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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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Higher Mental Functions of the Brain: Language01:10

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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.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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Language Development01:22

Language Development

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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...
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Components of Language01:24

Components of Language

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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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Modeling and Similitude01:12

Modeling and Similitude

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Related Experiment Video

Updated: Jan 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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From Concept to Representation: Modeling Driving Capability and Task Demand with a Multimodal Large Language Model.

Haoran Zhou1, Alexander Carballo2,3,4, Keisuke Fujii1,5

  • 1Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a multimodal large language model framework to quantify driving task demand and capability. It provides an interpretable measure of task difficulty, enabling proactive risk assessment in intelligent vehicles.

Keywords:
driving capabilitymultimodal large language modelrepresentation learningtask demandtask difficultytask–capability interface model

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Area of Science:

  • Intelligent transportation systems
  • Artificial intelligence in automotive safety
  • Human-computer interaction in driving

Background:

  • Driving safety relies on the balance between task demand and driver capability.
  • Current methods lack a unified, quantifiable approach to assess this interplay.
  • Existing frameworks struggle to predict potential capability shortfalls before unsafe driving occurs.

Purpose of the Study:

  • To develop a novel framework for quantifying driving task demand and capability using multimodal data.
  • To create an interpretable measurement of task difficulty for proactive risk assessment.
  • To enable early detection of driver capability degradation and potential safety risks.

Main Methods:

  • Utilized a multimodal large language model (LLM) integrating scene images, maneuver descriptions, control inputs, and traffic states.
  • Transformed heterogeneous driving signals into low-dimensional embeddings for task demand and driving capability.
  • Projected embeddings into a shared latent space for interpretable task difficulty measurement.
  • Employed a customized BLIP 2 backbone, fine-tuned on diverse simulated driving scenarios.

Main Results:

  • The framework successfully quantifies task demand and driving capability from multimodal inputs.
  • An interpretable measurement of task difficulty was generated, correlating with potential capability shortfalls.
  • The model demonstrated consistency within tasks and captured impairment-related capability degradation.
  • The framework showed effective transferability to real-world motorway data without retraining.

Conclusions:

  • The proposed LLM-based framework offers a concise and effective method for proactive risk assessment in intelligent vehicles.
  • It provides an explainable measurement of driving difficulty, crucial for enhancing vehicle safety.
  • This approach paves the way for more sophisticated safety systems that anticipate and mitigate risks before they materialize.