Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

MOSAIC: A scalable framework for fMRI dataset aggregation and modeling of human vision.

bioRxiv : the preprint server for biology·2026
Same author

A feedforward mechanism for human-like contour integration.

PLoS computational biology·2025
Same author

Intrinsically memorable words have unique associations with their meanings.

Journal of experimental psychology. General·2025
Same author

Touch to text: Spatiotemporal evolution of braille letter representations in blind readers.

bioRxiv : the preprint server for biology·2024
Same author

A large-scale examination of inductive biases shaping high-level visual representation in brains and machines.

Nature communications·2024
Same author

Contrastive learning explains the emergence and function of visual category-selective regions.

Science advances·2024

Related Experiment Video

Updated: Nov 22, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

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

Published on: January 23, 2017

10.1K

Spatial ensemble statistics are efficient codes that can be represented with reduced attention.

George A Alvarez1, Aude Oliva

  • 1Department of Psychology, Harvard University, Cambridge, MA 02138, USA. alvarez@wjh.harvard.edu

Proceedings of the National Academy of Sciences of the United States of America
|April 22, 2009
PubMed
Summary
This summary is machine-generated.

Even with reduced visual attention, the brain efficiently encodes spatial patterns by leveraging structural regularities. This "spatial ensemble statistics" method overcomes noise in local feature detection for better overall representation.

More Related Videos

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

11.7K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.9K

Related Experiment Videos

Last Updated: Nov 22, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

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

Published on: January 23, 2017

10.1K
Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

11.7K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.9K

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Visual Perception

Background:

  • The natural environment exhibits structural regularities, enabling efficient information representation.
  • Efficient coding is well-studied in low-level sensory processing but less so in visual attention.

Purpose of the Study:

  • To investigate efficient coding of spatial patterns under reduced attention.
  • To determine if "spatial ensemble statistics" are effectively encoded when attention is divided.

Main Methods:

  • Observers monitored background changes while attentively tracking foreground objects.
  • Stimuli dissociated local structure changes from ensemble structure changes.
  • Sensitivity to background changes was measured under divided attention.

Main Results:

  • Observers were more sensitive to background changes altering ensemble structure than local structure.
  • Reduced attention increased noise in local feature representations.
  • Spatial ensemble statistics compensated for local noise by pooling information.

Conclusions:

  • Structural regularities allow efficient encoding of spatial ensemble statistics even with divided attention.
  • Pooling across local measurements via ensemble statistics enhances representational precision.
  • This mechanism overcomes noise inherent in reduced attentional states.