Cited by 350Neural dynamics and brain functionVisual perception and processing mechanismsNeural and Behavioral Psychology Studies

Variance as a signature of neural computations during decision making

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Churchland, Anne. K., Kiani, R., Chaudhuri, R., Wang, Xiao-Jing, Pouget, Alexandre, Shadlen, M.N.·2011·Neuron

Abstract

Traditionally, insights into neural computation have been furnished by averaged firing rates from many stimulus repetitions or trials. We pursue an analysis of neural response variance to unveil neural computations that cannot be discerned from measures of average firing rate. We analyzed single-neuron recordings from the lateral intraparietal area (LIP), during a perceptual decision-making task. Spike count variance was divided into two components using the law of total variance for doubly stochastic processes: (1) variance of counts that would be produced by a stochastic point process with a given rate, and loosely (2) the variance of the rates that would produce those counts (i.e., "conditional expectation"). The variance and correlation of the conditional expectation exposed several neural mechanisms: mixtures of firing rate states preceding the decision, accumulation of stochastic "evidence" during decision formation, and a stereotyped response at decision end. These analyses help to differentiate among several alternative decision-making models.

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Crossref · 10.1016/j.neuron.2010.12.037

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  • VarCE calculated from residuals is similar and shows a linear increase between decision onset and decision termination times.

    “When VarCE is calculated from residuals (5, 51) (Materials and Methods), it is similar (Fig. 4G, black) showing linear increase between decision onset (Fig. 4G, up arrow) and decision termination (Fig. 4G, down arrow) times.”

    From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]

    The Methods (Data Analysis; page unavailable) say VarCE was calculated from residuals, and the Results (VarCE during decision formation; page unavailable) report a linear rise after decision onset. The paper does not show that this rise continues through decision termination: its Results (VarCE at the time of the choice) report a sharp decline near the saccade for one choice direction. (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • Analysis of spike count noise during this period showed characteristic linear increase in internally driven spike rate variability (VarCE) and decrease of its correlations (CorCE) consistent with the gradual accumulation models of decision-making.

    “Analysis of spike count noise during this period showed characteristic linear increase in internally driven spike rate variability (VarCE) and decrease of its correlations (CorCE) consistent with the gradual accumulation models of decision-making (51).”

    From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]

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    Experimental — accuracy not yet measured
  • The fast increase in VarCE indicates accumulation of stochastic trial-by-trial diffusion noise.

    “indicating accumulation of stochastic trial-by-trial diffusion noise (5, 51).”
  • A gradual accumulation of noise is an important diagnostic signature of perceptual decision-making expected for most accumulation-to-bound decision models.

    “An important diagnostic signature of perceptual decision-making is the gradual accumulation of noise (5, 51) expected for most accumulation-to-bound decision models.”
  • Total variance of the doubly stochastic process is the sum of two components: PPV, proportional to mean spike rates, and variance of conditional expectation (VarCE), reflecting variance of rates.

    “Total variance of this doubly stochastics process becomes a sum of two separate components (51) (Materials and Methods): PPV, proportional to mean spike rates, and variance of conditional expectation (VarCE), reflecting variance of rates.”

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Authors
Churchland, A. K., Kiani, R., Chaudhuri, R., Wang, X.-J., Pouget, A., & Shadlen, M. N.
Affiliations
  • University of Washington (US)
  • Howard Hughes Medical Institute (US)
  • Yale University (US)
  • University of Rochester (US)
Corresponding author
Churchland, Anne. K.
Venue
Neuron
Publisher
Elsevier BV
Published
2011-02
Volume
69
Issue
4
Pages
818-831
ISSN
0896-6273 (ISSN-L)
OpenAlex
W2105007656
Cited by
350
References
56
Citations by year
2026: 7 · 2025: 19 · 2024: 27 · 2023: 15 · 2022: 20 · 2021: 25 · 2020: 21 · 2019: 20 · 2018: 20 · 2017: 22 · 2016: 30 · 2015: 35 · 2014: 28 · 2013: 20 · 2012: 33
FWCI
14.30
Type
journal article
Language
en
Open access
Locations
  • Neuron (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
Funders
  • Howard Hughes Medical Institute
  • James S. McDonnell Foundation (P30 EY001319)
  • National Institutes of Health (RR001 66, EY001319)
  • U.S. Public Health Service (BCS0346785)
  • Multidisciplinary University Research Initiative (N00014-07-1-0937)
  • National Institute of Mental Health (MH 062349, R01MH062349)
  • National Institute on Drug Abuse (DA022780, R01DA022780)
  • National Eye Institute (EY011378, EY019072, R00EY019072, K99EY019072, R01EY011378, P30EY001319)
  • National Center for Research Resources (RR00166, P51RR000166)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
perceptual decision making, lateral intraparietal area, law of total variance, doubly stochastic processes, conditional expectation, single-unit recording, decision models
MeSH terms
Action Potentials, Animals, Haplorhini, Computer Simulation, Decision Making, Models, Neurological, Motion Perception, Neurons, Numerical Analysis, Computer-Assisted, Photic Stimulation, Reaction Time, Statistics as Topic, Stochastic Processes, Time Factors

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