Comparing models of contrast gain using psychophysical experiments
Open accessAbstract
In a wide variety of neural systems, neurons tuned to a primary dimension of interest often have responses that are modulated in a multiplicative manner by other features such as stimulus intensity or contrast. In this methodological study, we present a demonstration that it is possible to use psychophysical experiments to compare competing hypotheses of multiplicative gain modulation in a neural population, using the specific example of contrast gain modulation in orientation-tuned visual neurons. We demonstrate that fitting biologically interpretable models to psychophysical data yields physiologically accurate estimates of contrast tuning parameters and allows us to compare competing hypotheses of contrast tuning. We demonstrate a powerful methodology for comparing competing neural models using adaptively generated psychophysical stimuli and demonstrate that such stimuli can be highly effective for distinguishing qualitatively similar hypotheses. We relate our work to the growing body of literature that uses fits of neural models to behavioral data to gain insight into neural coding and suggest directions for future research.
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The power law relationship is consistent with a form of the Naka-Rushton function commonly used to describe the mapping from reliability to neural gain.
“The relationship is also consistent with a form of the Naka-Rushton function [84, 85] commonly used to describe the mapping from reliability to neural gain g:”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [85]
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Comparing Bayesian and non-Bayesian accounts of human confidence reports
Adler, W. T., & Ma, W. J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006572 · Cited by 118 · see citing works
- Authors
- DiMattina, C.
- Affiliations
- Florida Gulf Coast University (US)
- Corresponding author
- DiMattina, Christopher
- Venue
- Journal of Vision
- Published
- 2016-07-01
- Volume
- 16
- Issue
- 9
- Pages
- 1
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2462702331
- Cited by
- 12
- References
- 12
- Citations by year
- 2025: 1 · 2022: 2 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 3 · 2017: 1
- FWCI
- 1.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Vision (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Florida Gulf Coast University
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- neural coding
- MeSH terms
- Humans, Neurons, Orientation, Psychophysics, Visual Cortex, Contrast Sensitivity
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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