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

Association Areas of the Cortex

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

You might also read

Related Articles

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

Sort by

Same author

Visual search for people wearing protective clothing in a forestial environment - Differences between gaze and behavior.

PloS one·2026

Same author

Task-irrelevant human and robot head movements bias gaze in humans who follow them through virtual reality.

Scientific reports·2026

Same author

Unsupervised Representation Learning From Sparse Transformation Analysis.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

CellSAM: a foundation model for cell segmentation.

Nature methods·2025

Same author

Violated expectations during locomotion through virtual environments: Age effects on gaze guidance.

Journal of vision·2025

Same author

A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification.

International journal of computer vision·2025

Same journal

Analysis of human visual experience data.

Journal of vision·2026

Same journal

Pyramid-based Bayesian modeling for high-resolution behavioral analysis.

Journal of vision·2026

Same journal

Sensation without perception: The white whale effect and perceptual blindness in autonomous vehicles.

Journal of vision·2026

Same journal

Gaze behavior during closed-captioned movie viewing adapts to absent audio through more frequent switching between text and scene.

Journal of vision·2026

Same journal

In pursuit of saccade awareness: Limited volitional control and minimal conscious access to catch-up saccades during smooth pursuit eye movements.

Journal of vision·2026

Same journal

Dissociable effects of element-lifetime and stimulus-duration on local and global motion processing: An equivalent noise study.

Journal of vision·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 26, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Objects predict fixations better than early saliency.

Wolfgang Einhäuser¹, Merrielle Spain, Pietro Perona

¹Division of Biology, California Institute of Technology, Pasadena, CA, USA. wet@physik.uni-marburg.de

Journal of Vision

|January 17, 2009

Summary

This summary is machine-generated.

Human eye movements during scene viewing are driven by interesting objects, not just basic visual features. Object recognition, not early saliency, better predicts attention and visual search.

More Related Videos

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 26, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

Cognitive Psychology
Neuroscience
Computer Vision

Background:

Human eye movements are linked to attention and visual recognition.
Current attention models often assume early visual features (color, contrast) directly guide attention.

Purpose of the Study:

To test the hypothesis that observers attend to 'interesting' objects rather than solely relying on low-level visual features.
To investigate the relationship between object recognition, attention, and visual saliency in natural scene perception.

Main Methods:

Measured human eye position while observers viewed photographs of natural scenes.
Observers performed tasks including artistic evaluation, content analysis, and visual search.
Object recall frequency was used to predict eye fixations.

Main Results:

Recognized objects, weighted by recall frequency, predicted eye fixations better than low-level saliency maps across different tasks.
Saliency combined with object locations predicted frequently named objects.
This suggests saliency influences attention indirectly through object recognition.

Conclusions:

Object-based attention plays a crucial role in visual perception and scene understanding.
Attention and object recognition models need to be integrated, as attention is not merely a preprocessing step for recognition.
Future models should incorporate object-level information to accurately predict human eye movements and attention allocation.