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This study explored individual differences in pragmatic reasoning depth during language comprehension. Findings suggest heterogeneous populations better explain comprehension in reference games requiring complex implicatures.

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Area of Science:

  • Psycholinguistics
  • Cognitive Science
  • Computational Linguistics

Background:

  • Probabilistic pragmatics models language use as inference.
  • Current models often assume homogeneous populations, ignoring individual differences.
  • Theory of Mind (ToM) is crucial for pragmatic reasoning.

Purpose of the Study:

  • Investigate individual differences in ToM-related pragmatic reasoning depth.
  • Analyze reasoning in reference games with varying implicature complexity.
  • Determine if population heterogeneity explains comprehension data better.

Main Methods:

  • Utilized reference games to elicit ad hoc Quantity implicatures.
  • Employed Bayesian model comparison to assess population models.
  • Focused on comprehension data to evaluate model fit.

Main Results:

  • A heterogeneous population model significantly outperformed a homogeneous model.
  • This improved fit was particularly evident for comprehension.
  • Individual differences in reasoning depth were detectable.

Conclusions:

  • Population heterogeneity is a crucial factor in pragmatic reasoning models.
  • Future probabilistic models should account for individual differences.
  • This has implications for understanding language comprehension and ToM.