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The processing of compound radial frequency patterns.

Gunnar Schmidtmann1, Frederick A A Kingdom2, Gunter Loffler3

  • 1Eye & Vision Sciences Research Group, School of Health Professions, University of Plymouth, Plymouth, Devon, England, United Kingdom.

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|May 15, 2019
PubMed
Summary
This summary is machine-generated.

Complex shapes are perceived by combining radial frequency (RF) patterns. Our study shows additive summation, not just probability summation, suggesting efficient combination of RF components by a single noise-limited mechanism.

Keywords:
Additive summationProbability summationRadial frequency patternsShape channelsSummation

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Area of Science:

  • Visual perception
  • Computational neuroscience

Background:

  • Complex visual shapes can be constructed by combining radial frequency (RF) patterns.
  • Previous research suggests that the brain may encode these complex shapes using multiple, narrowly-tuned RF shape channels.

Purpose of the Study:

  • To test the hypothesis that complex RF shapes are encoded by multiple, narrowly-tuned channels.
  • To investigate the summation mechanisms underlying the detection and discrimination of combined RF components.

Main Methods:

  • Measured detection and discrimination thresholds for combinations of two RF components.
  • Compared empirical data against predictions from various models: winner-take-all, single-channel additive summation (AS), and multi-channel probability summation (PS) and AS models.
  • Evaluated multi-channel models under Fixed and Matched Attention Window scenarios.

Main Results:

  • Sensitivity for compound RF patterns was significantly better than for individual components, indicating summation.
  • Summation effects exceeded predictions of probability summation (PS), suggesting additive summation (AS) via a common mechanism.
  • Winner-take-all and single-channel models were rejected based on the data.

Conclusions:

  • The findings support a model where detection of combined RF patterns involves channels that are efficiently combined.
  • The best-performing model was an additive summation (AS) model with a Fixed Attention Window.
  • This suggests that RF component detection is limited by a single source of internal noise for both components and compounds.