A salience misattribution model for addictive-like behaviors

Kalhan, Shivam; Redish, A. David; Hester, Robert; Garrido, Marta I.

doi:10.1016/j.neubiorev.2021.02.039

Cited by 12 publications

(16 citation statements)

References 105 publications

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“…Our findings are also in line with a recent review by Kalhan et al. (2021) which suggested that ACC dysfunction leads to misaligned internal models and hence, affected patients might misattribute addiction‐related stimuli with a higher salience. Therefore, it appears important to note that ACC abnormalities are not specific to SPA, but can be clearly considered as transdiagnostic abnormality of brain morphology.…”

Section: Discussionsupporting

confidence: 93%

“…It is also a hint to a dimensional and transnosologically valid neural signature related to compulsive behavior, an essential mechanism that also drives SUD (Oh et al, 2021) and behavioral addictions (Zhang et al, 2020). Our findings are also in line with a recent review by Kalhan et al (2021) which suggested that ACC dysfunction leads to misaligned internal models and hence, affected patients might misattribute addiction-related stimuli with a higher salience. Therefore, it appears important to note that ACC abnormalities are not specific to SPA, but can be clearly considered as transdiagnostic abnormality of brain morphology.…”

Section: Discussionsupporting

confidence: 89%

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Cortical surface variation in individuals with excessive smartphone use

Hirjak

Henemann

Schmitgen

et al. 2022

Developmental Neurobiology

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Excessive smartphone use has been repeatedly related to adverse effects on mental health and psychological well‐being in young adults. The continued investigation of the neurobiological mechanism underlying excessive smartphone use—sometimes also referred to as “smartphone addiction”(SPA)—is considered a top priority in system neuroscience research. Despite progress in the past years, cortical morphology associated with SPA is still poorly understood. Here, we used structural magnetic resonance imaging (MRI) at 3 T to investigate two cortical surface markers of distinct neurodevelopmental origin such as the complexity of cortical folding (CCF) and cortical thickness (CTh) in individuals with excessive smartphone use (n = 19) compared to individuals not fulfilling SPA criteria (n‐SPA; n = 22). SPA was assessed using the Smartphone Addiction Inventory (SPAI). CCF and CTh were investigated using the Computational Anatomy Toolbox (CAT12). SPA individuals showed lower CCF in the right superior frontal gyrus as well as in the right caudal (cACC) and rostral anterior cingulate cortex (rACC) compared to n‐SPA individuals (TFCE, uncorrected at p < 0.001). Following a dimensional approach, across the entire sample, CCF of the right cACC was significantly associated with SPAI total score, as well as with distinct SPAI subdimensions, particularly time spent with the device, compulsivity, and sleep interference in all participants (n = 41; p < 0.05, FDR‐corrected). Collectively, these findings suggest that SPA is associated with aberrant structural maturation of regions important for cognitive control and emotional regulation.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 89%

Cortical surface variation in individuals with excessive smartphone use

Hirjak

Henemann

Schmitgen

et al. 2022

Developmental Neurobiology

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“…This builds on the conceptual framework that salience modulates internal representation updating and may contribute to addictive behaviours by producing misaligned internal representations (Kalhan et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Reward prediction-errors weighted by cue salience produces addictive behaviors in simulations, with asymmetrical learning and steeper delay discounting

Kalhan

Garrido

Hester

et al. 2023

Preprint

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Dysfunction in learning and motivational systems are thought to contribute to addictive behaviors. Previous models have suggested that dopaminergic roles in learning and motivation could produce addictive behaviors through pharmacological manipulations that provide excess dopaminergic signaling towards these learning and motivational systems. Redish 2004 suggested a role based on dopaminergic signals of value prediction error, while Zhang et al. 2009 suggested a role based on dopaminergic signals of motivation. Both these models present significant limitations. They do not explain the reduced sensitivity to drug-related costs/negative consequences, the increased impulsivity generally found in people with a substance use disorder, craving behaviors, and non-pharmacological dependence, all of which are key hallmarks of addictive behaviors. Here, we propose a novel mathematical definition of salience, that combines aspects of dopaminergic function in both, learning and motivation, within the reinforcement learning framework. Using a single parameter regime, we simulated addictive behaviors that the Zhang et al. 2009 and Redish 2004 models also produce but we went further in simulating the downweighting of drug-related negative prediction-errors, steeper delay discounting of drug rewards, craving behaviors and aspects of behavioral/non-pharmacological addictions. The current salience model builds on our recently proposed conceptual theory that salience modulates internal representation updating and may contribute to addictive behaviors by producing misaligned internal representations (Kalhan et al., 2021). Critically, our current mathematical model of salience argues that the seemingly disparate learning and motivational aspects of dopaminergic functioning may interact through a salience mechanism that modulates internal representation updating.

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“…Could the results of the two studies be reconciled by considering additional potential mechanisms of unblocking? For example, it has been argued that the blocked stimulus attains lesser salience than the blocking stimulus 45 and that salience may be misattributed as a consequence of drug-induced dopaminergic hyperactivity 46 . Could intense dopaminergic activation by the optical pulse train lead to the misattribution of salience to a redundant stimulus that would otherwise have attracted little attention?…”

Section: Alternative Roles For Dopaminementioning

confidence: 99%

Does phasic dopamine release cause policy updates?

Carter

Cossette

Trujillo-Pisanty

et al. 2022

Preprint

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Phasic dopamine activity is believed to both encode reward-prediction errors (RPEs) and to cause the adaptations that these errors engender. If so, a rat working for optogenetic stimulation of dopamine neurons will repeatedly update its policy and/or action values, thus iteratively increasing its work rate. Here, we challenge this view by demonstrating stable, non-maximal work rates in the face of repeated optogenetic stimulation of midbrain dopamine neurons. Furthermore, we show that rats learn to discriminate between world states distinguished only by their history of dopamine activation. Comparison of these results to reinforcement learning simulations suggests that the induced dopamine transients acted more as rewards than RPEs. However, pursuit of dopaminergic stimulation drifted upwards over a time scale of days and weeks, despite its stability within trials. To reconcile the results with prior findings, we consider multiple roles for dopamine signaling.

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A salience misattribution model for addictive-like behaviors

Cited by 12 publications

References 105 publications

Cortical surface variation in individuals with excessive smartphone use

Cortical surface variation in individuals with excessive smartphone use

Reward prediction-errors weighted by cue salience produces addictive behaviors in simulations, with asymmetrical learning and steeper delay discounting

Does phasic dopamine release cause policy updates?

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