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Associative judgment and vector space semantics.

Sudeep Bhatia1

  • 1University of Pennsylvania.

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Summary
This summary is machine-generated.

Vector space models quantify semantic relatedness to predict high-level judgments. This research reveals close links between judgment representations and language, formalizing decision-making theories for quantitative predictions.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Decision Making Research

Background:

  • High-level judgment involves complex associative processing.
  • Existing theories of judgment lack quantitative predictive power for natural language tasks.
  • Vector space semantic models offer a novel approach to quantify semantic relationships.

Purpose of the Study:

  • To investigate the role of semantic relatedness in associative processing within high-level judgment.
  • To determine if vector space semantic models can predict responses in judgment tasks.
  • To formalize and rigorously test established decision-making theories using computational methods.

Main Methods:

  • Utilizing vector space semantic models to quantify semantic relatedness.
  • Applying these measures to a range of existing and novel judgment tasks.
  • Comparing model predictions against empirical data.

Main Results:

  • Semantic relatedness, as measured by vector space models, accurately reflects associations in judgment.
  • These models successfully predict responses across diverse judgment tasks.
  • The findings demonstrate a strong correlation between representations in judgment and those in language.

Conclusions:

  • Vector space semantic models provide a robust framework for understanding associative judgment.
  • This approach bridges cognitive science and computational linguistics by linking judgment and language representations.
  • Established decision-making theories can be quantitatively tested and refined using these computational methods.