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
12 - The knowledge and resonance (KRES) model of category learning
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
As this volume reflects, the dozens of models proposed to account for supervised category learning and classification over the last several decades differ from one another in multiple ways. One dimension of variation concerns activation dynamics. Under many sorts of theories, a stimulus is perceived, is compared to category representations, and is then (perhaps probabilistically) assigned to a category (Gluck & Bower, 1988; Kruschke, 1992; Love, Medin, & Gureckis, 2004; Medin & Schaffer, 1978). Much of the research in this framework explores the nature of the similarity processes that are used to compare stimuli to category representations.
Other lines of research have hinted that this sort of unidirectional flow of information from stimulus to category label may be inadequate to explain a number of phenomena. For example, Wisniewski and Medin (1994) found evidence for reinterpretation of ambiguous visual features based on prior conceptual knowledge. Thus, an alternative class of models views categorization as a constraint-satisfaction process, where representations of the stimulus and category representations interact to form an internally consistent representation (Goldstone & Medin, 1994; Rehder & Murphy, 2003). An assumption of interactivity is consistent with the many psychological systems that incorporate complex perceptual constraints at the earliest stages of processing (e.g., Balcetis & Dale, 2007; Friston, 2003; Spratling & Johnson, 2006; Tanenhaus et al., 1995).
A second dimension of variation concerns the nature of category representations.
Information
- Type
- Chapter
- Information
- Formal Approaches in Categorization , pp. 274 - 298Publisher: Cambridge University PressPrint publication year: 2011
References
(1991). Is human cognition adaptive?Behavioral and Brain Sciences, 14, 471–517.CrossRefGoogle Scholar
, & (2007). Conceptual set as a top-down constraint on visual object identification. Perception, 36, 581–595.CrossRefGoogle ScholarPubMed
, , & (1993). Incorporating prior biases in network models of conceptual rule learning. Memory & Cognition, 21, 413–423.CrossRefGoogle ScholarPubMed
, , & (2005). Inference and classification learning of abstract coherent categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 86–99.Google ScholarPubMed
, & (1988). From conditioning to category learning: an adaptive network model. Journal of Experimental Psychology: General, 117, 227–247.CrossRefGoogle Scholar
, & (1994). Similarity, interactive activation, and mapping: an overview. In & (eds.), Advances in Connectionist and Neural Computation Theory, Vol. 2: Analogical Connections (pp. 321–362). New York: Ablex.Google Scholar
(1979). Polymorphous concepts in semantic memory. Journal of Verbal Learning and Verbal Behavior, 18, 441–461.CrossRefGoogle Scholar
(1995). Testing the prototype theory of concepts. Journal of Memory and Language, 34, 686–708.CrossRefGoogle Scholar
(in preparation). Uneven weights in category learning revealed by tests of partial items.
, & (2006). Modeling category learning with exemplars and prior knowledge. In (ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 1440–1445). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
(1994). Models of the effects of prior knowledge on category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 1264–1282.Google ScholarPubMed
, & (2000). Knowledge selection in category learning. In (ed.), Psychology of Learning and Motivation (Vol. 39, pp. 163–199). San Diego, CA: Academic Press.Google Scholar
, , & (2008). Prior knowledge enhances the category dimensionality effect. Memory & Cognition, 36, 256–270.CrossRefGoogle ScholarPubMed
, & (2006). Category dimensionality and feature knowledge: when more features are learned as easily as fewer. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 301–315.Google ScholarPubMed
(1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 81, 3088–3092.CrossRefGoogle Scholar
(1984). Neurons with graded responses have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences, 81, 3088–3092.CrossRefGoogle Scholar
, & (in press). How prior knowledge affects selective attention during category learning: an eyetracking study. Memory and Cognition.
(1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.CrossRefGoogle ScholarPubMed
, , & (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
, , & (1987). Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology, 19, 242–279.CrossRefGoogle ScholarPubMed
(1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.CrossRefGoogle ScholarPubMed
(1996). Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8, 895–938.CrossRefGoogle Scholar
, , , & (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57–83.CrossRefGoogle ScholarPubMed
, & (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.CrossRefGoogle Scholar
, & (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759–784.CrossRefGoogle ScholarPubMed
, & (2001). Abstract coherent categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1261–1275.Google ScholarPubMed
, , & (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75.CrossRefGoogle Scholar
(2002). Forgetting curves: implications for connectionist models. Cognitive Psychology, 45, 95–152.CrossRefGoogle ScholarPubMed
, & (1998). Prototypes in the mist: the early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 1411–1436.Google Scholar
(1986). Information processing in dynamical systems: foundations of harmony theory. In , , & the PDP Research Group (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1, pp. 194–281). Cambridge, MA: MIT Press.Google Scholar
, & (1996). Effects of background knowledge on category construction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 525–538.Google Scholar
, & (2006). A feedback model of perceptual learning and categorization. Visual Cognition, 13, 129–165.CrossRefGoogle Scholar
, , , & (1995). Integration of visual and linguistic information in spoken language comprehension. Science, 268, 1632–1634.CrossRefGoogle ScholarPubMed
, , , & (1986). Linear separability and concept learning: context, relational properties, and concept naturalness. Cognitive Psychology, 18, 158–194.CrossRefGoogle ScholarPubMed
, & (1994). On the interaction of theory and data in concept learning. Cognitive Science, 18, 221–281.CrossRefGoogle Scholar
, & (1998). Category learning by inference and classification. Journal of Memory and Language, 39, 124–148.CrossRefGoogle Scholar
Accessibility standard: Unknown
Accessibility compliance for the PDF of this book is currently unknown
and may be updated in the future.