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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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 measuredAnalysis 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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe fast increase in VarCE indicates accumulation of stochastic trial-by-trial diffusion noise.
“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]
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.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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.
“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]
The calculated CorCE matrix exhibits a characteristic increase of correlation as a function of time and, for a given time, shows sequential decay as often observed during decision-making.
“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]
Overall, VarCE exhibits a linear increase during epoch 4, a characteristic signature of a diffusion process consistent with an accumulation-to-bound model.
“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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Cites
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MethodInfluentialSelf-citationResponses of neurons in macaque MT to stochastic motion signals
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“…mean spike count and its near independence from conditions affecting the mean firing rate, such as motion strength, contrast, and attentional state (Britten et al., 1993; Geisler et al., 2001; McAdams and Maunsell, 1999; Tolhurst et al., 1983), imply that f is unlikely to vary systematically as a…”
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“By perturbing the background of random dot motion displays, Huk and Shadlen (2005) demonstrated that LIP neurons represent the integral of motion evidence in the display, but thesemeasurements did not demonstrate that spike trains from individual trials represent realizations of random walk or…”
BackgroundSelf-citationFlexible control of mutual inhibition: a neural model of two-interval discrimination
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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).”
MethodDecisions in Changing Conditions: The Urgency-Gating Model
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“during Decision Making”
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“A constant VarCE is still evident when the firing rate is perturbed by additive noise, as in a doubly stochastic Poisson process (Figure 2D and magenta lines in Figure 2F), also known as a Cox process (Cox and Isham, 1980).”
MethodRefractoriness and Neural Precision
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MethodEffects of Attention on the Reliability of Individual Neurons in Monkey Visual Cortex
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“…near independence from conditions affecting the mean firing rate, such as motion strength, contrast, and attentional state (Britten et al., 1993; Geisler et al., 2001; McAdams and Maunsell, 1999; Tolhurst et al., 1983), imply that f is unlikely to vary systematically as a function of firing rate.”
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“This model would also explain decisions using competing mechanisms (Reddi et al., 2003), although we did not implement this.”
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“Other violations of the renewallike assumption are characterized in Teich et al. (1997), although we suspect some are simply manifestations of VarCE and less exotic than these authors propose.”
BackgroundIndeterminacy in brain and behavior
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“Here, we used a simplified firing-rate version of such a model (Wong et al., 2007).”
BackgroundMethodInfluentialMeasurement of variability dynamics in cortical spike trains
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“Other violations of the renewallike assumption are characterized in Teich et al. (1997), although we suspect some are simply manifestations of VarCE and less exotic than these authors propose.”
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Self-citationMotion direction signals in the primary visual cortex of cat and monkey
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“…near independence from conditions affecting the mean firing rate, such as motion strength, contrast, and attentional state (Britten et al., 1993; Geisler et al., 2001; McAdams and Maunsell, 1999; Tolhurst et al., 1983), imply that f is unlikely to vary systematically as a function of firing…”
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“There is an approximately linear relationship between the mean and variance of the spike counts for neurons in several cortical areas for repetitions of identical stimuli (Britten et al., 1993; Churchland et al., 2006; Geisler and Albrecht, 1995;McAdams andMaunsell, 1999).”
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“The lower average rate is probably not explained by amechanism that invokes less excitation or greater suppression on all trials, owing perhaps to greater uncertainty (Basso and Wurtz, 1998), or normalization (Tolhurst and Heeger, 1997), or surround inhibition (Balan et al., 2008).”
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“The lower average rate is probably not explained by amechanism that invokes less excitation or greater suppression on all trials, owing perhaps to greater uncertainty (Basso and Wurtz, 1998), or normalization (Tolhurst and Heeger, 1997), or surround inhibition (Balan et al., 2008).”
BackgroundThe encoding of alternatives in multiple-choice decision making
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Self-citationNoise and the Visual Threshold
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“It follows that the variability of firing rate across repetitions might bear on the fidelity of these neural signals (Barlow, 1956; Bulmer et al., 1957; Tolhurst et al., 1983).”
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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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“Thesolutionstohid- denfictivestatesinthisdynamiccontextwouldbemorecom-plicatedbutnotcomputationallyintractable denfictivestatesinthisdynamiccontextwouldbemorecom-plicatedbutnotcomputationallyintractable.”
Neural correlates of reliability-based cue weighting during multisensory integration
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Self-citationBump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory
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Discrete attractor dynamics underlies persistent activity in the frontal cortex
Inagaki, H. K., Fontolan, L., Romani, S., & Svoboda, K. (2019) · Nature · doi:10.1038/s41586-019-0919-7 · Cited by 334 · see citing works
Perceptual Decision Making in Rodents, Monkeys, and Humans
Hanks, T. D., & Summerfield, C. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.003 · Cited by 388 · see citing works
Factorial comparison of working memory models.
van den Berg, R., Awh, E., & Ma, W. J. (2014) · Psychological Review · doi:10.1037/a0035234 · Cited by 311 · see citing works
Large-scale neural recordings call for new insights to link brain and behavior
Urai, A. E., Doiron, B., Leifer, A. M., & Churchland, A. K. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00980-9 · Cited by 324 · see citing works
Self-citationSingle-trial spike trains in parietal cortex reveal discrete steps during decision-making
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Behavior needs neural variability
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Decision making by urgency gating: theory and experimental support
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“However, Churchland et al. (2011) recently suggested a method for distinguishing such models at the neural level using analyses of high-frequency noise in neural spike trains.”
MethodSpontaneous and Task-Evoked Brain Activity Negatively Interact
He, B. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2922-12.2013 · Cited by 272 · see citing works
“Further, variability increase might carry different information from variability reduction (Briggman et al., 2005; Churchland et al., 2011).”
BackgroundEncoding and decoding in parietal cortex during sensorimotor decision-making
Park, I. M., Meister, M. L. R., Huk, A. C., & Pillow, J. W. (2014) · Nature Neuroscience · doi:10.1038/nn.3800 · Cited by 264 · see citing works
History-dependent variability in population dynamics during evidence accumulation in cortex
Morcos, A. S., & Harvey, C. D. (2016) · Nature Neuroscience · doi:10.1038/nn.4403 · Cited by 241 · see citing works
“Trials of the same type therefore had distinguishable trajectories of activity patterns and did not converge to similar paths through a small set of clusters, which is consistent with previous studies of variability in the activity of cortical neurons 21,22,27-29 .”
Emotion and decision-making explained: a précis.
Rolls, E. (2013) · Cortex; a journal devoted to the study of the nervous system and behavior · doi:10.1016/j.cortex.2014.01.020 · Cited by 268 · see citing works
“Recently, multiple-choice decision tasks have received increasing attention in the context | 609Overview of different models of decision-making of distinctions between models (Churchland et al. 2011, Ditterich 2010, Leite & Ratcliff 2010, Purcell, Heitz, Cohen, Schall, Logan & Palmeri 2010).”
Precise multimodal optical control of neural ensemble activity
Mardinly, A. R., Oldenburg, I. A., Pégard, N. C., Sridharan, S., Lyall, E. H., Chesnov, K., Brohawn, S. G., Waller, L., & Adesnik, H. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0139-8 · Cited by 268 · see citing works
The population doctrine in cognitive neuroscience
Ebitz, R. B., & Hayden, B. Y. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.011 · Cited by 273 · see citing works
Perceptual decision neurosciences – A model-based review
Mulder, M., van Maanen, L., & Forstmann, B. U. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.07.031 · Cited by 214 · see citing works
A neural mechanism of speed-accuracy tradeoff in macaque area LIP
Hanks, T., Kiani, R., & Shadlen, M. N. (2014) · eLife · doi:10.7554/elife.02260 · Cited by 222 · see citing works
“…of evidence in favor of peripheral choice targets in the RFs of the neurons (Roitman and Shadlen, 2002; Huk and Shadlen, 2005; Kiani et al., 2008; Churchland et al., 2011), and it is supported in the current study by the time of the earliest significant effects of stimulus strength on LIP…”
BackgroundResultInfluentialSelf-citationThe Oxford Handbook of Computational and Mathematical Psychology
Busemeyer, J., Townsend, J., & Wang, Z. (2015) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199957996.001.0001 · Cited by 402 · see citing works
“…provide challenging constraints for low-level spiking neural models (e.g., Lo, Boucher, Paré, Schall, & Wang, 2009), and more generally that considerations of behavioral variability can yield insights into neural processes (e.g., Churchland et al., 2011; Purcell, Heitz, Cohen, & Schall, 2012).”
BackgroundBrain mechanisms for perceptual and reward-related decision-making
Deco, G., Rolls, E. T., Albantakis, L., & Romo, R. (2013) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.01.010 · Cited by 196 · see citing works
“Analyzing higher-order statistical properties of neurophysiological data from their 2-and 4-alternative random dot motion task, Churchland et al. (2008, 2011) were able to distinguish between models categorized by their different sources of variability.”
BackgroundMethodNeural tuning and representational geometry
Kriegeskorte, N., & Wei, X.-X. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00502-3 · Cited by 187 · see citing works
Bridging Neural and Computational Viewpoints on Perceptual Decision-Making
O’Connell, R. G., Shadlen, M. N., Wong‐Lin, K., & Kelly, S. P. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.06.005 · Cited by 214 · see citing works
“For example, in one line of work, single neurons in the monkey lateral intraparietal area (LIP) have been shown to exhibit strongly choice-predictive activity that builds at a rate proportional to physical evidence strength [33,34], linearly grows in variance as more evidence is sampled over time [35], and reaches a s…”
Self-citationThe Basal Ganglia Do Not Select Reach Targets but Control the Urgency of Commitment
Thura, D., & Cisek, P. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.039 · Cited by 195 · see citing works
“It has been suggested that, in addition to firing rates, neural variability across trials can provide further insights about the neural computations underlying deliberation, choice commitment, and action execution (Churchland et al., 2006, 2010, 2011).”
BackgroundResultSummerfield, C., & Tsetsos, K. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00070 · Cited by 167 · see citing works
Neural underpinnings of the evidence accumulator
Brody, C. D., & Hanks, T. D. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.003 · Cited by 184 · see citing works
Representation of accumulating evidence for a decision in two parietal areas
de Lafuente, V., Jazayeri, M., & Shadlen, M. N. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2451-14.2015 · Cited by 162 · see citing works
“We then combined the residuals and their associated means using Equation 6 from the study by Churchland et al. (2011) to estimate the VarCE, S Ni 2 , in the six time bins under the four conditions (area by effector), given .”
BackgroundMethodInfluentialSelf-citationNatural behavior is the language of the brain
Miller, C. T., Gire, D. H., Hoke, K. L., Huk, A. C., Kelley, D. B., Leopold, D. A., Smear, M. C., Theunissen, F. E., Yartsev, M. M., & Niell, C. M. (2022) · Current Biology · doi:10.1016/j.cub.2022.03.031 · Cited by 170 · see citing works
Neural Mechanisms of Post-error Adjustments of Decision Policy in Parietal Cortex
Purcell, B. A., & Kiani, R. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.027 · Cited by 152 · see citing works
“Bottom: 95% confidence interval (CI) around the post-error minus post-correct VarCE.”
InfluentialSelf-citationPosterior Parietal Cortex Guides Visual Decisions in Rats
Licata, A., Kaufman, M. T., Raposo, D. C., Ryan, M., Sheppard, J. P., & Churchland, A. K. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0105-17.2017 · Cited by 148 · see citing works
“We speculate that the sharper decrease in the VarCE seen on visual decisions (Figure 5E, blue) may reflect the dual stabilizing influences of visual sensory input and action planning feedback, while the slower and less deep decrease of the VarCE on auditory decisions (Figure 5E, green) may reflect only a single stabil…”
BackgroundMethodInfluentialSelf-citationHistory-based action selection bias in posterior parietal cortex
Hwang, E. J., Dahlen, J. E., Mukundan, M., & Komiyama, T. (2017) · Nature Communications · doi:10.1038/s41467-017-01356-z · Cited by 152 · see citing works
Ito, T., Hearne, L. J., & Cole, M. W. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117141 · Cited by 154 · see citing works
Neural dynamics and circuit mechanisms of decision-making
Wang, X. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.08.006 · Cited by 136 · see citing works
Self-citationZhao, Y., & Park, I. M. (2017) · Neural Computation · doi:10.1162/neco_a_00953 · Cited by 110 · see citing works
“In addition, it is difficult to disambiguate different possible neural implementations of computation from the average trajectory since they may only differ in their trial-to-trial variability [6, 21].”
BackgroundThura, D., Cos, I., Trung, J., & Cisek, P. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0162-14.2014 · Cited by 109 · see citing works
“The predominant model of SAT in decision-making is the “drift-diffusion model,” which suggests that decisions involve a slow accumulation of sensory information until a fixed accuracy criterion is reached (Gold and Shadlen, 2007; Ratcliff and McKoon, 2008; Churchland et al., 2011).”
BackgroundMethodThe mouse posterior parietal cortex: Anatomy and functions.
Lyamzin, D., & Benucci, A. (2019) · Neurosciences research · doi:10.1016/j.neures.2018.10.008 · Cited by 117 · see citing works
Making Decisions with Unknown Sensory Reliability
Denève, S. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00075 · Cited by 87 · see citing works
Oscillatory Brain State Predicts Variability in Working Memory
Myers, N. E., Stokes, M. G., Walther, L., & Nobre, A. C. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4741-13.2014 · Cited by 108 · see citing works
“Possibly, top-down attention constrains the variability in firing rate (Churchland et al., 2010; Cohen and Maunsell, 2010; Churchland et al., 2011), leading to increased encoding precision.”
BackgroundNoise and Correlations in Parallel Perceptual Decision Making
Otto, T. U., & Mamassian, P. (2012) · Current Biology · doi:10.1016/j.cub.2012.05.031 · Cited by 110 · see citing works
“Regarding our modeling approach using the LATER model [12, 13], we should note, however, that its simplistic nature is not compatible with the variance of neurons involved in decision making [37].”
Marcos, E., Pani, P., Brunamonti, E., Deco, G., Ferraina, S., & Verschure, P. F. M. J. (2013) · Neuron · doi:10.1016/j.neuron.2013.02.006 · Cited by 92 · see citing works
“Estimation of Neural Variability To calculate the across-trial variability of the neural response, we follow the method in Churchland et al. (2011) in which the total calculated variance is approximated as the sum of the VarCE and the point process variance (PPV).”
BackgroundMethodInfluentialScaling Properties of Dimensionality Reduction for Neural Populations and Network Models
Williamson, R. C., Cowley, B. R., Litwin-Kumar, A., Doiron, B., Kohn, A., Smith, M. A., & Yu, B. M. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005141 · Cited by 100 · see citing works
No Evidence for an Item Limit in Change Detection
Keshvari, S., van den Berg, R., & Ma, W. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002927 · Cited by 100 · see citing works
“Recent evidence points in this direction [54–55], although formal model comparison remains to be done.”
ResultPrinciples of Multisensory Behavior
Otto, T. U., Dassy, B., & Mamassian, P. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4678-12.2013 · Cited by 110 · see citing works
“Moreover, the LATER model appears too simple to be consistent with physiological recordings of neurons involved in decision making (Churchland et al., 2011).”
MethodNeural Correlation Is Stimulus Modulated by Feedforward Inhibitory Circuitry
Middleton, J. W., Omar, C., Doiron, B., & Simons, D. J. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3474-11.2012 · Cited by 85 · see citing works
“Variability is not simply detrimental “noise” that interferes with cognition and sensory processing, but instead is intimately connected with behavioral output (M. M. Churchland et al., 2006; A. K. Churchland et al., 2011).”
BackgroundModels and processes of multisensory cue combination
Seilheimer, R. L., Rosenberg, A., & Angelaki, D. E. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.11.008 · Cited by 105 · see citing works
Population-wide distributions of neural activity during perceptual decision-making
Wohrer, A., Humphries, M. D., & Machens, C. K. (2012) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.09.004 · Cited by 89 · see citing works
“nature of the underlying neural computations (Churchland et al., 2011).”
Neural dynamics of choice: Single-trial analysis of decision-related activity in parietal cortex
Bollimunta, A., Totten, D., & Ditterich, J. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5752-11.2012 · Cited by 83 · see citing works
“In contrast, Roitman and Shadlen (2002) and Churchland et al. (2011) have provided indirect arguments for LIP neurons undergoing a gradual firing rate change.”
BackgroundSelf-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses
Ocker, G. K., Litwin-Kumar, A., & Doiron, B. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004458 · Cited by 84 · see citing works
New advances in understanding decisions among multiple alternatives
Churchland, A. K., & Ditterich, J. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.04.009 · Cited by 72 · see citing works
Self-citationForaging as an evidence accumulation process
Davidson, J. D., & Hady, A. E. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007060 · Cited by 91 · see citing works
Coherent chaos in a recurrent neural network with structured connectivity
Landau, I., & Sompolinsky, H. (2018) · bioRxiv · doi:10.1371/journal.pcbi.1006309 · Cited by 82
“Firing-rate fluctuations and irregular spiking are ubiquitous in the neocortex [1, 2].”
BackgroundNeural Variability Quenching Predicts Individual Perceptual Abilities
Arazi, A., Censor, N., & Dinstein, I. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1671-16.2016 · Cited by 92 · see citing works
“…or extracellular recordings and regardless of whether the animal is performing a task, passively awake, or anesthetized (Monier et al., 2003; Finn et al., 2007; Mitchell et al., 2007; Churchland et al., 2010, 2011; Hussar and Pasternak, 2010; Ledberg et al., 2012; Qi and Constantinidis, 2012).”
BackgroundPrecursor processes of human self-initiated action
Khalighinejad, N., Schurger, A., Desantis, A., Zmigrod, L., & Haggard, P. (2017) · bioRxiv · doi:10.1016/j.neuroimage.2017.09.057 · Cited by 79 · see citing works
“Measurement of inter-trial variability has been extensively used in the analysis of neural data (Averbeck and Lee, 2003; Churchland et al., 2011, 2010, 2006; He, 2013; Saberi-Moghadam et al., 2016; Schurger et al., 2015).”
Competing Neural Ensembles in Motor Cortex Gate Goal-Directed Motor Output
Zagha, E., Ge, X., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.044 · Cited by 78 · see citing works
Responses of primate frontal cortex neurons during natural vocal communication
Miller, C. T., Thomas, A. W., Nummela, S. U., & de la Mothe, L. A. (2015) · Journal of Neurophysiology · doi:10.1152/jn.01003.2014 · Cited by 82 · see citing works
“Previous studies of NHP cortical neurons reported that decreases in the variability of neural activity can be a meaningful correlate of important behavioral events (Churchland et al. 2011; Mitchell et al. 2009).”
BackgroundNeural Correlates of Optimal Multisensory Decision MakingPreprint
Hou, H., Zheng, Q., Zhao, Y., Pouget, A., & Gu, Y. (2018) · bioRxiv · doi:10.1101/480178 · Cited by 76 · see citing works
“On the contrary, this step would introduce temporal correlations (Churchland et al., 2011), in which case, even with ilPPC, a simple sum of the momentary evidence would no longer be optimal (Bogacz et al.”
BackgroundSelf-citationThe Computational and Neural Basis of Rhythmic Timing in Medial Premotor Cortex
Merchant, H., & Averbeck, B. B. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0367-17.2017 · Cited by 85 · see citing works
“…2002; Simen et al., 2011, 2016) as we have used here; (2) diffusion models where the response bound changes to produce different intervals (Churchland et al., 2011); (3) biophysical state-dependent networks, where time is encoded in the current state of recurrent networks (Buonomano,…”
MethodPropagating Waves Can Explain Irregular Neural Dynamics
Keane, A., & Gong, P. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1669-14.2015 · Cited by 74 · see citing works
“These transitions appear to coexist with variable spike timing of individual neurons, which is a characteristic feature of a doubly stochastic process (Churchland et al., 2011; Churchland and Abbott, 2012; Litwin-Kumar and Doiron, 2012).”
BackgroundInfluentialBeaman, C. B., Eagleman, S. L., & Dragoi, V. (2017) · Nature Communications · doi:10.1038/s41467-017-01030-4 · Cited by 74 · see citing works
Probabilistic Computation in Human Perception under Variability in Encoding Precision
Keshvari, S., van den Berg, R., & Ma, W. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040216 · Cited by 60 · see citing works
“Recent physiological studies have reported evidence for doubly stochastic processes in cortex [12,13,14,15].”
BackgroundOptimizing Working Memory with Heterogeneity of Recurrent Cortical Excitation
Kilpatrick, Z. P., Ermentrout, B., & Doiron, B. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1641-13.2013 · Cited by 61 · see citing works
“However, how exactly neural variability drives behavioral variability is largely unknown (Britten et al., 1996; Churchland et al., 2011; Brunton et al., 2013; Haefner et al., 2013).”
BackgroundWinkel, J., Keuken, M. C., van Maanen, L., Wagenmakers, E., & Forstmann, B. U. (2014) · Psychonomic Bulletin & Review · doi:10.3758/s13423-013-0551-8 · Cited by 45 · see citing works
“Such incoming bounds are also consistent with findings of increased overall activation levels across groups of neurons with different response selectivities (Churchland, Kiani, & Shadlen, 2008; Drugowitsch et al., 2012). One reason why it is important to correctly formulate decision-making models is that they help to…”
MethodResultInfluentialThe Role of the Lateral Intraparietal Area in (the Study of) Decision Making
Huk, A. C., Katz, L. N., & Yates, J. L. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031508 · Cited by 72 · see citing works
“Otherwise, such finer-grained analyses of the statistics of LIP responses may reflect differential dynamics that are in part due to sensory responses, presaccadic activity, or both (see also Churchland et al. 2011).”
BackgroundReproducibility of in vivo electrophysiological measurements in micePreprint
Banga, K., Benson, J., Bhagat, J., Biderman, D., Birman, D., Bonacchi, N., Bruijns, S. A., Buchanan, K. L., Campbell, R. A. A., Carandini, M., et al. (2024) · eLife · doi:10.7554/elife.100840 · Cited by 18 · see citing works
Self-citationStochastic transitions into silence cause noise correlations in cortical circuits
Mochol, G., Hermoso-Mendizabal, A., Sakata, S., Harris, K. D., & de la Rocha, J. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1410509112 · Cited by 62 · see citing works
What is value—accumulated reward or evidence?
Friston, K. J., Adams, R. A., & Montague, P. R. (2012) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2012.00011 · Cited by 64 · see citing works
Cortical feedback loops bind distributed representations of working memory
Voitov, I., & Mrsic‐Flogel, T. D. (2022) · Nature · doi:10.1038/s41586-022-05014-3 · Cited by 86 · see citing works
Direct observation of the neural computations underlying a single decisionPreprint
Steinemann, N. A., Stine, G. M., Trautmann, E. M., Zylberberg, A., Wolpert, D., & Shadlen, M. (2024) · bioRxiv · doi:10.1101/2022.05.02.490321 · Cited by 60 · see citing works
“…the signal of interest is dominated by a small subpopulation of neurons with response fields that overlap one of the choice targets, consistent with earlier single neuron studies ( e.g., Shadlen and Newsome, 1996 ; Roitman and Shadlen, 2002 ; Churchland et al., 2011 ; Gold and Shadlen, 2007 )”
MethodResultSelf-citationThe impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs
Mendonça, A. G., Drugowitsch, J., Vicente, M. I., DeWitt, E. E. J., Pouget, A., & Mainen, Z. F. (2020) · Nature Communications · doi:10.1038/s41467-020-16196-7 · Cited by 54 · see citing works
Self-citationSpontaneous Neural Fluctuations Predict Decisions to Attend
Bengson, J. J., Kelley, T. A., Zhang, X., Wang, J.-L., & Mangun, G. R. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00650 · Cited by 67 · see citing works
Legenstein, R. A., & Maass, W. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003859 · Cited by 54 · see citing works
“Furthermore, we show in Text S4 that the model is consistent with features of neuronal activity during random-dot motion tasks [1,26,27].”
MethodPredicting the Accuracy of a Decision: A Neural Mechanism of ConfidencePreprint
Fetsch, C. R., Kiani, R., & Shadlen, M. N. (2014) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2014.79.024893 · Cited by 54 · see citing works
“In the lateral intraparietal area (LIP), this activity reflects not only the mean (Roitman and Shadlen 2002; Huk and Shadlen 2005; Kiani et al. 2008) but also the variance and autocorrelation (Churchland et al. 2011) of an evolving DV that explains choice and RT.”
Self-citationvan Vugt, M. K., Simen, P., Nystrom, L. E., Holmes, P. J., & Cohen, J. D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090943 · Cited by 50 · see citing works
“, [5–7]), human magnetoencephalography (e.”
Categorization=decision making+generalization
Seger, C. A., & Peterson, E. J. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.03.015 · Cited by 59 · see citing works
“Activity in LIP in the coherent dot motion perception task is well characterized by accumulator models (Churchland et al., 2011; Gold et al., 2010; Gold and Shadlen, 2007).”
BackgroundVariability of Prefrontal Neuronal Discharges before and after Training in a Working Memory Task
Qi, X.-L., & Constantinidis, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041053 · Cited by 54 · see citing works
“Conversely, the variance of firing rate was shown to increase in the course of decision making, and as a function of the number of alternative choices [14].”
BackgroundArazi, A., Gonen-Yaacovi, G., & Dinstein, I. (2017) · eNeuro · doi:10.1523/eneuro.0292-17.2017 · Cited by 62 · see citing works
“Note that certain tasks, such as perceptual decision-making, may generate an increase in neural variability before the decision is made (Churchland et al., 2011).”
BackgroundSpike-field activity in parietal area LIP during coordinated reach and saccade movements
Hagan, M. A., Dean, H. L., & Pesaran, B. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00867.2011 · Cited by 51 · see citing works
“Area LIP also carries spatial cognitive signals related to perceptual and economic decision-making (Churchland et al. 2011; Dorris and Glimcher 2004; Gold and Shadlen 2007; Kiani and Shadlen 2009; Louie and Glimcher 2010; Louie et al. 2011; Platt and Glimcher 1999; Roitman and Shadlen 2002; Seo et…”
BackgroundEvidence against perfect integration of sensory information during perceptual decision making
Carland, M. A., Marcos, E., Thura, D., & Cisek, P. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00264.2015 · Cited by 42 · see citing works
“…filter with a short time constant is capable of simulating the effects of early motion signals, even on RT distributions whose means are 1,000 ms. Churchland et al. (2011) suggested that the UGM cannot explain correlations in neural activity, but those authors also did not include the low-pass…”
BackgroundThe impact of MRI scanner environment on perceptual decision-making
van Maanen, L., Forstmann, B. U., Keuken, M. C., Wagenmakers, E., & Heathcote, A. (2015) · Behavior Research Methods · doi:10.3758/s13428-015-0563-6 · Cited by 58 · see citing works
“The increase in drift rate difference with coherence is consistent with the literature (Churchland et al., 2011; Donkin et al., 2009; Ho et al., 2009; Mulder et al., 2013; Palmer et al., 2005).”
MethodResultAccumulators, Neurons, and Response Time
Schall, J. D. (2019) · Trends in Neurosciences · doi:10.1016/j.tins.2019.10.001 · Cited by 71 · see citing works
“Certainly, discharge rates of single neurons wax and wane with variable rates; a measure of the variance of spike rates across trials increases over time as expected if the process were similar to Brownian motion [54].”
BackgroundScale (in)variance in a unified diffusion model of decision making and timing.
Simen, P., Vlasov, K. Y., & Papadakis, S. (2015) · Psychological Review · doi:10.1037/rev0000014 · Cited by 54 · see citing works
A Bayesian Attractor Model for Perceptual Decision Making
Bitzer, S., Bruineberg, J. P., & Kiebel, S. J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004442 · Cited by 40 · see citing works
Goldberg, J. A., & Bergman, H. (2011) · Neuroscience · doi:10.1016/j.neuroscience.2011.08.068 · Cited by 49 · see citing works
Evidence accumulation relates to perceptual consciousness and monitoring
Pereira, M., Mégevand, P., Tan, M. X., Chang, W., Wang, S., Rezai, A. R., Seeck, M., Corniola, M. V., Momjian, S., Bernasconi, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23540-y · Cited by 102 · see citing works
Ongoing, rational calibration of reward-driven perceptual biases
Fan, Y., Gold, J. I., & Ding, L. (2018) · bioRxiv · doi:10.1101/242651 · Cited by 53
Piercing of Consciousness as a Threshold-Crossing Operation
Kang, Y. H., Petzschner, F. H., Wolpert, D. M., & Shadlen, M. N. (2017) · Current Biology · doi:10.1016/j.cub.2017.06.047 · Cited by 87 · see citing works
“Here, there is substantial evidence that decision termination is mediated by application of a threshold to the neural representation of accumulating evidence [8, 46, 47], and this also holds in experiments that do not allow for free responses, as in our controlled-duration experiment [13, 16, 48].”
Self-citationDePasquale, B., Sussillo, D., Abbott, L. F., & Churchland, M. M. (2023) · Neuron · doi:10.1016/j.neuron.2022.12.007 · Cited by 57 · see citing works
Two layers of neural variability
Churchland, M., & Abbott, L. (2012) · Nature Neuroscience · doi:10.1038/nn.3247 · Cited by 52 · see citing works
Chang, M. H., Armstrong, K. M., & Moore, T. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2967-11.2012 · Cited by 47 · see citing works
“…changes in response variability have been observed as a correlate of active behavioral states (M. M. Churchland et al., 2006; Mitchell et al., 2007; Cohen and Maunsell, 2009; Steinmetz and Moore, 2010; A. K. Churchland et al., 2011), suggesting that variability may reflect behavioral engagement.”
BackgroundMiller, P. C., & Katz, D. B. (2013) · Journal of Computational Neuroscience · doi:10.1007/s10827-013-0452-x · Cited by 46 · see citing works
“2009) and electrophysiological (Leon and Shadlen 2003; Janssen and Shadlen 2005; Genovesio et al. 2006; Mita et al. 2009; Churchland et al. 2011) evidence—further improves the advantage of the nonlinear system over a perfect integrator.”
BackgroundResidual dynamics resolves recurrent contributions to neural computation
Galgali, A. R., Sahani, M., & Mante, V. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01230-2 · Cited by 43 · see citing works
“Our analysis of residual dynamics extends previous work that leveraged trial-by-trial variability to understand neural computations 19,20,22,23,41 , by providing a full, quantitative description of the time-varying dynamics of population-level trial-by-trial variability.”
Background
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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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