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How Distractor Objects Trigger Referential Overspecification: Testing the Effects of Visual Clutter and Distractor

Ruud Koolen1, Emiel Krahmer2, Marc Swerts2

  • 1Tilburg Center for Cognition and Communication (TiCC), School of Humanities, Tilburg University. r.m.f.koolen@uvt.nl.

Cognitive Science
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PubMed
Summary
This summary is machine-generated.

Speakers add redundant color information to object descriptions when visual clutter or specific distractors are present. This impacts human-like AI language generation.

Keywords:
Computational modelsDefinite referenceDistractor distanceOverspecificationVisual clutter

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Area of Science:

  • Psycholinguistics
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Understanding how humans describe objects is crucial for developing natural language generation (NLG) systems.
  • Visual saliency cues influence human referential communication, but their precise role in overspecification is not fully understood.

Purpose of the Study:

  • To investigate how visual saliency cues, such as clutter and distractors, affect the use of redundant color attributes in object descriptions.
  • To evaluate the implications of these findings for algorithms generating human-like object descriptions.

Main Methods:

  • Two experiments were conducted using realistic visual scenes.
  • Participants' object descriptions were analyzed for the redundant use of color attributes in the presence of varying visual clutter and distractor types/colors.

Main Results:

  • Speakers were more likely to redundantly mention color in visually cluttered scenes.
  • Redundant color use was highest when a distractor object shared the same type but differed in color from the target referent.
  • Distractor distance did not reliably affect redundant color use.

Conclusions:

  • Visual saliency cues guide speakers in identifying relevant distractors, leading to overspecification.
  • Current NLG algorithms, like the Incremental Algorithm, may fail to replicate human-like descriptions due to their focus on distinguishing properties rather than accounting for saliency-driven overspecification.