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Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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Related Experiment Video

Updated: Feb 28, 2026

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

Driving Under the Influence (of Language).

Daniel Paul Barrett, Scott Alan Bronikowski, Haonan Yu

IEEE Transactions on Neural Networks and Learning Systems

|June 15, 2017

Summary

This study introduces a framework for connecting language and robotic driving. It enables machines to understand and generate driving directions, achieving 74.9% accuracy compared to human performance.

Area of Science:

Robotics
Natural Language Processing
Artificial Intelligence

Background:

Bridging the gap between natural language instructions and robotic actions is crucial for human-robot interaction.
Current systems often struggle with nuanced semantic understanding in dynamic environments like driving.

Purpose of the Study:

To develop a unified framework for grounding natural-language semantics in robotic driving.
To enable machines to learn, generate, and comprehend language related to driving paths.

Main Methods:

Acquisition: Learning meanings of nouns and prepositions from human-annotated driving paths.
Generation: Creating sentential descriptions for new driving paths using learned meanings.
Comprehension: Enabling automated driving to follow natural language navigational goals.

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Examining Bilingual Language Control Using the Stroop Task

Examining Bilingual Language Control Using the Stroop Task

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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

Related Experiment Videos

Last Updated: Feb 28, 2026

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

Examining Bilingual Language Control Using the Stroop Task

Examining Bilingual Language Control Using the Stroop Task

Published on: February 26, 2020

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

Main Results:

Human judges evaluated the semantic fidelity of machine-generated and interpreted sentences.
Machine performance reached 74.9% in understanding and generating driving-related language.
Human annotator performance served as a benchmark at 83.8%.

Conclusions:

The proposed framework effectively grounds natural language in robotic driving tasks.
Demonstrated capabilities in learning, generation, and comprehension show promise for advanced human-robot collaboration.
Further research can refine machine performance to approach human-level understanding in this domain.