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Related Concept Videos

Language Development01:22

Language Development

403
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
403
Components of Language01:24

Components of Language

329
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.
329
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.
377
Language01:16

Language

250
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.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
250
Structuralism01:26

Structuralism

709
Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
Titchener's approach to structuralism was unique. He...
709
Structure of Self01:29

Structure of Self

459
Sigmund Freud's model of the human psyche is often illustrated using an iceberg analogy. The iceberg's visible tip represents the conscious mind, which includes thoughts and perceptions that individuals are immediately aware of. However, the larger, submerged portion of the iceberg represents the unconscious mind, a reservoir of repressed desires, instincts, and memories. According to Freud, human behavior is primarily shaped by this hidden realm.
Components of the Mind: Id, Ego, and...
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Related Experiment Video

Updated: Jul 25, 2025

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
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Finding Structure in One Child's Linguistic Experience.

Wentao Wang1, Wai Keen Vong1, Najoung Kim1,2

  • 1Center for Data Science, New York University.

Cognitive Science
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

Neural networks trained on a single child's language input learn syntactic and semantic word categories. Visual data offers minor gains, showing core linguistic knowledge is learnable from limited developmental experience.

Keywords:
Child developmentFirst-person videoLanguage learningLearnabilityMultimodal learningNeural networksStatistical learning

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Developmental Psychology

Background:

  • Neural networks excel in natural language processing but require vast training data.
  • Children acquire language with significantly less input than typical AI models.
  • The potential for distributional learners to extract linguistic knowledge from limited, naturalistic data is underexplored.

Purpose of the Study:

  • To investigate what linguistic knowledge neural networks can acquire from a single child's developmental language data.
  • To compare the learning capabilities of language-only versus vision-and-language models.
  • To determine the impact of visual input on language acquisition in artificial models.

Main Methods:

  • Training language-only and vision-and-language neural networks on a longitudinal dataset of a single child's egocentric visual data and text transcripts.
  • Analyzing emergent word clusters for syntactic (e.g., nouns, verbs) and semantic categories.
  • Assessing the models' sensitivity to linguistic acceptability contrasts (e.g., agreement, argument structure).

Main Results:

  • Neural networks formed emergent clusters of words corresponding to syntactic and semantic categories, mirroring human language acquisition.
  • Models demonstrated sensitivity to grammatical acceptability phenomena like determiner-noun agreement and argument structure.
  • Incorporating visual information provided incremental improvements in word prediction, particularly for visually grounded categories, without fundamentally altering linguistic representations.

Conclusions:

  • A significant amount of linguistic knowledge, including syntactic and semantic categorization, is learnable from a restricted, naturalistic language sample.
  • Visual input offers some benefits for language learning in artificial models but is not essential for acquiring core linguistic structures.
  • This study highlights the learnability of fundamental linguistic knowledge from early developmental experiences, providing insights into both AI and human language acquisition.