Dorsal anterior cingulate cortex and the value of control

Journal name:
Nature Neuroscience
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Debates over the function(s) of dorsal anterior cingulate cortex (dACC) have persisted for decades. So too have demonstrations of the region's association with cognitive control. Researchers have struggled to account for this association and, simultaneously, dACC's involvement in phenomena related to evaluation and motivation. We describe a recent integrative theory that achieves this goal. It proposes that dACC serves to specify the currently optimal allocation of control by determining the overall expected value of control (EVC), thereby licensing the associated cognitive effort. The EVC theory accounts for dACC's sensitivity to a wide array of experimental variables, and their relationship to subsequent control adjustments. Finally, we contrast our theory with a recent theory proposing a primary role for dACC in foraging-like decisions. We describe why the EVC theory offers a more comprehensive and coherent account of dACC function, including dACC's particular involvement in decisions regarding foraging or otherwise altering one's behavior.

At a glance


  1. dACC's proposed role in control allocation based on EVC.
    Figure 1: dACC's proposed role in control allocation based on EVC.

    Top: the EVC theory proposes that dACC monitors for information relevant to evaluating EVC and specifies the optimal control allocation to downstream regions. dACC is shown in the central panel, based on a Neurosynth meta-analysis of 428 human neuroimaging studies associated with cognitive control ( Example input and output structures are shown at the left and right sides of the panel. Bottom: Previous findings suggest a role for dACC in using each of the monitoring signals listed (for example, errors, conflict) as the basis for one or more subsequent adjustments in control (for example, adjustment in one's speed-accuracy tradeoff or attentional focus). As indicated in the central panel, the EVC theory proposes that dACC evaluates the expected future benefits of applying varying intensities of control (arrow in each gauge) for each candidate control signal (different gauges; for example, different rules and the degree to which they could be attended) and subtracts from this the intrinsic cost of applying a given control intensity. This results in an estimate of the EVC. dACC then selects for execution the control signal settings that maximize EVC, projecting information about these signals to relevant downstream regions responsible for implementing the corresponding signals. Elsewhere we have provided a formal description of this process that aligns EVC and its components with ideas from artificial intelligence and control theory4, 74 and shown how a computational implementation of the theory can account for observed influences of incentives on control adjustments (and associated behavior)74. OFC: orbitofrontal cortex; STN: subthalamic nucleus; mPFC: medial prefrontal cortex; PFC: prefrontal cortex. Center panel adapted with permission from ref. 4, Elsevier.

  2. Decisions about engaging in a current task versus an alternate task from the perspective of FVT and EVC.
    Figure 2: Decisions about engaging in a current task versus an alternate task from the perspective of FVT and EVC.

    Left: FVT focuses on the expected reward for remaining on the current task (the default) and for switching to the alternate, nondefault task (including the time and resources consumed during the switch). (Note that these are not argued to be the only signals in dACC but to comprise the key foraging decision-related components of the dACC signal.) Right: by contrast, EVC considers the degree to which allocating different amounts of control to each task (separately or jointly) will accrue reward and effort-like costs. EVC also considers the switch costs in terms of both time and the demands of adjusting control signals, as well as the cognitive demands associated with decision conflict (as when the two tasks are mutually exclusive and close in value). Differences are highlighted in bold.

  3. Disentangling potential explanations for dACC involvement in foraging settings.
    Figure 3: Disentangling potential explanations for dACC involvement in foraging settings.

    (a) For foraging situations in which the value of the current or default behavior diminishes in a progressive manner (for example, through patch depletion, satiety, etc.), the marginal value theorem (MVT)66 predicts that it is optimal to switch when the value of the current behavior eventually reaches the value of switching (as estimated by the average expected value of the environment); this corresponds to the point of maximum decision difficulty (when the stay/switch options are closest in value), which is likely to increase the demands for control1, 15, 21 (for example, to support higher-fidelity judgments75 or adjustments in other default decision parameters such as response threshold15). Real-world foraging decisions therefore populate the area to the left of this indifference point. Foraging value signals can be examined past this indifference point, but care must be taken to estimate difficulty ideographically and to include sufficient choices on either side of the indifference point so that trials with high foraging values are neither especially difficult nor especially rare (i.e., potentially surprising)67. (b) We found that human dACC activity was best explained by an inverse U, with activity maximal around indifference and decreasing with less difficult choices. (c) Across two studies67, 68 we found that dACC was reliably engaged by the difficulty of foraging choices but saw no evidence of overlapping or adjacent regions signaling the value of foraging per se, even when excluding the easiest choices. Activations reflect liberally thresholded t-statistics (t ≥ 2.0) but dACC's choice-difficulty responses are robust to standard statistical thresholds. Panels adapted from ref. 67, Nature Publishing Group, and ref. 68, Psychonomic Society, Inc. Error bars reflect s.e.m.


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  1. Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.

    • Amitai Shenhav,
    • Jonathan D Cohen &
    • Matthew M Botvinick
  2. Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island, USA.

    • Amitai Shenhav
  3. Brown Institute for Brain Science, Brown University, Providence, Rhode Island, USA.

    • Amitai Shenhav
  4. Department of Psychology, Princeton University, Princeton, New Jersey, USA.

    • Jonathan D Cohen &
    • Matthew M Botvinick
  5. Google DeepMind, London, UK.

    • Matthew M Botvinick
  6. Gatsby Computational Neuroscience Unit, University College London, London, UK.

    • Matthew M Botvinick

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