Foraging Value, Risk Avoidance, and Multiple Control Signals: How the Anterior Cingulate Cortex Controls Value-based Decision-making

Brown, Joshua W.; Alexander, William H.

doi:10.1162/jocn_a_01140

Cited by 47 publications

(49 citation statements)

References 98 publications

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“…A recent proposal extended the PRO model in this direction ( PRO-Control , Brown and Alexander, 2017), suggesting how prediction and error signals computed in ACC can serve as the basis for proactive and reactive control. While the PRO-Control assigns the same computations to ACC as in previous iterations of the model (Alexander and Brown, 2011, 2014; Alexander et al, 2015), it is able to account for additional behavioral and imaging effects related to deploying control, including effects of foraging value and choice difficulty (cf.…”

Section: Discussionmentioning

confidence: 99%

Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort

2017

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In the last two decades the anterior cingulate cortex (ACC) has become one of the most investigated areas of the brain. Extensive neuroimaging evidence suggests countless functions for this region, ranging from conflict and error coding, to social cognition, pain and effortful control. In response to this burgeoning amount of data, a proliferation of computational models has tried to characterize the neurocognitive architecture of ACC. Early seminal models provided a computational explanation for a relatively circumscribed set of empirical findings, mainly accounting for EEG and fMRI evidence. More recent models have focused on ACC's contribution to effortful control. In parallel to these developments, several proposals attempted to explain within a single computational framework a wider variety of empirical findings that span different cognitive processes and experimental modalities. Here we critically evaluate these modeling attempts, highlighting the continued need to reconcile the array of disparate ACC observations within a coherent, unifying framework.

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Section: Discussionmentioning

confidence: 99%

Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort

2017

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“…However, two alternative interpretations may be possible. This activity may reflect simply predicting the nature of the upcoming task (i.e., is it difficult or not, Vassena, Deraeve, et al, 2017), as opposed to playing a causal role in effort preparation (Brown & Alexander, 2017; Verguts et al, 2015). Alternatively, it may predict other variables related to task difficulty, such as increased error likelihood (Brown & Braver, 2005) or time-on-task (Grinband et al, 2011), although these models predict these effects in the dACC rather than DLPFC.…”

Section: Discussionmentioning

confidence: 99%

Preparation for mental effort recruits Dorsolateral Prefrontal Cortex: an fNIRS investigation

Vassena

Gerrits

Demanet

et al. 2017

Preprint

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Preparing for a mentally demanding task calls upon cognitive and motivational resources. The underlying neural implementation of these mechanisms is receiving growing attention, given the implications for professional, social, and medical contexts. While several fMRI studies converge in assigning a crucial role to a cortico-subcortical network including Anterior Cigulate Cortex (ACC) and striatum, the involvement of Dorsolateral Prefrontal Cortex (DLPFC) during mental effort anticipation has yet to be replicated. This study was designed to target DLPFC contribution using functional Near Infrared Spectroscopy (fNIRS), as a more cost-effective tool measuring cortical hemodynamics. We adapted a validated mental effort task, where participants performed easy and difficult mental calculation, while measuring DLPFC activity during the anticipation phase. As hypothesized, DLPFC activity increased during preparation for a hard task as compared to an easy task. Besides replicating a previous fMRI study, these results establish fNIRS as an effective tool to investigate cortical contributions to preparation for effortful behavior. This is especially useful if one requires testing large samples (e.g., to target individual differences), populations with contraindication for functional MRI (e.g., infants or patients with metal implants), or subjects in more naturalistic environments (e.g., work or sport).

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“…These models specify a set of components needed for optimal decision-making about control allocation and they are not mutually exclusive. Many of them include the representations of outcome value and/or outcome controllability (Brown and Alexander, 2017;Holroyd and McClure, 2015;Shenhav et al, 2013;Verguts et al, 2015). Also, many of these models assume that learning is crucial in forming these representations and emphasize the importance of learning for cognitive control (Abrahamse et al, 2016;Bhandari et al, 2017).…”

Section: Computational Models Of Cognitive Controlmentioning

confidence: 99%

Motivation and Cognitive Control in Depression

Grahek

Shenhav

Musslick

et al. 2018

Preprint

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Depression is linked to deficits in cognitive control and a host of other cognitive impairments arise as a consequence of these deficits. Despite of their important role in depression, there are no mechanistic models of cognitive control deficits in depression. In this paper we propose how these deficits can emerge from the interaction between motivational and cognitive processes. We review depression-related impairments in key components of motivation along with new cognitive neuroscience models that focus on the role of motivation in the decision-making about cognitive control allocation. Based on this review we propose a unifying framework which connects motivational and cognitive control deficits in depression. This framework is rooted in computational models of cognitive control and offers a mechanistic understanding of cognitive control deficits in depression.

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Foraging Value, Risk Avoidance, and Multiple Control Signals: How the Anterior Cingulate Cortex Controls Value-based Decision-making

Cited by 47 publications

References 98 publications

Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort

Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort

Preparation for mental effort recruits Dorsolateral Prefrontal Cortex: an fNIRS investigation

Motivation and Cognitive Control in Depression

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