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Related Concept Videos

Association Areas of the Cortex01:21

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:
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Related Experiment Video

Updated: Jul 9, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Brain reading using full brain support vector machines for object recognition: there is no "face" identification

Stephen José Hanson1, Yaroslav O Halchenko

  • 1Rutgers Mind/Brain Analysis Laboratories, Psychology Department, Rutgers University, Newark, NJ 07102, USA. jose@tractatus.rutgers.edu

Neural Computation
|December 1, 2007
PubMed
Summary

New brain imaging methods show distinct brain areas are not solely responsible for identifying objects like faces or houses. This research offers a more nuanced understanding of object recognition in the human brain.

Related Experiment Videos

Last Updated: Jul 9, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Brain Imaging

Background:

  • Distinguishing between detecting brain responses and identifying object classes has been a challenge in object recognition research.
  • Current associative methods in brain imaging, like the general linear model, cannot definitively resolve claims about brain areas being necessary and sufficient for object identification.

Purpose of the Study:

  • To address the controversy surrounding brain areas responsible for object identification.
  • To develop and apply novel machine learning techniques for analyzing brain activity during object recognition tasks.

Main Methods:

  • Trained whole-brain (40,000 voxels) single repetition time (TR) classifiers on data from 10 subjects.
  • Utilized two recognition tasks focusing on controversial stimuli: houses and faces.
  • Employed out-of-sample generalization testing on unseen TRs to validate classifier performance.

Main Results:

  • Achieved 97.4% median out-of-sample generalization, enabling reliable voxel diagnosticity assessment.
  • Identified potential areas diagnostic for specific stimuli (e.g., LO for houses, STS for faces).
  • Found that neither the fusiform face area nor the parahippocampal place area were uniquely diagnostic for faces or places, respectively.

Conclusions:

  • The study challenges the notion of single brain areas being exclusively responsible for recognizing specific object categories.
  • Highlights the limitations of traditional methods in resolving complex questions in cognitive neuroscience.
  • Suggests a more distributed or complex neural representation for object recognition than previously assumed.