Stimulus-dependent relationships between behavioral choice and sensory neural responses
Abstract
Understanding perceptual decision-making requires linking sensory neural responses to behavioral choices. In two-choice tasks, activity-choice covariations are commonly quantified with a single measure of choice probability (CP), without characterizing their changes across stimulus levels. We provide theoretical conditions for stimulus dependencies of activity-choice covariations. Assuming a general decision-threshold model, which comprises both feedforward and feedback processing and allows for a stimulus-modulated neural population covariance, we analytically predict a very general and previously unreported stimulus dependence of CPs. We develop new tools, including refined analyses of CPs and generalized linear models with stimulus-choice interactions, which accurately assess the stimulus- or choice-driven signals of each neuron, characterizing stimulus-dependent patterns of choice-related signals. With these tools, we analyze CPs of macaque MT neurons during a motion discrimination task. Our analysis provides preliminary empirical evidence for the promise of studying stimulus dependencies of choice-related signals, encouraging further assessment in wider data sets.
Data availability
No data was collected as part of this study.
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A relationship between behavioral choice and the visual responses of neurons in macaque MTData from the Vis Neurosci 1996 paper by these authors and with this title.
Article and author information
Author details
Funding
National Institute of Neurological Disorders and Stroke (R01 NS108410)
- Stefano Panzeri
National Institute of Neurological Disorders and Stroke (U19 NS107464)
- Stefano Panzeri
National Eye Institute (R01 EY028811)
- Ralf M Haefner
Fondation Bertarelli
- Daniel Chicharro
National Institute of Neurological Disorders and Stroke (U19 NS118246)
- Ralf M Haefner
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Chicharro et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Computational and Systems Biology
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