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Expanding the Computational Capabilities of Guided Search with a Multidimensional Scaling "Plug-In".

Collin Scarince1, Michael C Hout2,3

  • 1Department of Psychology and Sociology, Texas A&M University-Corpus Christi, USA.

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This study introduces a new computational method combining multidimensional scaling (MDS) with Guided Search (GS) models. This approach enhances visual search research for complex objects, showing search is faster with dissimilar distractors.

Keywords:
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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Computer Vision

Background:

  • Guided Search (GS) models visual search using simple stimuli (lines, colors).
  • Quantifying visual qualities of complex real-world objects for GS is challenging.
  • Existing GS models struggle with the complexity of naturalistic visual stimuli.

Purpose of the Study:

  • To propose and test a modified GS model incorporating multidimensional scaling (MDS).
  • To enable computational analysis of visual search with complex stimuli.
  • To investigate how stimulus similarity affects search efficiency.

Main Methods:

  • Applied multidimensional scaling (MDS) to analyze stimulus similarity.
  • Integrated MDS-derived similarity data into a modified Guided Search (GS) model.
  • Conducted two experiments with participants searching for novel objects among varied distractors.

Main Results:

  • Search efficiency increased when distractors were dissimilar to the target.
  • The modified GS model accurately predicted target-present search response trends.
  • MDS effectively quantified stimulus similarity for computational modeling.

Conclusions:

  • Multidimensional scaling (MDS) is a viable method for modeling stimulus similarity in visual search.
  • The proposed GS "plug-in" extends computational GS applications to complex stimuli.
  • This approach offers potential for further research into target-absent search patterns.