, 420–428.
Reber, P., Stark, C., & Squire, L., (1998b). Cortical areas supporting category learning identified using functional MRI. Proceedings of the National Academy of Sciences, 95, 747– 750.
Rehder, B., & Murphy, G.L. (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759–784.
Rosch, E., & Mervis, C.B. (1975). Family resemblances: studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.
Rosch, E., Mervis, C.B., Gray, W., Johnson, D., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.
Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.
Smith, E.E., & Medin, D.L. (1981). Categories and Concepts. Cambridge, MA: Harvard University Press.
Smith, J.D. (2005). Wanted: a new psychology of exemplars. Canadian Journal of Experimental Psychology, 2003 Festschrift for Lee R. Brooks, 59, 47–53.
Smith, J.D., & Minda, J.P. (1998). Prototypes in the mist: the early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411–1436.
Smith, J. D., (2000). Thirty categorization results in search of a model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 3–27.
Smith, J. D., (2001). Journey to the center of the category: the dissociation in amnesia between categorization and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 4, 501–516.
Smith, J.D., Murray, J., Morgan, J., & Minda, J.P. (1997). Straight talk about linear separability. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 659–680.
Smith, J.D., Redford, J.S., & Haas, S.M. (2008). Prototype abstraction by monkeys (Macaca mulatta). Journal of Experimental Psychology: General, 137, 390–401.
Wittgenstein, L. (1958/2001). Philosophical Investigations. New York: Blackwell.
Yamauchi, T., & Markman, A. B. (1998). Category learning by inference and classification. Journal of Memory & Language, 39, 124–148.
Zeithamova, D., Maddox, W.T., & Schnyer, D.M. (2008). Dissociable prototype learning systems: evidence from brain imaging and behavior. Journal of Neuroscience, 28, 13194–13201.
Cancel
Confirm
×