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Predicting human perceptual decisions by decoding neuronal information profiles.

Tzvetomir Tzvetanov1, Thilo Womelsdorf

  • 1Cognitive Neuroscience Laboratory, German Primate Center, Kellnerweg 4, 37077 Göttingen, Germany. ttzvetanov@dpz.gwdg.de

Biological Cybernetics
|April 1, 2008
PubMed
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Neuronal populations encode environmental information as Fisher information (FI). This study shows FI decoding explains perceptual misjudgments and discrimination thresholds in motion perception tasks, aligning with neurophysiological evidence.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Perception

Background:

  • Perception arises from neuronal population responses in sensory cortex.
  • Existing models struggle to fully explain how population activity translates to perception and discrimination.
  • Fisher information (FI) offers a potential framework for understanding sensory information representation.

Purpose of the Study:

  • To propose and validate a model where neuronal population responses explicitly represent environmental information as Fisher information (FI).
  • To investigate how FI decoding can explain perceptual judgments and discrimination in fine discrimination tasks.
  • To compare the predictive power of the FI decoding model against standard decoding models using human motion discrimination data.

Main Methods:

Related Experiment Videos

  • Developed a model where perceived stimulus is inferred from FI across neuronal populations.
  • Applied the FI decoding model and standard models (population vector, maximum likelihood, MAP Bayesian inference) to human motion discrimination data.
  • Analyzed perceptual misjudgments (motion repulsion) and discrimination thresholds in relation to surround motion effects.

    • Main Results:

    • The FI decoding model successfully predicted perceptual misjudgments and discrimination thresholds, consistent with neurophysiological data.
    • All models predicted the observed perceptual misjudgments, but the FI approach required neuronal tuning characteristics more aligned with experimental evidence.
    • The FI approach explained perceptual variations based on changes in total FI content with varying surround motion.

    Conclusions:

    • Neuronal population responses explicitly encoding Fisher information can explain perceptual judgments and misjudgments.
    • The FI decoding scheme provides a neurophysiologically plausible account of how the brain decodes sensory information.
    • This framework offers testable predictions regarding neuronal tuning properties underlying human perception.