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Stimulus-dependent relationships between behavioral choice and sensory neural responses.

Daniel Chicharro1,2, Stefano Panzeri1, Ralf M Haefner3

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Summary

Researchers developed new methods to analyze how neural activity relates to choices, revealing stimulus-dependent choice probabilities (CPs) in perceptual decision-making. This advances understanding of neural signals driving behavior.

Keywords:
choice probabilityneural codingneuroscienceperceptual decision-makingrhesus macaquesensory neurons

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Decision Neuroscience

Background:

  • Perceptual decision-making links sensory neural activity to behavioral choices.
  • Choice probability (CP) quantifies activity-choice covariations in two-choice tasks but often overlooks stimulus level effects.
  • Existing methods lack the granularity to capture stimulus-dependent neural signal variations influencing choices.

Purpose of the Study:

  • To theoretically predict and empirically validate stimulus dependencies in activity-choice covariations.
  • To develop novel analytical tools for dissecting stimulus- and choice-driven neural signals.
  • To characterize stimulus-dependent patterns of choice-related signals in neural populations.

Main Methods:

  • Developed a general decision-threshold model incorporating feedforward/feedback processing and stimulus-modulated covariance.
  • Derived analytical predictions for stimulus-dependent choice probabilities (CPs).
  • Introduced refined CP analyses and generalized linear models with stimulus-choice interactions.

Main Results:

  • Analytically predicted a general, previously unreported stimulus dependence of CPs under specific model assumptions.
  • Developed and applied new analytical tools to assess stimulus- or choice-driven signals per neuron.
  • Preliminary analysis of macaque MT neurons in a motion discrimination task showed evidence supporting stimulus-dependent choice signals.

Conclusions:

  • Choice probabilities (CPs) exhibit stimulus-dependent variations, challenging previous assumptions of uniformity.
  • New analytical methods accurately characterize stimulus-dependent choice-related neural signals.
  • Findings encourage further investigation into stimulus dependencies of choice signals across broader datasets.