The DOI matches its Crossref record; 51 of the 56 references it cites were found in a registry or an index.
Variance as a Signature of Neural Computations during Decision Making
Open accessAbstract
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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“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“indicating accumulation of stochastic trial-by-trial diffusion noise (5, 51).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
The cited source says what the claim attributes to it.
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“An important diagnostic signature of perceptual decision-making is the gradual accumulation of noise (5, 51) expected for most accumulation-to-bound decision models.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“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.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“It has been argued (51) that if the variance of spike rates during evidence accumulation is defined by a diffusion process, then expectation of spike counts in consecutive time bins (CorCE) should covary.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“The calculated (Materials and Methods) CorCE matrix (Fig. 4J) indeed exhibits characteristic increase of correlation as a function of time and, for given time (e.g., along the top row), shows sequential decay as often observed (5, 51) during decision-making.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“Overall, VarCE exhibits a linear increase during epoch 4, a characteristic signature of diffusion process consistent with an accumulation-to-bound model (5, 51).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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Citations
Cites
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764 · cited 6×
“during Decision Making”
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“during Decision Making”
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“Lastly, we consider a recurrent neural network that is exemplary of a class of dynamical ‘‘attractor’’ models of decision making (e.g., Albantakis and Deco, 2009; Bogacz et al., 2006; Machens et al., 2005; Usher and McClelland, 2001; Wang, 2002).”
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“This is the same as drift diffusion with an upper and lower stopping bound (Bogacz et al., 2006; Ratcliff and Rouder, 1998), so long as the evidence for one alternative is evidence against the other (Gold and Shadlen, 2007).”
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“For example, the irregular discharge of neurons bears on theories of coding, synaptic integration and circuit function (Shadlen and Newsome, 1998; Softky and Koch, 1993).”
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“Together, the mean and variance of neural responses furnish rich insight into the limits of perception, motor control and decision making (Faisal et al., 2008; Glimcher, 2005; Parker and Newsome, 1998; Shadlen and Newsome, 1998).”
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Wang, X.-J. (2002) · Neuron · doi:10.1016/s0896-6273(02)01092-9 · Cited by 1,242 · cited 1×
“Lastly, we consider a recurrent neural network that is exemplary of a class of dynamical ‘‘attractor’’ models of decision making (e.g., Albantakis and Deco, 2009; Bogacz et al., 2006; Machens et al., 2005; Usher and McClelland, 2001; Wang, 2002).”
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Items 301–329 of 329
Stochastic computation of dominant eigenvalue and the law of total variance
Georgiou, G. M., Voigt, K., & Qiao, H. (2015) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn.2015.7280739 · Cited by 1
Active sensing explains cortical variability and improves visual informationPreprint
Ressmeyer, R. A., Rowley, D. P., Kothapalli, T., Galor, D., Wu, R.-J., Michele, R., Mitchell, J. F., Butts, D. A., Huk, A. C., & Yates, J. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.12.758571
“…call the residual covariance, The second term is the covariance of the conditional mean spike count, R ( t, e ) which we call the rate covariance, The total covariance therefore separates into a residual component and a component arising from structured changes in the underlying firing rates [45],”
So, N., Zylberberg, A., & Shadlen, M. N. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.11.750920
“…the decision, providing a direct observation of the single-trial trajectory itself—a different order of evidence from the trial-averaged firing rates and the variance signatures of bounded diffusion previously derived from single-neuron spike counts [12], or from behavioral model-fitting alone.”
Self-citationBenchmarking criteria to determine latent linear dimensionality in neural data
Vaccari, F. E., Diomedi, S., Bettazzi, E., Filippini, M., Vitis, M. D., Hadjidimitrakis, K., & Fattori, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-55225-1
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Decision-making through irruption theory: how to use and interpret the Haken-Kelso-Bunz model
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Partitioning Neural Co-Variability
Thomas, S., Zhu, B. J., Cullen, K. E., & Charles, A. S. (2026) · arXiv.org · doi:10.48550/arxiv.2605.06995
Macaque Area LIP Reflects Confidence-Dependent Changes in Decision Policy
Tu, M. S., Vivar-Lazo, M., & Fetsch, C. R. (2026) · Journal of Neuroscience · doi:10.64898/2026.04.27.721150
“Because LIP activity in the random-dot motion task has been shown to reflect a process of evidence accumulation (Churchland et al., 2011; Huk & Shadlen, 2005; Steinemann et al., 2024; Vivar-Lazo & Fetsch, 2026), the neural DV can be used to infer certain dynamic features of this process.”
The Generative Mind: Internalizing Noise in Brains and Machines
Ryu, J. (2026) · Journal of Sciences and Innovation for Sustainable Peace · doi:10.23184/jsisp.vol.4.no.1.06
Canonical cortical circuits: A unified sampling machine for static and dynamic inferencePreprint
Sale, E., & Zhang, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.06.663395
“The stochastic sampling closely matches the stochastic nature of neural dynamics, characterized by the large, structured neuronal response variability (Shadlen & Newsome, 1998; Churchland et al., 2011; Orbán et al., 2016; Echeveste et al., 2020; Zhang et al., 2023).”
Incorporation of a Cost of Deliberation Time in Perceptual Decision Making
Kira, S., Zylberberg, A., & Shadlen, M. N. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.2426-24.2025 · Cited by 11
“…area (LIP) gives a clear indication of a terminating threshold — a point of minimum variance just before the response (Roitman and Shadlen, 2002; Churchland et al., 2011; Kira et al., 2015) — implying that a downstream area terminates the decision when the fi ring rate reaches a critical level.”
Self-citationQian, Y., Herikstad, R., & Libedinsky, C. (2026) · Nature Communications · doi:10.1038/s41467-026-69380-6
Uncertainty Quantification in Working Memory via Moment Neural Networks
Ma, H., Lu, W.-L., & Feng, J.-F. (2024) · arXiv.org · doi:10.48550/arxiv.2411.14196
“Previous studies have demonstrated the significant role this variability plays in neural computation [41, 42].”
History-dependence shapes causal inference of brain-behaviour relationships
Caie, B., & Blohm, G. (2024) · doi:10.48550/arxiv.2403.00947
“Because of this, models accounting for behaviour and/or neuronal activity often introduce a random process at some level in order to explain empirical variability (e.g. Churchland et al., 2011; Deco et al., 2009; Kanashiro et al., 2017).”
MethodSignatures of heterogeneity in the statistical structure of target state aligned ensembles
Lenner, N., Haring, M., Eule, S., Grosshans, J., & Wolf, F. (2023) · doi:10.48550/arxiv.2304.09719
Lenner, N., Haring, M., Eule, S., Grosshans, J., & Wolf, F. (2023) · doi:10.48550/arxiv.2304.04279
Amengual, J. L., Bello, F. D., Hassen, S. B. H., Gaillard, C., Ästrand, E., & Hamed, S. B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.28.522139 · Cited by 1
“Previous literature has reported evidence that the activity in the prefrontal cortex is contained in a lowdimensional manifold that can be extracted projecting the firing rates from the high-dimensional recording space onto few latent variables that represent the majority of the variance in the recordings (7,13,38).”
BackgroundSpatial and temporal structure of choice representations in primate prefrontal cortexPreprint
Kollmorgen, S., Newsome, W. T., & Mante, V. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/595520
“…within a condition, which may further constrain the nature of the underlying neural processes (Bollimunta, Totten, & Ditterich, 2012; A. K. Churchland et al., 2011; M. M. Churchland et al., 2010; Latimer, Yates, Meister, Huk, & Pillow, 2015; Morcos & Harvey, 2016; Seidemann, Meilijson,…”
Neural Mechanisms of Individuality - EEG Studies in Self and Morality
Wolff, A. (2019) · doi:10.20381/ruor-23266
“…observed on multiple levels of neural activity, including cellular recordings (Arieli et al., 1996; Monier et al., 2003; Finn et al., 2007; Churchland et al., 2010, 2011; Hussar and Pasternak, 2010; Scaglione et al., 2011; Chang et al., 2012; White et al., 2012; Goris et al., 2014;…”
BackgroundInter-subject phase synchronization and the dynamics of human cognitionPreprint
Bolt, T., Nomi, J. S., Vij, S. G., Chang, C. E., & Uddin, L. Q. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/167072
Modeling stimulus-dependent variability improves decoding of population neural responsesPreprint
Ghanbari, A., Lee, C. M., Read, H. L., & Stevenson, I. H. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/146415
Is There a General Role for the Monkey Oculomotor System in Perceptual Decision-Making?
Li, Y., & Krishnamurthy, K. V. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1818-15.2015 · Cited by 1
“However, differences between the two areas emerged in terms of the secondorder statistics of spike trains, which has been used previously to differentiate evidence-accumulation from presaccadic bursts in area LIP (Churchland et al., 2011).”
BackgroundMicroscopic instability in recurrent neural networks
Yamanaka, Y., Амари, Ш.-И., & Shinomoto, S. (2015) · Physical Review E · doi:10.1103/physreve.91.032921 · Cited by 1
The paradoxical relationship of difficulty and lateral frontal cortex activityPreprint
Chatham, C. H., Long, N. M., & Badre, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.04700
Self-organization of microcircuits in networks of neurons with plastic synapses
Ocker, G. K., Litwin-Kumar, A., & Doiron, B. (2014) · doi:10.48550/arxiv.1411.3956
Parallel formation of multiple decisions in the visual cortex
Roelfsema, P. R., Zylberberg, A. D., Ouellette, B. G., Zeeuw, C. I. D., Sigman, M., & Lorteije, J. A. M. (2013) · Journal of Vision · doi:10.1167/13.9.125
“Most previous studies on sensory decisionmaking examined the activity of neurons in the parietal and frontal cortex (Churchland et al., 2011; Ding and Gold, 2012; Gold and Shadlen, 2000; Kiani and Shadlen, 2009; Kiani et al., 2008; Kim and Shadlen, 1999; Roitman and Shadlen, 2002; Shadlen and…”
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Nonlinear Filtering in Artificial Neural Network Applications in Business and Engineering
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“…that decision termination is mediated by application of a threshold to the neural representation of accumulating evidence (Gold and Shadlen 2007; Churchland et al. 2011; P L Smith and Ratcliff 2004) and this also holds in experiments that do not allow for free responses, as in our…”
BackgroundCompetition between visual stimuli in the monkey parietal cortex
Falkner, A. L. (2012) · doi:10.7916/d8794bpx
“Using the law of total variance which is derived by subtracting the amount of variability due to spiking from the total measured variance, an analysis of LIP data during a decision making task supports the evidence accumulation hypothesis (Churchland et al., 2011).”
Method
Metadata
- 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)
- PMC
- PMC3066020
- 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
- bronze
- doi.org
- Licence
- 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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