paper:doi-10-1093-cercor-bhu159The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes
Original abstract (expand)
Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a "limited offer" game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing.
Related work— refs + corpus + external arXiv
Cited / in-corpus / arXiv badges show which signals surfaced each row. Multi-source rows weighted higher.
- What happens next and when "next" happens: Mechanisms of spatial and temporal predictionDean Wyatte2014≈ 74%
- A coalgebraic perspective on predictive processingFilippo Torresan, Tomoya Nakai Manuel Baltieri2025≈ 73%
- Concurrent generative models inform prediction error in the human auditory pathwayAlejandro Tabas and Katharina von Kriegstein2022≈ 73%
- ≈ 73%
- ≈ 73%
- A Dual-Head Transformer-State-Space Architecture for Neurocircuit Mechanism Decomposition from fMRICole Korponay2026≈ 73%
- Emergence of Deviance Detection in Cortical Cultures through Maturation, Criticality, and Early ExperienceAmit Yaron, Dai Akita, Tomoyo Isoguchi Shiramatsu, Zenas C. Chao, Hirokazu Takahashi Zhuo Zhang2025≈ 73%
- The Predictive Brain: Neural Correlates of Word Expectancy Align with Large Language Model Prediction ProbabilitiesKonstantin Tziridis, Andreas Maier, Thomas Kinfe, Ricardo Chavarriaga, Achim Schilling, Patrick Krauss Nikola K\"olbl2025≈ 72%
- Bistable perception, precision and neuromodulationThomas Parr, Karl Friston, M. Berk Mirza, Noor Sajid Filip Novicky2022≈ 72%
- ≈ 72%
- Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope programNicholas Audette, Ryszard Auksztulewicz, Krzysztof Basi\'nski, Andr\'e M. Bastos, Michael Berry, Andres Canales-Johnson, Hannah Choi, Claudia Clopath, Uri Cohen, Rui Ponte Costa, Roberto De Filippo, Roman Doronin, S\'everine Durand, Steven P. Errington, Jeffrey P. Gavornik, Colleen J. Gillon, Arno Granier, Jordan P. Hamm, Loreen Hert\"ag, Henry Kennedy, Sandeep Kumar, Alexander Ladd, Hugo Ladret, J\'er\^ome A. Lecoq, Alexander Maier, Patrick McCarthy, Jie Mei, Jorge Mejias, John Hongyu Meng, Fabian Mikulasch, Noga Mudrik, Farzaneh Najafi, Kevin Nejad, Hamed Nejat, Karim Oweiss, Mihai A. Petrovici, Viola Priesemann, Lucas Rudelt, Sarah Ruediger, Simone Russo, Alessandro Salatiello, Walter Senn, Eli Sennesh, Sepehr Sima, Cem Uran, Anna Vasilevskaya, Julien Vezoli, Martin Vinck, Xiao-Jing Wang, Jacob A. Westerberg, Katharina Wilmes, Yihan Sophy Xiong Ido Aizenbud2026≈ 72%
- The DIME Architecture: A Unified Operational Algorithm for Neural Representation, Dynamics, Control and IntegrationNicu Bizdoaca, Ionica Pirici, Tudor-Adrian Balseanu, Eduard Nicusor Bondoc Ionel Cristian Vladu2026≈ 72%
- Dendritic predictive coding: A theory of cortical computation with spiking neuronsLucas Rudelt, Michael Wibral, Viola Priesemann Fabian A. Mikulasch2022≈ 72%
- AFA-PredNet: The action modulation within predictive codingJunpei Zhong and Angelo Cangelosi and Xinzheng Zhang and Tetsuya Ogata2018≈ 72%
- ≈ 72%
- Brain in the Dark: Design Principles for Neuro-mimetic Learning and InferenceSzymon Urbas, Karl Friston Mehran H. Bazargani2023≈ 71%
- Predictive Processing in Cognitive Robotics: a ReviewGuido Schillaci, Giovanni Pezzulo, Verena V. Hafner, Bruno Lara Alejandra Ciria2021≈ 71%
- Active Inference, Curiosity and Insightin corpus2017≈ 69%
- The computational boundary of a 'self': developmental bioelectricity drives multicellularity and scale-free cognitionin corpus2019≈ 69%
- ≈ 69%
- Collective intelligence: A unifying concept for integrating biology across scales and substratesin corpus2024≈ 69%
- ≈ 68%
- Active Inference: A Process Theoryin corpus2017≈ 68%
- ≈ 68%
- ≈ 68%
- The Non-Linear Representation Dilemma: Is Causal Abstraction Enough for Mechanistic Interpretability?in corpus2025≈ 68%
- The Platonic Representation Hypothesisin corpus2024≈ 68%
- Mechanistic Knobs in LLMs: Retrieving and Steering High-Order Semantic Features via Sparse Autoencodersin corpus2026≈ 68%
- Model Alignment Searchin corpus2025≈ 68%
- Life as we know itin corpus2013≈ 68%
Similar preprints — Semantic Scholar
Cited by (3)
- Active Inference, Curiosity and Insight
Minimizing expected variational free energy under a discrete-state Markov decision process generative model is sufficient to produce curiosity, epistemic learning, and insight without any additional m
- Active Inference: A Process Theory
A single variational principle—minimizing variational free energy via gradient descent on a Markov decision process (MDP) generative model—is sufficient to derive neuronal dynamics that reproduce, wit
- Active inference on discrete state-spaces: a synthesis
Active inference on discrete state-spaces, formalized as partially observable Markov decision processes (POMDPs) with likelihood matrix A, transition matrix B, and prior D, unifies perception, plannin