The expected value of control: An integrative theory of anterior cingulate cortex function

By Amitai Shenhav, Matthew Botvinick, J. Cohen. Neuron, 2013

2,072 citations recorded by the source corpus.

Summary The dorsal anterior cingulate cortex (dACC) has a near-ubiquitous presence in the neuroscience of cognitive control. It has been implicated in a diversity of functions, from reward processing and performance monitoring to the execution of control and action selection. Here, we propose that this diversity can be understood in terms of a single underlying function: allocation of control based on an evaluation of the expected value of control (EVC). We present a normative model of EVC that integrates three critical factors: the expected payoff from a controlled process, the amount of control that must be invested to achieve that payoff, and the cost in terms of cognitive effort. We propose that dACC integrates this information, using it to determine whether, where and how much control to allocate. We then consider how the EVC model can explain the diverse array of findings concerning dACC function.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/4dc26de3ee2e546d0d2abb5a54ccdc658066dca2

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In Cognitive Neuroscience

Citations in the corpus

Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation (2017, background): “…initiated (Dehaene & Sigman, 2012), whether to make the task easier by offloading intentions into the environment (Gilbert, 2015), or more generally when it is advantageous to deliberate (Keramati et al., 2011) or engage cognitive control (Boureau et al., 2015; Shenhav, Botvinick, & Cohen, 2013).”
The Effort Paradox: Effort Is Both Costly and Valued (2018, background): “, to integrate representations of effort costs), providing a normative account of the overall expected value (EV) of a given allocation of effort [23,131] (see also [55,132,133]): EVðeffort; stateÞ 1⁄4 i Prðoutcomei jeffort; stateÞ ValueðoutcomeiÞ þ ValueðeffortÞ [I]”
Model-based hierarchical reinforcement learning and human action control (2014, background): “It is also worth noting that the actions in question may include not only overt motor behaviours, but also ‘cognitive actions’ governing attention, memory and other faculties (see [13]).”
Controllability of structural brain networks (2014, positive): “These two systems are characterized by different functional connectivity patterns at rest 11 and are thought to support distinct functional roles within the general area of cognitive control 23 : task-switching 24 and task-set maintenance 25 , respectively.”
Hierarchical Active Inference: A Theory of Motivated Control (2018, background): “Engaging each additional level incurs a ‘ cost of control ’ [48] and is equivalent to inference under a more complex model, or a model that includes more variables (e.g., semantic information plus affordances).”
High Boredom Proneness and Low Trait Self-Control Impair Adherence to Social Distancing Guidelines during the COVID-19 Pandemic (2020, background): “As self-control can be understood as the capacity to overcome competing responses in order to reach a goal [16], it is indeed plausible that adhering to these guidelines demands self-control.”
How prior expectations shape multisensory perception (2016, background): “Thus, one may ask whether lIFS plays a key role in controlling information integration and segregation in multisensory perception (Shenhav et al., 2013).”
Integrating memories to guide decisions (2015, background): “More generally, this is a promising instance of a broader trend in cognitive neuroscience toward rational cost-benefit analyses of cognitive control phenomena [55,56].”
Efficiency of learning vs. processing: Towards a normative theory of multitasking (2020, background): “Additionally, a cost of control parameter could be incorporated that scales with the number of tasks being executed and/or the complexity of the task environment (Shenhav et al., 2013).”