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Research Articles, Behavioral/Cognitive

Rat Anterior Cingulate Cortex Continuously Signals Decision Variables in a Patch Foraging Task

Gary A. Kane, Morgan H. James, Amitai Shenhav, Nathaniel D. Daw, Jonathan D. Cohen and Gary Aston-Jones
Journal of Neuroscience 20 July 2022, 42 (29) 5730-5744; DOI: https://doi.org/10.1523/JNEUROSCI.1940-21.2022
Gary A. Kane
1Department of Psychology and Neuroscience Institute, Princeton University, Princeton, New Jersey 08544
2Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02155
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Morgan H. James
3Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey 08854
4Brain Health Institute, Rutgers University, Pisccataway, New Jersey 08854
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Amitai Shenhav
5Department of Cognitive, Linguistic, & Psychological Sciences and Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912
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Nathaniel D. Daw
1Department of Psychology and Neuroscience Institute, Princeton University, Princeton, New Jersey 08544
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Jonathan D. Cohen
1Department of Psychology and Neuroscience Institute, Princeton University, Princeton, New Jersey 08544
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Gary Aston-Jones
4Brain Health Institute, Rutgers University, Pisccataway, New Jersey 08854
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Figures

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  • Figure 1.
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    Figure 1.

    Diagram of the foraging task. A, On trial n, the rat chose to press the lever to harvest from the patch, then received reward in the reward magazine in the center of the chamber. After an ITI (7 s), the rat chose to press the same lever on trial n + 1 to harvest a smaller volume of reward. On trial n + 2, the rat chose to nose poke in the back of the chamber, initiating a “travel time” delay (10 s), after which, the rat could continue to harvest in a replenished patch by pressing the lever on the other side of the chamber (trial n + 3). B, Reward depletion curves for each of the nine patch starting reward volumes. Colors indicate whether the patch was a subjective low, medium, or high reward patch, for consistency with further analyses.

  • Figure 2.
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    Figure 2.

    Rat behavior in the patch foraging task. In all panels, points, dark lines, and error bars represent the mean and SE across rats, and lighter lines represent behavior of each individual rat. A, The number of trials spent in each patch as a function of the starting reward volume of the patch. B, The reward rate (reward volume/trial time) rats received on the trial before rats chose to nose poke to leave the patch. C, The median RTs for each rat over the course of trials in the patch, split by patches that started with low (30–60 μl), medium (75–105 μl), or high (120–150 μl) starting reward volumes. D, The median RTs for each rat as rats became closer to leaving patches; 0 trials remaining in the patch indicated the trial in which they nose poked to leave the patch, 1 trial remaining is the last lever press to stay in the patch, 2 is the second to last lever press before leaving the patch, etc. E, Pairwise t tests across the patch leaving threshold (the local reward rate when rats decided to leave the patch), for all patch types. Colors indicate the difference in the reward rate threshold at which rats decided to leave the patches (i.e., y-axis leaving threshold – x-axis leaving threshold). Asterisks indicate statistically significant comparisons (pairwise χ2 tests with Holm–Bonferroni correction). F, Median RTs of rats from the travel time experiment by Kane et al. (2019). Rats visited patches that started with 60, 90, or 120 μl within sessions and experienced travel time delays of 10 or 30 s in separate sessions.

  • Figure 3.
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    Figure 3.

    The leaky accumulator model of the foraging task. A, Diagram of the model, consisting of two layers of leaky accumulators. The bottom layer, the value layer (“local rate” and “global rate” units), estimated the local reward rate and global reward rate by integrating over rewards on different timescales. The top layer, the decision layer, was an LCA model that made decisions to stay versus leave via an accumulation to bound process at the start of the trial, with input from the local and global rate units. B, C, Example activity of the value layer and decision layer units during a 600-s sample of a model simulation. The solid black lines represent the local reward rate and decision stay unit activity, and the solid red lines represent the global reward rate and decision leave unit activity. The dotted blue and green vertical lines indicate the start of a trial in which the model decided to stay in the patch or to leave the patch, respectively. The horizontal dashed line in C represents the decision threshold. D, E, The leaky accumulator model-predicted number of trials spent in each patch type (D) and predicted RTs as by the number of trials spent in patches (E) plotted against observed rat behavior. Points and error bars represent the mean ± SE across rats, lines and ribbon represents the mean ± SE of model-predicted behavior for each rat.

  • Figure 4.
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    Figure 4.

    Cross-validation analysis of the LCA model. A, The likelihood of the LCA model fit to all data (x-axis) compared with the likelihood of the model on left-out samples (parameters fit to two thirds of the data, likelihood calculated on the left-out third; y-axis). Each point represents individual rats; the dashed line represents the identity line (y = x). B, C, LCA predicted behavior (lines and ribbon) plotted against the left-out splits (points and error bars). Number of trials spent in patches is shown in B and RTs as a function of the number of trials spent in patches is shown in C. Points and lines indicate the mean across rats, error bars and ribbon indicate the SEM across rats.

  • Figure 5.
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    Figure 5.

    PETHs of LCA model unit activity in the foraging task. Each panel represents the PETH of activity of LCA model units (localRate, globalRate, stayDecisionActivity, leaveDecisionActivity) or hypotheses for ACC activity measured from LCA model units (decision difficulty, value of leaving, and decision conflict) during a simulation of the foraging task. For each PETH, time = 0 represents the time of the lever press to harvest reward or nose poke to leave the patch, and colors represent the number of trials until leaving the patch.

  • Figure 6.
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    Figure 6.

    ACC activity correlates with foraging decisions and RTs. A, Diagram of recording locations within the Cg1 region; labels indicate distance from bregma. Example unit traces and isolation are shown in Extended Data Figure 6-1. B, C, Average ACC activity over the course of entire trials, normalized and averaged across units, as a function of the log of RTs (B) and trials until leaving the patch and the patch starting reward (C). Points and lines represent the mean normalized (z-scored) activity across units and error bars represent the SE across units. D, Trial-by-trial raster plot (top) and trial-averaged PETH (bottom) locked to the time of decisions for one example single unit and one example multi-unit. E, The average absolute effect of trials until leaving and RTs at each time point within a trial, locked to the time of the lever press to stay in the patch. F, The proportion of units with significant effects of trials until leaving, RTs or both (p < 0.05, z-test on regression coefficient), at each time point within the trial. The horizontal dashed lines represent the median and 95% confidence interval of the expected proportion of false positives given α = 0.05.

  • Figure 7.
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    Figure 7.

    ACC activity correlates with leaky accumulator model decision variables. A, The value of leaving (globalRate – localRate; top) plotted next to average, normalized PETH across ACC units for 5, 3, 1, or 0 trials until leaving the patch (bottom). B, C, PCA was performed across the PETH for all units on these trials. The first nine PCs are presented in Extended Data Figure 7-1. B, The localRate unit activity from the model (top), next to the first PC of ACC activity (second panel), and an example unit that with a strong loading for the first PC (trial-by-trial raster in the third panel and trial-averaged PETH in the bottom panel). C, leaveDecisionActivity from the model (top), next to the second PC of ACC activity (second panel), and an example unit with a strong loading for the second PC (trial-by-trial raster in the third panel and trial-averaged PETH in the bottom panel). PETHs are locked to the time of decisions (the lever press or nose poke). PETHs of leaky accumulator model decision variables were created from simulations using parameters fit to each rat, lines and ribbon represent the average across these simulations. D, Cumulative proportion of variance explained by each PC. E, The proportion of individual units that correlated with each model variable. As many units exhibited significant correlations with multiple model variables, units were assigned to the variable with which they exhibited the strongest correlation.

  • Figure 8.
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    Figure 8.

    Effects of Inactivation of ACC on foraging behavior. A, Diagram of cannula locations in Cg1. B, The number of trials spent in each of the three patch types. C, RTs as rats became closer to leaving patches, where 0 trials until leaving is the nose poke to leave the patch, 1 is the last lever press in the patch, and so on. D, The value of leaving (global reward rate – local reward rate) the rat experienced on the last trial in which they harvested reward from the patch (trials until leaving = 1). In B–D, points and error bars represent the mean and SE across rats, whereas lines and ribbons represent mean and SE across leaky accumulator model predictions for each rat. E, The handling time or time from lever press to entering the reward magazine. Transparent points and lines represent individual rat behavior, and the horizontal bar shows the mean across rats for each condition. F, The value of leaving (global reward rate – local reward rate) over the course of trials in the patch for each of the three patch starting reward volumes. Points and error bars represent the mean and SE across rats. The horizontal dashed line represents the optimal time to leave patches (global rate – local rate = 0), according to MVT. G, Extrapolating from F, the optimal time to leave patches in the aCSF and Bac-Mus conditions, according to MVT.

  • Figure 9.
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    Figure 9.

    LCA model parameters fit to aCSF and Bac-Mus (B-M) sessions. The box represents the first and third quartile, whiskers represent 1.5 times the interquartile range, with all individuals plotted transparently.

Tables

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    Table 1.

    Parameter estimates for LCA fits to animal behavior during recording sessions

    Parameter1st quartileMedian3rd quartile
    wglobalRate0.0080.010.012
    wlocalRate−localRate0.7530.7660.775
    wglobalRate−globalRate0.9940.9950.996
    σrate0.0250.0260.029
    wd−input1.1811.4431.913
    wd−rec0.6750.7070.743
    wd−comp−1.497−1.147−0.814
    σdecision0.0230.0260.03
    z0.610.6480.665
    g4.9975.155.274
    b−0.1−0.050.048
    η2.442.8953.225
    γ1.6351.7771.999
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    Table 2.

    Correlation between PETH of average ACC activity and LCA model variables

    LCA variableR-valueP-value
    localRate−0.798<0.001
    globalRate0.2680.008
    stayDecisionActivity−0.715<0.001
    leaveDecisionActivity0.492<0.001
    decision difficulty0.767<0.001
    value of leaving0.797<0.001
    decision conflict−0.678<0.001
    • R-values represent the strongest absolute correlation coefficient observed across all lags tested; p-values are calculated by comparing correlation coefficients against the correlation coefficient observed using shuffled data.

    • View popup
    Table 3.

    Pairwise t tests on LCA model parameters fit to aCSF sessions versus parameters fit to Bac-Mus sessions

    Parametert valuedfP-value
    wglobalRate−1.468101
    wlocalRate−localRate0.636101
    wglobalRate−globalRate1.186101
    σrate−2.102100.618
    wd−input−1.221101
    wd−rec1.398101
    wd−comp0.362101
    σdecision−2.421100.396
    z2.492100.383
    g−1.047101
    b−1.726101
    η−9.28710< 0.001
    γ0.709101

Extended Data

  • Figures
  • Tables
  • Extended Data Figure 6-1

    Example recording trace and unit isolation. A, An example trace of a bandpass filtered signal (300–3000 Hz) of a single channel for the duration of one trial. The solid black line indicates the start of the trial, and the dashed black line indicates the time of the lever press. The colored portion of the trace indicates spikes assigned to one of two units on this channel. B, The waveforms of the units in A. Download Figure 6-1, TIF file.

  • Extended Data Figure 7-1

    PCs of ACC unit activity. A, The first nine PCs across all individual units for 5, 3, 1, and 0 trials until leaving the patch. B, Correlations among the first 15 PCs and seven leaky accumulator model variables. Color indicates the strength of the correlation, asterisks denote statistically significant correlations (permutation test). Download Figure 7-1, TIF file.

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The Journal of Neuroscience: 42 (29)
Journal of Neuroscience
Vol. 42, Issue 29
20 Jul 2022
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Rat Anterior Cingulate Cortex Continuously Signals Decision Variables in a Patch Foraging Task
Gary A. Kane, Morgan H. James, Amitai Shenhav, Nathaniel D. Daw, Jonathan D. Cohen, Gary Aston-Jones
Journal of Neuroscience 20 July 2022, 42 (29) 5730-5744; DOI: 10.1523/JNEUROSCI.1940-21.2022

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Rat Anterior Cingulate Cortex Continuously Signals Decision Variables in a Patch Foraging Task
Gary A. Kane, Morgan H. James, Amitai Shenhav, Nathaniel D. Daw, Jonathan D. Cohen, Gary Aston-Jones
Journal of Neuroscience 20 July 2022, 42 (29) 5730-5744; DOI: 10.1523/JNEUROSCI.1940-21.2022
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Keywords

  • anterior cingulate cortex
  • decision-making
  • electrophysiology
  • foraging
  • marginal value theorem
  • rats

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