Book contents
- Frontmatter
- Contents
- List of figures
- List of tables
- List of contributors
- 1 Introduction
- 2 The generalized context model: an exemplar model of classification
- 3 Prototype models of categorization: basic formulation, predictions, and limitations
- 4 COVIS
- 5 Semantics without categorization
- 6 Models of attentional learning
- 7 An elemental model of associative learning and memory
- 8 Nonparametric Bayesian models of categorization
- 9 The simplicity model of unsupervised categorization
- 10 Adaptive clustering models of categorization
- 11 Cobweb Models of Categorization and Probabilistic Concept Formation
- 12 The knowledge and resonance (KRES) model of category learning
- 13 The contribution (and drawbacks) of models to the study of concepts
- 14 Formal models of categorization: insights from cognitive neuroscience
- 15 Comments on models and categorization theories: the razor's edge
- Index
- References
3 - Prototype models of categorization: basic formulation, predictions, and limitations
Published online by Cambridge University Press: 05 June 2012
Book contents
- Frontmatter
- Contents
- List of figures
- List of tables
- List of contributors
- 1 Introduction
- 2 The generalized context model: an exemplar model of classification
- 3 Prototype models of categorization: basic formulation, predictions, and limitations
- 4 COVIS
- 5 Semantics without categorization
- 6 Models of attentional learning
- 7 An elemental model of associative learning and memory
- 8 Nonparametric Bayesian models of categorization
- 9 The simplicity model of unsupervised categorization
- 10 Adaptive clustering models of categorization
- 11 Cobweb Models of Categorization and Probabilistic Concept Formation
- 12 The knowledge and resonance (KRES) model of category learning
- 13 The contribution (and drawbacks) of models to the study of concepts
- 14 Formal models of categorization: insights from cognitive neuroscience
- 15 Comments on models and categorization theories: the razor's edge
- Index
- References
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.
- Type
- Chapter
- Information
- Formal Approaches in Categorization , pp. 40 - 64Publisher: Cambridge University PressPrint publication year: 2011
References
, & (2005). Human category learning. Annual Review of Psychology, 56, 149–178.CrossRefGoogle ScholarPubMed
, & (2001). Expanding the search for a linear separability constraint on category learning. Memory & Cognition, 29, 1153–1164.CrossRefGoogle ScholarPubMed
, (2004). As easy to memorize as they are to classify: the 5-4 categories and the category advantage. Memory & Cognition, 31, 1293–1301.CrossRefGoogle Scholar
, & (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.Google ScholarPubMed
, , , & (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.CrossRefGoogle Scholar
, & (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.Google Scholar
, & (1993). The learning of categories: parallel brain systems for item memory and category knowledge. Science, 262, 1747–1749.CrossRefGoogle ScholarPubMed
, & (2007). Models in search of a brain. Cognitive, Affective, & Behavioral Neuroscience, 7, 90–108.CrossRefGoogle Scholar
, , & (2004). SUSTAIN: a network model of category learning. Psychological Review, 111, 309–332.CrossRefGoogle ScholarPubMed
, & (1978). Context theory of classification learning. Psychological Review, 85, 207–238.CrossRefGoogle Scholar
, & (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7, 355–368.Google Scholar
, & (2004). Learning categories by making predictions: an investigation of indirect category learning. Memory & Cognition, 32, 1355–1368.CrossRefGoogle ScholarPubMed
, & (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.Google ScholarPubMed
, & , (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.Google ScholarPubMed
(2000). The importance of complexity in model selection. Journal of Mathematical Psychology, 44, 190–204.CrossRefGoogle ScholarPubMed
(1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.CrossRefGoogle ScholarPubMed
(1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87–108.Google ScholarPubMed
(1988). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700–708.Google Scholar
(1992). Exemplars, prototypes, and similarity rules. In , , & (eds.), From Learning Theory to Connectionist Theory: Essays in Honor of William K. Estes (Vol. 1, pp. 149–167). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
, & (2002). Exemplar and prototype models revisited: response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 924–940.Google ScholarPubMed
, & (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.CrossRefGoogle ScholarPubMed
, & (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.CrossRefGoogle Scholar
, , & (1998a). Contrasting cortical activity associated with category memory and recognition memory. Learning & Memory, 5, 420–428.Google ScholarPubMed
, , & , (1998b). Cortical areas supporting category learning identified using functional MRI. Proceedings of the National Academy of Sciences, 95, 747– 750.CrossRefGoogle ScholarPubMed
, & (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759–784.CrossRefGoogle ScholarPubMed
, & (1975). Family resemblances: studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.CrossRefGoogle Scholar
, , , , & (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.CrossRefGoogle Scholar
(1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.CrossRefGoogle Scholar
(2005). Wanted: a new psychology of exemplars. Canadian Journal of Experimental Psychology, 2003 Festschrift for Lee R. Brooks, 59, 47–53.Google ScholarPubMed
, & (1998). Prototypes in the mist: the early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411–1436.Google Scholar
, (2000). Thirty categorization results in search of a model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 3–27.Google ScholarPubMed
, (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.Google Scholar
, , , & (1997). Straight talk about linear separability. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 659–680.Google Scholar
, , & (2008). Prototype abstraction by monkeys (Macaca mulatta). Journal of Experimental Psychology: General, 137, 390–401.CrossRefGoogle Scholar
, & (1998). Category learning by inference and classification. Journal of Memory & Language, 39, 124–148.CrossRefGoogle Scholar
, , & (2008). Dissociable prototype learning systems: evidence from brain imaging and behavior. Journal of Neuroscience, 28, 13194–13201.CrossRefGoogle ScholarPubMed
- 17
- Cited by