The utility of a latent-cause framework for understanding addiction phenomena
Computational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | Addiction Neuroscience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772392524000026 |
| _version_ | 1797342083555524608 |
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| author | Sashank Pisupati Angela J. Langdon Anna B. Konova Yael Niv |
| author_facet | Sashank Pisupati Angela J. Langdon Anna B. Konova Yael Niv |
| author_sort | Sashank Pisupati |
| collection | DOAJ |
| description | Computational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomena such as craving and relapse. Moreover, they cannot account for goal-directed aspects of addiction that necessitate contrasting, model-based formulations. Here we synthesize a growing body of evidence and propose that a latent-cause framework can help unify our understanding of several recurrent phenomena in addiction, by viewing them as the inferred return of previous, persistent “latent causes”. We demonstrate that applying this framework to Pavlovian and instrumental settings can help account for defining features of craving and relapse such as outcome-specificity, generalization, and cyclical dynamics. Finally, we argue that this framework can bridge model-free and model-based formulations, and account for individual variability in phenomenology by accommodating the memories, beliefs, and goals of those living with addiction, motivating a centering of the individual, subjective experience of addiction and recovery. |
| first_indexed | 2024-03-08T10:28:16Z |
| format | Article |
| id | doaj.art-1e3d4a565a4c4126ad68778653c0e82d |
| institution | Directory Open Access Journal |
| issn | 2772-3925 |
| language | English |
| last_indexed | 2024-03-08T10:28:16Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Addiction Neuroscience |
| spelling | doaj.art-1e3d4a565a4c4126ad68778653c0e82d2024-01-27T07:00:41ZengElsevierAddiction Neuroscience2772-39252024-03-0110100143The utility of a latent-cause framework for understanding addiction phenomenaSashank Pisupati0Angela J. Langdon1Anna B. Konova2Yael Niv3Limbic Limited, London UK; Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton NJ, USANational Institute of Mental Health & National Institute on Drug Abuse, National Institutes of Health, Bethesda MD, USADepartment of Psychiatry, University Behavioral Health Care & Brain Health Institute, Rutgers University, New Brunswick NJ, USACorresponding author.; Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton NJ, USAComputational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomena such as craving and relapse. Moreover, they cannot account for goal-directed aspects of addiction that necessitate contrasting, model-based formulations. Here we synthesize a growing body of evidence and propose that a latent-cause framework can help unify our understanding of several recurrent phenomena in addiction, by viewing them as the inferred return of previous, persistent “latent causes”. We demonstrate that applying this framework to Pavlovian and instrumental settings can help account for defining features of craving and relapse such as outcome-specificity, generalization, and cyclical dynamics. Finally, we argue that this framework can bridge model-free and model-based formulations, and account for individual variability in phenomenology by accommodating the memories, beliefs, and goals of those living with addiction, motivating a centering of the individual, subjective experience of addiction and recovery.http://www.sciencedirect.com/science/article/pii/S2772392524000026Latent-cause inferenceAddictionCravingRelapse |
| spellingShingle | Sashank Pisupati Angela J. Langdon Anna B. Konova Yael Niv The utility of a latent-cause framework for understanding addiction phenomena Addiction Neuroscience Latent-cause inference Addiction Craving Relapse |
| title | The utility of a latent-cause framework for understanding addiction phenomena |
| title_full | The utility of a latent-cause framework for understanding addiction phenomena |
| title_fullStr | The utility of a latent-cause framework for understanding addiction phenomena |
| title_full_unstemmed | The utility of a latent-cause framework for understanding addiction phenomena |
| title_short | The utility of a latent-cause framework for understanding addiction phenomena |
| title_sort | utility of a latent cause framework for understanding addiction phenomena |
| topic | Latent-cause inference Addiction Craving Relapse |
| url | http://www.sciencedirect.com/science/article/pii/S2772392524000026 |
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