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      Embodying addiction: A predictive processing account

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      Brain and Cognition
      Elsevier BV

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          Abstract

          Highlights • New perspective on reward learning model of addiction based on predictive processing. • Reward learning systems track the agent’s confidence (precision) in its predictions. • Embodied feelings that track rate of error reduction weigh precision of predictions. • Substance addiction is the outcome of aberrant precision estimation. • Addiction is not a brain disease, but a breakdown in the agent-environment dynamics.

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          Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications.

          The loss of control over drug intake that occurs in addiction was initially believed to result from disruption of subcortical reward circuits. However, imaging studies in addictive behaviours have identified a key involvement of the prefrontal cortex (PFC) both through its regulation of limbic reward regions and its involvement in higher-order executive function (for example, self-control, salience attribution and awareness). This Review focuses on functional neuroimaging studies conducted in the past decade that have expanded our understanding of the involvement of the PFC in drug addiction. Disruption of the PFC in addiction underlies not only compulsive drug taking but also accounts for the disadvantageous behaviours that are associated with addiction and the erosion of free will.
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            The debate over dopamine's role in reward: the case for incentive salience.

            Debate continues over the precise causal contribution made by mesolimbic dopamine systems to reward. There are three competing explanatory categories: 'liking', learning, and 'wanting'. Does dopamine mostly mediate the hedonic impact of reward ('liking')? Does it instead mediate learned predictions of future reward, prediction error teaching signals and stamp in associative links (learning)? Or does dopamine motivate the pursuit of rewards by attributing incentive salience to reward-related stimuli ('wanting')? Each hypothesis is evaluated here, and it is suggested that the incentive salience or 'wanting' hypothesis of dopamine function may be consistent with more evidence than either learning or 'liking'. In brief, recent evidence indicates that dopamine is neither necessary nor sufficient to mediate changes in hedonic 'liking' for sensory pleasures. Other recent evidence indicates that dopamine is not needed for new learning, and not sufficient to directly mediate learning by causing teaching or prediction signals. By contrast, growing evidence indicates that dopamine does contribute causally to incentive salience. Dopamine appears necessary for normal 'wanting', and dopamine activation can be sufficient to enhance cue-triggered incentive salience. Drugs of abuse that promote dopamine signals short circuit and sensitize dynamic mesolimbic mechanisms that evolved to attribute incentive salience to rewards. Such drugs interact with incentive salience integrations of Pavlovian associative information with physiological state signals. That interaction sets the stage to cause compulsive 'wanting' in addiction, but also provides opportunities for experiments to disentangle 'wanting', 'liking', and learning hypotheses. Results from studies that exploited those opportunities are described here. In short, dopamine's contribution appears to be chiefly to cause 'wanting' for hedonic rewards, more than 'liking' or learning for those rewards.
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              Neurobiologic Advances from the Brain Disease Model of Addiction.

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                Author and article information

                Journal
                Brain and Cognition
                Brain and Cognition
                Elsevier BV
                02782626
                February 2020
                February 2020
                : 138
                : 105495
                Article
                10.1016/j.bandc.2019.105495
                5ac63ea1-a987-4083-aabb-422dff008f79
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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