Dopamine enhances model-free credit assignment through boosting of retrospective model-based inference
- University of Würzburg, Germany
- University College London, United Kingdom
- Charité - Universitätsmedizin Berlin, Germany
- TU Dresden, Germany
- Max Planck Institute for Biological Cybernetics, Germany
Abstract
Dopamine is implicated in representing model-free (MF) reward prediction errors a as well as influencing model-based (MB) credit assignment and choice. Putative cooperative interactions between MB and MF systems include a guidance of MF credit assignment by MB inference. Here, we used a double-blind, placebo-controlled, within-subjects design to test an hypothesis that enhancing dopamine levels boosts the guidance of MF credit assignment by MB inference. In line with this, we found that levodopa enhanced guidance of MF credit assignment by MB inference, without impacting MF and MB influences directly. This drug effect correlated negatively with a dopamine-dependent change in purely MB credit assignment, possibly reflecting a trade-off between these two MB components of behavioural control. Our findings of a dopamine boost in MB inference guidance of MF learning highlights a novel DA influence on MB-MF cooperative interactions.
Data availability
Necessary source data files are openly available at: https://osf.io/4dfkv/.
Article and author information
Author details
Funding
Wellcome Trust (098362/Z/12/Z)
- Raymond J Dolan
Max-Planck-Gesellschaft (Open-access funding)
- Lorenz Deserno
- Rani Moran
- Peter Dayan
- Raymond J Dolan
Deutsche Forschungsgemeinschaft (402170461)
- Lorenz Deserno
- Raymond J Dolan
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The study was approved by the University College London Research Ethics Committee (Project ID 11285/001). Subjects gave written informed consent before the experiment.
Copyright
© 2021, Deserno et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Dopamine enhances model-free credit assignment through boosting of retrospective model-based inference
eLife 10:e67778.
https://doi.org/10.7554/eLife.67778
Further reading
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Background:
Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An interpretable machine learning model was developed to predict PSE using medical records from four hospitals in Chongqing.
Methods:
Medical records, imaging reports, and laboratory test results from 21,459 ischemic stroke patients were collected and analyzed. Univariable and multivariable statistical analyses identified key predictive factors. The dataset was split into a 70% training set and a 30% testing set. To address the class imbalance, the Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors was employed. Nine widely used machine learning algorithms were evaluated using relevant prediction metrics, with SHAP (SHapley Additive exPlanations) used to interpret the model and assess the contributions of different features.
Results:
Regression analyses revealed that complications such as hydrocephalus, cerebral hernia, and deep vein thrombosis, as well as specific brain regions (frontal, parietal, and temporal lobes), significantly contributed to PSE. Factors such as age, gender, NIH Stroke Scale (NIHSS) scores, and laboratory results like WBC count and D-dimer levels were associated with increased PSE risk. Tree-based methods like Random Forest, XGBoost, and LightGBM showed strong predictive performance, achieving an AUC of 0.99.
Conclusions:
The model accurately predicts PSE risk, with tree-based models demonstrating superior performance. NIHSS score, WBC count, and D-dimer were identified as the most crucial predictors.
Funding:
The research is funded by Central University basic research young teachers and students research ability promotion sub-projec t(2023CDJYGRH-ZD06), and by Emergency Medicine Chongqing Key Laboratory Talent Innovation and development joint fund project (2024RCCX10).
