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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

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Published on: March 1, 2022

Renewing the respect for similarity.

Shimon Edelman1, Reza Shahbazi

  • 1Department of Psychology, Cornell University, Ithaca NY, USA.

Frontiers in Computational Neuroscience
|July 20, 2012
PubMed
Summary

This study advocates for a renewed focus on similarity as a core concept in cognitive science and artificial intelligence. It explores similarity-preserving algorithms for associative memory, object recognition, and scene interpretation, bridging computational neuroscience and computer vision.

Keywords:
object recognitionscene interpretationscene spaceshape spacesimilarityview spacevisual structure

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Area of Science:

  • Cognitive Neuroscience
  • Computer Vision
  • Artificial Intelligence

Background:

  • The concept of similarity is fundamental yet debated in psychology, influenced by context and problem framing.
  • Similarity-preserving methods are crucial for cognitive functions like associative lookup and dimensionality reduction.
  • Existing methods face challenges in representing structured objects and scenes effectively.

Purpose of the Study:

  • To re-establish similarity as a key explanatory concept in cognitive modeling and AI.
  • To survey and advance theory and methods in similarity-preserving associative lookup and dimensionality reduction.
  • To introduce novel computational frameworks for object and scene representation.

Main Methods:

  • Review of established and novel algorithms for similarity-preserving associative lookup, including locality-sensitive hashing (LSH).
  • Development and mathematical analysis of the Chorus of Prototypes framework for object representation.
  • Introduction and empirical testing of the Chorus of Relational Descriptors (ChoRD) model for scene representation.

Main Results:

  • Demonstrated mathematical insights into the effectiveness of similarity-based frameworks through their connection to LSH.
  • Presented the Chorus of Prototypes framework with supporting empirical evidence.
  • Introduced the ChoRD model, extending similarity-based approaches to scene interpretation.

Conclusions:

  • Similarity-based methods, particularly those leveraging LSH, offer powerful tools for cognitive modeling and AI.
  • The Chorus of Prototypes and ChoRD models provide robust frameworks for visual object and scene representation.
  • Further research can extend these similarity-based approaches to broader cognitive and computational challenges.