The Sapir-Whorf hypothesis and inference under uncertainty

Area of Science:

• Cognitive Science
• Linguistics
• Computational Linguistics

Background:

• The Sapir-Whorf hypothesis posits that language shapes human cognition and perception.
• Despite its influence, the hypothesis has seen limited computational investigation.
• It has generated significant debate within linguistics and cognitive science.

Purpose of the Study:

• To explore a computational perspective on the Sapir-Whorf hypothesis.
• To reframe the hypothesis using probabilistic inference.
• To address controversies and normalize the Sapir-Whorf hypothesis within established cognitive principles.

Main Methods:

• Review of recent developments in computational linguistics and cognitive science.
• Application of probabilistic inference models to linguistic relativity.
• Analysis of how language impacts nonlinguistic cognition through inferential processes.

Main Results:

• A probabilistic inference framework offers a new lens for the Sapir-Whorf hypothesis.
• This computational view may reconcile existing controversies surrounding linguistic relativity.
• Language's influence on cognition can be explained by standard principles of inference under uncertainty.

Conclusions:

• Viewing the Sapir-Whorf hypothesis through probabilistic inference provides a testable computational model.
• This approach integrates linguistic relativity with broader principles of cognitive science.
• It offers a path toward a more unified understanding of language, thought, and perception.