Neural dynamics of choice: Single-trial analysis of decision-related activity in parietal cortex
Open accessAbstract
Previous neurophysiological studies of perceptual decision-making have focused on single-unit activity, providing insufficient information about how individual decisions are accomplished. For the first time, we recorded simultaneously from multiple decision-related neurons in parietal cortex of monkeys performing a perceptual decision task and used these recordings to analyze the neural dynamics during single trials. We demonstrate that decision-related lateral intraparietal area neurons typically undergo gradual changes in firing rate during individual decisions, as predicted by mechanisms based on continuous integration of sensory evidence. Furthermore, we identify individual decisions that can be described as a change of mind: the decision circuitry was transiently in a state associated with a different choice before transitioning into a state associated with the final choice. These changes of mind reflected in monkey neural activity share similarities with previously reported changes of mind reflected in human behavior.
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Streak index analysis showed randomness of local spike count fluctuations with close to zero mean (0.06 ± 1.36, mean ± SD).
“streak index analysis (52) performed on a trial-by-trial basis (53) (SI Appendix, Fig. S10D, Materials and Methods), that showed randomness of local spike count fluctuations with close to zero mean (0.06 ± 1.36, mean ± SD, Fig. 4K)”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [53]
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BackgroundDecoding and perturbing decision states in real time
Peixoto, D., Verhein, J. R., Kiani, R., Kao, J. C., Nuyujukian, P., Chandrasekaran, C., Brown, J., Fong, S., Ryu, S. I., Shenoy, K. V., et al. (2021) · Nature · doi:10.1038/s41586-020-03181-9 · Cited by 120 · see citing works
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Response times from ensembles of accumulators
Zandbelt, B. B., Purcell, B. A., Palmeri, T. J., Logan, G. D., & Schall, J. D. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310577111 · Cited by 60 · see citing works
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Brinkman, B. A. W., Yan, H., Maffei, A., Park, I. M., Fontanini, A., Wang, J., & La Camera, G. (2022) · Applied Physics Reviews · doi:10.1063/5.0062603 · Cited by 64 · see citing works
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BackgroundThe Behavioral Relevance of Cortical Neural Ensemble Responses Emerges Suddenly
Sadacca, B. F., Mukherjee, N., Vladusich, T., Li, J. X., Katz, D. B., & Miller, P. C. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2265-15.2016 · Cited by 88 · see citing works
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BackgroundMethodItinerancy between attractor states in neural systems
Miller, P. C. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.05.005 · Cited by 58 · see citing works
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“Such ‘firing rate-state’ models have been successful in modeling both choice related hidden variables and the dynamics of perceptual processes (Seidemann et al., 1996, Jones et al., 2007, Kemere et al., 2008, Bollimunta et al., 2012, Deco et al., 2013, Moran and Katz, 2014).”
On second thoughts: changes of mind in decision-making
Stone, C., Mattingley, J. B., & Rangelov, D. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.02.004 · Cited by 57 · see citing works
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“We also could extend the stepping model to a general hidden Markov model, allowing for more than three discrete firing rates, with more flexible transition dynamics that allow for more than one transition per trial (Bollimunta et al., 2012).”
Latimer, K. W., Yates, J. L., Meister, M. L. R., Huk, A. C., & Pillow, J. W. (2016) · Science · doi:10.1126/science.aad3596 · Cited by 11 · see citing works
Self-citationMoving beyond generalization to accurate interpretation of flexible models
Genkin, M., & Engel, T. A. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00242-6 · Cited by 38 · see citing works
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The dynamics and geometry of choice in premotor cortex
Genkin, M., Shenoy, K., Chandrasekaran, C., & Engel, T. A. (2023) · bioRxiv · doi:10.1101/2023.07.22.550183 · Cited by 36
“The neural representation of the decision variable remains unknown since its unique trajectories on single trials are not observable 10,11 , and decision-related responses of cortical neurons are complex and heterogeneous 15,21,22 .”
InfluentialShushruth, S., Mazurek, M. E., & Shadlen, M. N. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0668-18.2018 · Cited by 45 · see citing works
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“Such experiments are just starting to be reported (Bollimunta et al., 2012), and more results from this enterprise are eagerly anticipated.”
BackgroundInfluentialUrgency, leakage, and the relative nature of information processing in decision-making.
Trueblood, J. S., Heathcote, A., Evans, N. J., & Holmes, W. R. (2020) · Psychological Review · doi:10.1037/rev0000255 · Cited by 57 · see citing works
Brittleness in model selection analysis of single neuron firing ratesPreprint
Chandrasekaran, C., Soldado-Magraner, J., Peixoto, D., Newsome, W. T., Shenoy, K. V., & Sahani, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/430710 · Cited by 20 · see citing works
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BackgroundMotor-like neural dynamics in two parietal areas during arm reaching
Diomedi, S., Vaccari, F. E., Galletti, C., Hadjidimitrakis, K., & Fattori, P. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102116 · Cited by 21 · see citing works
“Starting from a spike count for each cell and each trial (2-ms bins) aligned all at the same event, in order to generate an emission sequence, we assigned one symbol to each bin (Bollimunta et al., 2012; Mazurek et al., 2018).”
MethodPopulation dynamics of choice representation in dorsal premotor and primary motor cortexPreprint
Peixoto, D., Kiani, R., Chandrasekaran, C., Ryu, S. I., Shenoy, K. V., & Newsome, W. T. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/283960 · Cited by 14 · see citing works
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BackgroundLang, L., Camera, G. L., & Fontanini, A. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010865 · Cited by 15 · see citing works
Urgency, Leakage, and the Relative Nature of Information Processing in Decision-making
Trueblood, J., Heathcote, A., Evans, N., & Holmes, W. (2019) · bioRxiv · doi:10.1101/706291 · Cited by 17
Neural Correlates of State- and Strength-based Perception
Aly, M., Ranganath, C., & Yonelinas, A. P. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00532 · Cited by 15 · see citing works
Neural population dynamics underlying evidence accumulation in multiple rat brain regions
DePasquale, B. D., Brody, C., & Pillow, J. (2021) · bioRxiv · doi:10.1101/2021.10.28.465122 · Cited by 16
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BackgroundPerception of opposite-direction motion in random dot kinematograms
Bae, G., & Luck, S. J. (2022) · Visual Cognition · doi:10.1080/13506285.2022.2052216 · Cited by 16 · see citing works
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MethodNelson, M. J., Murthy, A., & Schall, J. D. (2016) · Journal of Neurophysiology · doi:10.1152/jn.01023.2014 · Cited by 14 · see citing works
Unifying and generalizing models of neural dynamics during decision-makingPreprint
Zoltowski, D. M., Pillow, J. W., & Linderman, S. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.04571 · Cited by 4 · see citing works
“One approach to address this question is to formulate models of decision-making behavior as generative models of neural activity (Dit-terich, 2006; Bollimunta et al., 2012; Latimer et al., 2015; Zoltowski et al., 2019).”
BackgroundWhat can neuronal populations tell us about cognition?
Arandia-Romero, I., Nogueira, R., Mochol, G., & Moreno‐Bote, R. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.07.008 · Cited by 8 · see citing works
Dynamics of Multiple-Choice Decision Making
You, H., & Wang, D. (2013) · Neural Computation · doi:10.1162/neco_a_00473 · Cited by 11 · see citing works
The importance of urgency in decision making based on dynamic information
Ferrucci, L., Genovesio, A., & Marcos, E. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009455 · Cited by 9 · see citing works
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Comparing models of contrast gain using psychophysical experiments
DiMattina, C. (2016) · Journal of Vision · doi:10.1167/16.9.1 · Cited by 12 · see citing works
A doubly stochastic renewal framework for partitioning spiking variability
Aghamohammadi, C., Chandrasekaran, C., & Engel, T. A. (2025) · Nature Communications · doi:10.1038/s41467-025-63821-4 · Cited by 8 · see citing works
“Finally, we describe our observations of spiking irregularity in several cortical areas,whichrevealsystematicdifferencesfromsensorytomotorareas and indicate that Poisson irregularity is a rare exception, not a rule.”
Latimer, K. W., Huk, A. C., & Pillow, J. W. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/160994 · Cited by 6 · see citing works
“The hypothetical signal that is posited to stop accumulation on the observed neuron’s Tout trials has never been identified in these experiments, nor have the “winning” and “losing” accumulator responses been simultaneously measured and directly tied into this model, although such datasets are now possible to acquire…”
BackgroundInfluentialVariable Statistical Structure of Neuronal Spike Trains in Monkey Superior Colliculus
Cho, S.-H., Crapse, T. B., Grimaldi, P., Lau, H. C., & Basso, M. A. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1491-20.2021 · Cited by 9 · see citing works
“, 2015), and the streak index (SI) (Horwitz and Newsome, 2001; Bollimunta et al., 2012).”
BackgroundMethodResultInfluentialIntegrating somatosensory information over time
Hernández-Pérez, R., Rojas-Hortelano, E., & de Lafuente, V. (2019) · bioRxiv · doi:10.1101/817262 · Cited by 5
Cortical and subcortical contributions to state- and strength-based perceptual judgments
Aly, M., Wansard, M., Segovia, F., Yonelinas, A. P., & Bastin, C. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.09.025 · Cited by 6 · see citing works
“…decision-making tasks, and activity in these neurons has been related to continuously-graded integration of sensory evidence (e.g., Shadlen and Newsome, 2001; Bollimunta et al., 2012; see Gold and Shadlen, 2007) as well as the degree of confidence in perceptual decisions (Kiani and Shadlen, 2009).”
BackgroundInfluentialKirchherr, S., Moraga, S. M., Coudé, G., Bimbi, M., Ferrari, P. F., Aarts, E., & Bonaiuto, J. J. (2023) · European Journal of Neuroscience · doi:10.1111/ejn.16065 · Cited by 3 · see citing works
“Classic frequentist HMMs applied to neural data have used a univariate multinomial distribution for emission probabilities (Bollimunta et al., 2012; Mazurek et al., 2018; Diomedi et al., 2021), encoding which of N neurons is spiking in each time bin, and choosing a random neuron if multiple neurons spike in the same b…”
MethodThe Encoding of Decision Difficulty and Movement Time in the Primate Premotor Cortex
Martínez-Garcia, M., Insabato, A., Pannunzi, M., Pardo‐Vázquez, J. L., Acuña, C., & Deco, G. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004502 · Cited by 5 · see citing works
“Recently, [36] reliable evidence has been provided for the hypothesis that LIP neurons display a gradual ramp.”
BackgroundAccumulation of Evidence in Decision Making
Huk, A. C., Katz, L. N., & Yates, J. L. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_309-2 · Cited by 3 · see citing works
Li, T.-S., & Camera, G. L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325979 · Cited by 3 · see citing works
“These results somewhat justify the use of a small number of initial guesses seen in previous works, sometimes as small as 5 or 10 [12,13,15,17,32,35].”
The dynamics and geometry of choice in the premotor cortex
Genkin, M., Shenoy, K. V., Chandrasekaran, C., & Engel, T. A. (2025) · Nature · doi:10.1038/s41586-025-09199-1 · Cited by 40 · see citing works
“The neural representation of the decision variable remains unknown as its unique trajectories on single trials are not observable 3,4 , and decision-related responses of cortical neurons are complex and heterogeneous 8,11,20 .”
InfluentialNeural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks
Kim, C. M., Chow, C., & Averbeck, B. (2024) · bioRxiv · doi:10.1101/2024.09.14.613033 · Cited by 3
“In particular, attractor dynamics, in which the network state moves towards discrete [9, 16] or along continuous [8, 17] attractor states, have been studied extensively as a potential neural mechanism for decision-making and evidence accumulation [18, 19].”
Rate fluctuations not steps dominate LIP activity during decision-makingPreprint
Zhao, X., & Kording, K. P. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/249672 · Cited by 2 · see citing works
“For RT trials integration activity is assumed to begin from 200 ms after stimulus onset and terminates 50 ms before saccade initiation, following (Bollimunta et al., 2012), where we use target acquisition time as a proxy for saccade initiation.”
MethodCapturing the temporal evolution of choice across prefrontal cortex
Hunt, L. T., Behrens, T. E. J., Hosokawa, T., Wallis, J. D., & Kennerley, S. W. (2015) · eLife · doi:10.7554/elife.11945 · Cited by 74 · see citing works
“Several recent studies have recovered single-trial dynamical information in structures close to motor output, based upon neuronal spiking data (Thura and Cisek, 2014; Kaufman et al., 2015; Murakami et al., 2014; Bollimunta et al., 2012; Kiani et al., 2014; Carnevale et al., 2015).”
Second Guessing in Perceptual Decision-Making
McLean, C. S., Ouyang, B., & Ditterich, J. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2787-19.2020 · Cited by 3 · see citing works
“They can explain the 50 distribution of choices and associated response times (RTs) for a wide range of decision tasks (Ratcliff 51 and Smith, 2004) and are consistent with decision-related neural activity, both averaged across trials as 52 well as on a single-trial level (Ditterich, 2006; Bollimunta et al., 2012).”
MethodSelf-citationBeyond generalization: Enhancing accurate interpretation of flexible modelsPreprint
Genkin, M., & Engel, T. A. (2019) · bioRxiv · doi:10.1101/808261 · Cited by 2 · see citing works
Distinguishing Between Models of Perceptual Decision Making
Ditterich, J. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2236-9_13 · Cited by 2 · see citing works
“When monkeys perform a random-dot motion direction discrimination task and report the choice with a goal-directed eye movement, the activity of neurons in the lateral intraparietal area (LIP) in parietal cortex reflects the ongoing decision [1, 18, 19] and has even been shown to have a causal effect on the outcome of…”
BackgroundSelf-citationBayesian inference for latent stepping and ramping models of spike train data
Latimer, K. W., Huk, A., & Pillow, J. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781139941433.008 · Cited by 2 · see citing works
“One recent study focused on model comparison for spike trains recorded in macaque lateral intraparietal cortex (LIP), a putative decision-making area (Bollimunta, Totten & Ditterich 2012).”
BackgroundNeural correlates of perceptual decision-making in the primary somatosensory cortex
Armstrong, A. G., & Vlasov, Y. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2514107123 · Cited by 1 · see citing works
“Streak index analysis (52, 53) also supports this interpretation.”
Neural Correlates of Perceptual Decision Making in Primary Somatosensory Cortex
Armstrong, A. G., & Vlasov, Y. D. (2025) · bioRxiv · doi:10.1101/2025.03.29.646003 · Cited by 1
“Streak index analysis 50,51 also supports this interpretation.”
InfluentialKurikawa, T., & Kaneko, K. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108381
Testing Optimal Timing in Value-Linked Decision Making
Bhui, R. (2019) · Computational Brain & Behavior · doi:10.1007/s42113-019-0025-9 · Cited by 28 · see citing works
Benchmarking 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
“To overcome this limitation in an unsupervised manner, methods like Hidden Markov Models 19,70–73 offer a powerful alternative by segmenting neural time series into distinct temporal states.”
Moxon, K. A., Moghbel, A. N., Kong, Z., Roadarmel, A., Tran, V., Ditterich, J., Sáez, I., & Peters, L. M. (2025) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf67917.2025.11443813
Self-citationNeural dynamics of reversal learning in the prefrontal cortex and recurrent neural networksPreprint
Kim, C. M., Chow, C. C., & Averbeck, B. B. (2024) · eLife · doi:10.7554/elife.103660.3 · Cited by 2 · see citing works
“…during the trial made the line attractor model, which requires the network state to stay close to attractor states ( Inagaki et al., 2019 ; Luo et al., 2023 ; Seung, 1996 ; Bollimunta et al., 2012 ), inadequate for explaining the neural activity encoding evidence accumulation in reversal learning.”
Spatial 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
“…sequence of units (Baeg et al., 2003; Fujisawa et al., 2008; Harvey et al., 2012; Morcos & Harvey, 2016; Rajan et al., 2016), sequence of patterns (Bollimunta et al., 2012; Goldman, 2009; Wehr & Laurent, 1996), and combinations thereof (M. M. Churchland et al., 2012; Kaufman, Churchland, Ryu, &…”
BackgroundOn the neuronal systems underlying perceptual decision-making and confidence in humans
Hebart, M. N. (2014) · edoc Publication server (Humboldt University of Berlin) · doi:10.18452/16924
“A strong research focus lies on the lateral intraparietal area (LIP) of the macaque monkey (Shadlen and Newsome, 2001; Roitman and Shadlen, 2002; Hanks et al., 2006; Churchland et al., 2008; Kiani and Shadlen, 2009; Bollimunta et al., 2012), but as pointed out above, current evidence is not conclusive regarding the ge…”
BackgroundResultInfluential
- Authors
- Bollimunta, A., Totten, D., & Ditterich, J.
- Affiliations
- University of California, Davis (US)
- ORCID
- Ditterich, Jochen
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2012-09-12
- Volume
- 32
- Issue
- 37
- Pages
- 12684-12701
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6703806
- OpenAlex
- W2017410988
- Cited by
- 83
- References
- 40
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 4 · 2023: 3 · 2022: 7 · 2021: 6 · 2020: 5 · 2019: 4 · 2018: 6 · 2017: 3 · 2016: 8 · 2015: 9 · 2014: 11 · 2013: 7 · 2012: 1
- FWCI
- 4.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Air Force Research Laboratory (FA9550-07-1-0537, FA9550)
- 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, parietal cortex, lateral intraparietal area, change of mind, choice
- MeSH terms
- Animals, Choice Behavior, Computer Simulation, Female, Macaca mulatta, Male, Models, Neurological, Motion Perception, Nerve Net, Parietal Lobe, Models, Statistical
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