Interpreting Silent Gesture: Cognitive Biases and Rational Inference in Emerging Language Systems

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.