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Area of Science:

  • Cognitive Science
  • Linguistics
  • Computational Modeling

Background:

  • Natural languages rely on constituent order to convey semantic roles like agent and patient.
  • The cognitive mechanisms driving these word order regularities remain incompletely understood.
  • Previous research suggests non-native language conventions may reveal underlying biases in ordering.

Purpose of the Study:

  • To investigate the interpretation of silent gesture, extending previous word order bias research.
  • To provide cross-linguistic evidence on how constituent order variability influences interpretation.
  • To computationally model the principles governing interpretation in emergent communication systems.

Main Methods:

  • Experimental studies involving silent gesture production and interpretation across different languages.
  • Development of a Bayesian model of interpretation based on biased inductive inference.
  • Estimation of cognitive biases from experimental data to parameterize the computational model.

Main Results:

  • Experimental evidence demonstrates that people utilize constituent order variability as a cue for interpretation.
  • Cross-linguistic data reveal consistent patterns in how gesture order influences meaning.
  • The Bayesian model successfully captures and quantifies the biases influencing interpretation.

Conclusions:

  • Human interpretation of non-conventional communication balances inherent ambiguity with skewed, asymmetric, yet defeasible ordering preferences.
  • These findings illuminate the cognitive underpinnings of language emergence and conventionalization.
  • The study provides a computational framework for understanding interpretation in communication systems.