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Joint perceptual decision-making: a case study in explanatory pluralism.

Drew H Abney1, Rick Dale1, Jeff Yoshimi1

  • 1Cognitive and Information Sciences, School of Social Sciences, Humanities and Arts, University of California Merced, CA, USA.

Frontiers in Psychology
|May 6, 2014
PubMed
Summary

Explanatory pluralism views diverse cognitive science approaches as complementary, not competing. This case study demonstrates how analyzing decision-making, confidence sharing, and acoustic energy together reveals a richer understanding of complex phenomena.

Keywords:
alignmentcomplexity matchingexplanatory pluralismjoint decision-makingphilosophy of science

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Area of Science:

  • Cognitive Science
  • Psychology
  • Linguistics
  • Acoustics

Background:

  • Cognitive science traditionally uses competing frameworks.
  • Explanatory pluralism offers a complementary perspective.
  • Previous applications of explanatory pluralism have been largely abstract.

Purpose of the Study:

  • To present a concrete case study of explanatory pluralism.
  • To demonstrate the application of explanatory pluralism to a perceptual decision-making task.
  • To explore the utility of explanatory pluralism for multiscale phenomena.

Main Methods:

  • Analysis of a perceptual decision-making task corpus.
  • Examination of three distinct analytical levels: behavioral (decision-making), linguistic (confidence sharing), and physical (acoustic energy).
  • Comparative analysis of findings from different descriptive levels.

Main Results:

  • Different analytical approaches revealed complementary insights into the same phenomenon.
  • Behavioral, linguistic, and physical analyses provided a multiscale understanding of perceptual decision-making.
  • The case study illustrated the practical application and benefits of explanatory pluralism.

Conclusions:

  • Explanatory pluralism provides a valuable framework for integrating diverse research approaches in cognitive science.
  • This case study highlights the importance of considering multiple levels of analysis for complex phenomena.
  • Best practices for critically assessing and complementing different approaches were identified.