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Generating Strictly Controlled Stimuli for Figure Recognition Experiments
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There Is a "U" in Clutter: Evidence for Robust Sparse Codes Underlying Clutter Tolerance in Human Vision.

Patrick H Cox1, Maximilian Riesenhuber2

  • 1Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20007.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|October 23, 2015
PubMed
Summary
This summary is machine-generated.

Object recognition in clutter is challenging. A computational model shows that clutter-tolerant neurons explain averaging responses and enable recognition, predicting a U-shaped performance curve confirmed by experiments.

Keywords:
HMAXcluttersparse codingvision

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Area of Science:

  • Neuroscience
  • Computational Vision
  • Cognitive Science

Background:

  • Object recognition in cluttered scenes is vital for human vision but poorly understood.
  • Existing models suggest neural responses average constituent object responses, posing a challenge for disentangling identities.
  • An alternative hypothesis proposes clutter-robust neurons that maintain selectivity.

Purpose of the Study:

  • To investigate the neural computations underlying object recognition in clutter using a computational model.
  • To explain averaging-like neural responses and demonstrate how clutter-robust neurons facilitate recognition.
  • To test a novel prediction regarding the relationship between target recognition and clutter similarity.

Main Methods:

  • Simulations using the HMAX model of object recognition.
  • Fitting the model to existing electrophysiology and fMRI data from inferotemporal cortex and object-selective cortex.
  • Experimental validation of a model-derived prediction on human object recognition performance.

Main Results:

  • The HMAX model successfully replicates averaging-like responses as a consequence of clutter-tolerant neurons responding to suboptimal stimuli.
  • The model demonstrates that object recognition can be achieved through sparse readout of clutter-robust neurons.
  • Human recognition performance exhibited a U-shaped dependency on target-clutter similarity, matching the model's prediction.

Conclusions:

  • Averaging-like neural responses in clutter can be explained by the activity of clutter-tolerant neurons.
  • Object recognition in clutter is achievable via sparse readout of neurons with robust selectivity.
  • The findings support a simple, unifying computational model for object recognition in complex visual scenes.