Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Representation is representation of similarities.

S Edelman1

  • 1Department of Brain and Cognitive Sciences, Massachussetts Institute of Technology, Cambridge 02142, USA. edelman@ai.mit.edu

The Behavioral and Brain Sciences
|March 31, 1999
PubMed
Summary

This study introduces a novel approach to visual representation, using reference shapes to create internal models of the world. This method enables accurate shape perception and categorization, overcoming limitations of existing theories.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Challenges faced by physicians when discussing the Type 2 diabetes diagnosis with patients: insights from a cross-national study (IntroDia<sup>®</sup> ).

Diabetic medicine : a journal of the British Diabetic Association·2017
Same author

Oxytocin receptor and vasopressin receptor 1a genes are respectively associated with emotional and cognitive empathy.

Hormones and behavior·2014
Same author

Control of type 2 diabetes after 1 year of laparoscopic adjustable gastric banding in the helping evaluate reduction in obesity (HERO) study.

Diabetes, obesity & metabolism·2014
Same author

Frailty and osteoporosis in older women--a prospective study.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·2013
Same author

Competitive learning in biological and artificial neural computation.

Trends in cognitive sciences·2011
Same author

Computational theories of object recognition.

Trends in cognitive sciences·2011

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Computer Vision

Background:

  • Advanced perceptual systems require a veridical relationship between external stimuli and internal representations.
  • Existing theories often struggle with categorization and instance identification, particularly for novel shapes.
  • Part-based decomposition presents computational challenges in shape representation.

Purpose of the Study:

  • To propose a unified theory for visual representation supporting superordinate, basic-level, and instance-level categorization.
  • To establish a principled and veridical relationship between the world and its internal representation.
  • To offer a computationally efficient alternative to part-based decomposition for shape processing.

Main Methods:

  • Representing internal shapes via responses from a small set of tuned modules measuring similarity to reference shapes.

Related Experiment Videos

  • Embedding stimuli into a low-dimensional proximal shape space defined by module outputs.
  • Deriving a general expression for stimulus similarity based on reference shape comparisons.

    • Main Results:

    • The proposed model achieves veridical representations of distal shape similarities (second-order isomorphisms).
    • Shape processing, including discrimination of dissimilar shapes, is supported without explicit part decomposition.
    • The similarity measure can derive various models of perceived similarity, from hierarchical to discrete.

    Conclusions:

    • A unified, module-based approach offers a principled and veridical method for visual representation.
    • This framework overcomes computational limitations of part-based theories and supports diverse similarity models.
    • The theory provides a robust foundation for understanding advanced perceptual systems and shape recognition.