David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Behavioral and Brain Sciences 21 (1):1-17 (1998)
According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding.
|Keywords||concept learning conceptual development features perceptual learning stimulus encoding|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Frank Jäkel, Bernhard Schölkopf & Felix A. Wichmann (2009). Does Cognitive Science Need Kernels? Trends in Cognitive Sciences 13 (9):381-388.
Lawrence W. Barsalou, W. Kyle Simmons, Aron K. Barbey & Christine D. Wilson (2003). Grounding Conceptual Knowledge in Modality-Specific Systems. Trends in Cognitive Sciences 7 (2):84-91.
Andy Clark (2006). Material Symbols. Philosophical Psychology 19 (3):291-307.
Fabian A. Soto & F. Gregory Ashby (2015). Categorization Training Increases the Perceptual Separability of Novel Dimensions. Cognition 139:105-129.
Paul T. Sowden & Philippe G. Schyns (2006). Channel Surfing in the Visual Brain. Trends in Cognitive Sciences 10 (12):538-545.
Similar books and articles
Cyril R. Latimer (1998). New Features for Old: Creation or Derivation? Behavioral and Brain Sciences 21 (1):31-32.
Axel Cleeremans (1998). The Other Hard Problem: How to Bridge the Gap Between Subsymbolic and Symbolic Cognition. Behavioral and Brain Sciences 21 (1):22-23.
James Tanaka (1998). Parts, Features, and Expertise. Behavioral and Brain Sciences 21 (1):37-38.
Katja Wiemer-Hastings & Arthur C. Graesser (1998). Who Needs Created Features? Behavioral and Brain Sciences 21 (1):39-39.
Adriaan Tijsseling (1998). Do Features Arise Out of Nothing? Behavioral and Brain Sciences 21 (1):38-39.
Stephen Grossberg (1998). Self-Organizing Features and Categories Through Attentive Resonance. Behavioral and Brain Sciences 21 (1):27-28.
Georg Dorffner (1998). Flexible Features, Connectionism, and Computational Learning Theory. Behavioral and Brain Sciences 21 (1):24-25.
E. Darcy Burgund & Chad J. Marsolek (1998). Fixed Versus Flexible Features in Dissociable Neural Processing Subsystems. Behavioral and Brain Sciences 21 (1):21-22.
Peter F. Dominey (1998). Flexible Categorization Requires the Creation of Relational Features. Behavioral and Brain Sciences 21 (1):23-24.
Philippe G. Schyns, Robert L. Goldstone & Jean-Pierre Thibaut (1998). Ways of Featuring in Object Categorization. Behavioral and Brain Sciences 21 (1):41-54.
Added to index2009-01-28
Total downloads12 ( #205,927 of 1,726,249 )
Recent downloads (6 months)5 ( #147,227 of 1,726,249 )
How can I increase my downloads?