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Attractor dynamics reflect decision confidence in macaque prefrontal cortex

Nature Neuroscience volume 26, pages 1970–1980 (2023)Cite this article

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Abstract

Decisions are made with different degrees of consistency, and this consistency can be linked to the confidence that the best choice has been made. Theoretical work suggests that attractor dynamics in networks can account for choice consistency, but how this is implemented in the brain remains unclear. Here we provide evidence that the energy landscape around attractor basins in population neural activity in the prefrontal cortex reflects choice consistency. We trained two rhesus monkeys to make accept/reject decisions based on pretrained visual cues that signaled reward offers with different magnitudes and delays to reward. Monkeys made consistent decisions for very good and very bad offers, but decisions were less consistent for intermediate offers. Analysis of neural data showed that the attractor basins around patterns of activity reflecting decisions had steeper landscapes for offers that led to consistent decisions. Therefore, we provide neural evidence that energy landscapes predict decision consistency, which reflects decision confidence.

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Fig. 1: Behavioral task, behavior and recording sites.
Fig. 2: Neuron populations in PFC encode choice and the offer value.
Fig. 3: Reconstruction of energy landscape.
Fig. 4: Numerical estimate of energy landscapes for each cue.
Fig. 5: Neural activity reflects evidence accumulation and fixed-point dynamics.
Fig. 6: Retraction coefficients versus reaction time.

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Data availability

Data from the manuscript can be found in the following Figshare repository:

Wang et al. Data for ‘Attractor dynamics reflect decision confidence in macaque prefrontal cortex’. Figshare. Dataset: https://doi.org/10.6084/m9.figshare.21701282. Source data are provided with this paper.

Code availability

Custom spike sorting and data analysis codes were used. Codes for the custom spike sorter can be found at https://github.com/wangxsiyu/WangGit_Pilot_SpikeSorter.git. Custom MATLAB scripts used to perform all analysis and generate all figures can be found at https://github.com/wangxsiyu/Paper_NN-A81142A.git

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Acknowledgements

This work was supported by the intramural research program of NIMH (ZIA MH002928 to B.A., ZIA MH002619 to B.R.). The authors thank C. Robinson and Y. Wei for their assistance in spike sorting.

Author information

Author notes

  1. Rossella Falcone

    Present address: Leo M. Davidoff Department of Neurological Surgery, Albert Einstein College of Medicine Montefiore Medical Center, Bronx, NY, USA

  2. These authors contributed equally: Siyu Wang, Rossella Falcone.

Authors and Affiliations

  1. Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

    Siyu Wang, Rossella Falcone, Barry Richmond & Bruno B. Averbeck

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Contributions

R.F. and B.R. designed the behavioral task, R.F. collected the data, S.W. and B.A. developed the analytical approach, S.W. analyzed the data, and S.W. and B.A. wrote the manuscript, with input from R.F. and B.R.

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Correspondence to Bruno B. Averbeck.

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Extended data

Extended Data Fig. 1 Reconstruction of energy landscapes for intermediate cues, separated by choice.

The manifolds for reject (dotted lines) and accept (solid lines) choices for Monkey V (panel a) and Monkey W (panel b) for intermediate cues. Intermediate cues are defined as cues with p(accept) between 0.25 and 0.75. The lines are separated by the probability of accepting the offer. Positive values in the choice dimensions reflect accept choices. Data are presented as mean values ± SEM.

Source data

Extended Data Fig. 2 Position of mean activity in 1-D choice dimension for each cue.

a-b. Position in 1-D choice dimension vs. time for each cue for monkey V (panel a) and monkey W (panel b). Data are presented as mean values ± SEM. The colors reflect p(accept), red reflects cues signaling offers that monkeys mostly accept, green reflects cues signaling offers that monkeys mostly reject, and yellow reflects cues signaling offers that monkeys have intermediate probabilities of accepting. c-d. Correlation of position of mean trajectory at the mean reaction time and entropy for accept and reject decisions for monkey V (panel c) and monkey W (panel d). For monkey V, r = −0.72, p < 0.001; for monkey W, r = −0.62, p < 0.001. e-f. Significance of correlation of entropy and position of mean activity over time for monkey V (panel e) and for monkey W (panel f). A two-tailed one-sample t-test was performed at each time bin, uncorrected for multiple comparison. N = 8 for each animal. Data are presented as mean values ± SEM.

Source data

Extended Data Fig. 3 Evidence estimated from neural data vs behavior.

a. Evidence in favor of choice estimated from behavior (see Methods). N = 8 for each animal. Data are presented as mean values ± SEM. b. Correlation of the behaviorally extracted evidence \({z}_{i}\) and the evidence term fitted from the neural data \({h}_{i}\) over time. A two-tailed one-sample t-test was performed at each time bin, uncorrected for multiple comparison. N = 8 for each animal. Data are presented as mean values ± SEM. c and d. Correlations between \({z}_{i}\) and \({h}_{i}\) at the mean reaction time for monkey V and W respectively. For monkey V, r = 0.82, p < 0.001; for monkey W, r = 0.67, p < 0.001.

Source data

Extended Data Fig. 4 Parameters in the alternative evidence model.

Error bars represent standard error of the mean. For plots that show data separately for each monkey, N = 8 as SEM was computed across sessions for each animal. a. Choice-related activity, characterized as the undriven fixed points, \({x}_{0,\,j},\) over time. Note the darker color curve is accept decision and lighter color curve is reject decision, for each monkey. b. Strength of evidence-driven neural activity over time by offer cue. The colors reflect p(accept), red reflects cues signaling offers that monkeys mostly accept, green reflects cues signaling offers that monkeys mostly reject, and yellow reflects cues signaling offers that monkeys have intermediate probabilities of accepting. c. Retraction coefficients are higher for consistent choices. A one-tailed paired t-test was performed at each time bin between certain and uncertain conditions by first averaging between accept and reject conditions, uncorrected for multiple comparison. d. Retraction coefficient significantly correlates with choice entropy across the 9 cues. A two-tailed one-sample t-test was performed at each time bin, uncorrected for multiple comparison. e. Correlation between retraction coefficient and choice entropy at median reaction time for Monkey V (left) and Monkey W (right). For monkey V, r = −0.62, p < 0.001; for monkey W, r = −0.56, p < 0.001. * = 0.05, ** = 0.01, *** = 0.001.

Source data

Extended Data Fig. 5 Model coefficients estimated when activity projected on an alternative dimension.

Model coefficients estimated when activity projected on choice-dimension estimated by vector difference in means for accept vs. reject decisions. a-e. Model parameters for dynamics model after data projected onto the 1-D choice dimension defined by the difference in mean responses for accept and reject trials. N = 8 for each animal. Data are presented as mean values ± SEM. Significance values as in Fig. 5. e. For monkey V, r = −0.45, p < 0.001; for monkey W, r = −0.56, p < 0.001. f. Correlation between retraction coefficients defined in the 1-D choice dimension defined by either the SVM or the projection onto the line defined by the difference in mean responses for each condition. The retraction coefficients were strongly correlated (r = 0.78, p < 0.001).

Source data

Extended Data Fig. 6 Parameters from a 3-dimensional dynamical system model.

a-c. Correlation between the three eigenvalues of the retraction matrix for each cue, and the behavioral entropy of that cue. A two-tailed one-sample t-test was performed at each time bin, uncorrected for multiple comparison. d. Alignment of the vector connecting the 3D undriven fixed point and the 1-D choice dimension. N = 8 for each animal. Data are presented as mean values ± SEM.

Source data

Extended Data Fig. 7 Mean firing rate of individual neurons to value.

a and b. Mean firing rates of neurons in response to different offers as a function of value-coding, where 1 is preference for better options, and 0 is preference for worse options. The colors reflect p(accept), red reflects cues signaling offers that monkeys mostly accept, green reflects cues signaling offers that monkeys mostly reject, and yellow reflects cues signaling offers that monkeys have intermediate probabilities of accepting. c. Weight of each neuron on the 1-D choice dimension as a function of value coding. N = 8 for each animal. Data are presented as mean values ± SEM.

Source data

Extended Data Fig. 8 Dynamical system model fit to positive, negative and mixture neurons.

a-c. Correlation between the retraction coefficient for each cue, and the behavioral entropy of that cue. Data are presented as mean values ± SEM. A two-tailed one-sample t-test was performed at each time bin, uncorrected for multiple comparison. d-f. The retraction coefficients over time for certain vs uncertain cues. N = 8 for each animal. Data are presented as mean values ± SEM. A one-tailed paired t-test was performed at each time bin between certain and uncertain conditions by first averaging between accept and reject conditions, uncorrected for multiple comparison.

Source data

Extended Data Fig. 9 Retraction coefficients vs accept reaction time.

a. The correlations between retraction coefficients and reaction time for accept decisions over time. A two-tailed one-sample t-test was performed at each time bin, uncorrected for multiple comparison. N = 8 for each animal. Data are presented as mean values ± SEM. b. The correlations between retraction coefficients at average reaction time for reject decisions and reaction time for accept decisions (monkey V, r = −0.05, p = 0.753). c. Same as b for monkey W (monkey W, r = −0.38, p = 0.021).

Source data

Extended Data Fig. 10 Posterior predictive checks.

Energy landscape for simulated data from the linear dynamical system model. The colors reflect p(accept), red reflects cues signaling offers that monkeys mostly accept, green reflects cues signaling offers that monkeys mostly reject, and yellow reflects cues signaling offers that monkeys have intermediate probabilities of accepting. Data are presented as mean values ± SEM.

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Wang, S., Falcone, R., Richmond, B. et al. Attractor dynamics reflect decision confidence in macaque prefrontal cortex. Nat Neurosci 26, 1970–1980 (2023). https://doi.org/10.1038/s41593-023-01445-x

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