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Related Concept Videos

Language and Cognition01:27

Language and Cognition

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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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Language01:16

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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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Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
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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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Components of Language01:24

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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.
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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
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Mutual influence between language and perception in multi-agent communication games.

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Deep learning models show how visual perception influences language emergence. Manipulating communication protocols improves agents' visual representations and aligns their understanding.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Language and perception are deeply intertwined in human cognition.
  • Understanding this interface is crucial for developing sophisticated artificial agents.
  • Deep learning offers novel methods for studying these complex interactions.

Purpose of the Study:

  • To investigate the interplay between language emergence and visual perception using deep learning.
  • To model how agents develop communication and how it affects their visual understanding.
  • To explore the co-evolution of communication protocols and perceptual representations.

Main Methods:

  • Utilized a reference game with sender and receiver agents implemented as deep neural networks.
  • Agents featured dedicated vision and language modules.
  • Systematically manipulated agents' visual representations and communication protocols.

Main Results:

  • Perceptual biases were found to significantly shape semantic categorization and communicative content.
  • Modifying communication protocols led to more accurate visual representations of specific attributes.
  • Communication partners' visual representations became more aligned after protocol adjustments.

Conclusions:

  • Visual perception influences the development and content of emergent communication.
  • Communication protocols can drive the refinement of visual representations in artificial agents.
  • Findings suggest potential for optimizing artificial agent learning through co-adaptation of perception and communication.