Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

Higher Mental Functions of the Brain: Language

Higher Mental Functions of the Brain: Language

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms.

Cancer research·2024

Same author

A foundation model for clinical-grade computational pathology and rare cancers detection.

Nature medicine·2024

Same author

Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.

Archives of pathology & laboratory medicine·2022

Same author

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans.

Diagnostics (Basel, Switzerland)·2022

Same author

Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems.

Frontiers in digital health·2021

Same author

Replay in Deep Learning: Current Approaches and Missing Biological Elements.

Neural computation·2021

Search research articles

Related Experiment Video

Updated: Nov 12, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Challenges and Prospects in Vision and Language Research.

Kushal Kafle¹, Robik Shrestha¹, Christopher Kanan^1,2,3

¹Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States.

Frontiers in Artificial Intelligence

|March 18, 2021

Summary

Current artificial intelligence (AI) vision and language (V&L) benchmarks are flawed, enabling algorithms to perform well without true understanding. New, carefully designed benchmarks are needed to accurately evaluate AI capabilities.

Keywords:

captioning computer vision dataset bias natural language understanding visual Turing test visual question answering

More Related Videos

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

Related Experiment Videos

Last Updated: Nov 12, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

Area of Science:

Artificial Intelligence
Computer Vision
Natural Language Processing

Background:

Language-grounded image understanding tasks are used to evaluate AI progress.
These tasks ideally integrate computer vision, reasoning, and natural language understanding.
Existing datasets and evaluation methods have significant flaws.

Purpose of the Study:

To highlight the limitations of current vision and language (V&L) benchmarks.
To argue that flawed benchmarks allow V&L algorithms to achieve high performance without robust understanding.
To propose solutions for creating more effective benchmarks.

Main Methods:

Analysis of recent studies in V&L literature.
Observation of dataset bias, robustness issues, and spurious correlations in V&L tasks.
Argumentation based on empirical evidence and literature review.

Main Results:

Current V&L benchmarks are replete with flaws.
Algorithms can achieve high scores on these benchmarks without genuine V&L understanding.
Dataset biases and spurious correlations are prevalent.

Conclusions:

Flawed benchmarks hinder the accurate evaluation of AI progress in V&L tasks.
There is a critical need for improved benchmark design.
Carefully constructed benchmarks can mitigate existing challenges and lead to more robust AI systems.