ArticleCell2025
Dopamine encodes deep network teaching signals for individual learning trajectories.
Article in Cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- The curriculum effect in visual learning: The role of readout dimensionality.PLoS computational biology · 2026Article
- Neuromodulator Dynamics Underlying Associative Learning in the Ventral Striatum's Olfactory Tubercle.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Dopamine and Acetylcholine in the Striatum: Circuit Interactions and Behavioral Control in Substance Use Disorders.Brain sciences · 2026Review
- Implementation of active methodologies: a six-phase instructional design model from a neuroeducational perspective.Frontiers in human neuroscience · 2026Article
- Infinite hidden Markov models can dissect the complexities of learning.Nature neuroscience · 2026Article
- Inferring learning rules during de novo task learning.bioRxiv : the preprint server for biology · 2025Article
- Dopamine as a teaching signal: understanding its role in shaping individual behavior.Signal transduction and targeted therapy · 2025Article
- Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks.Advances in neural information processing systems · 2025Article
- Dopaminergic computations for perceptual decisions.Current opinion in behavioral sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Striatal dopamine plays fundamental roles in fine-tuning learned decisions. However, when learning from naive to expert, individuals often exhibit diverse learning trajectories, defying understanding of its underlying dopaminergic mechanisms. Here, we longitudinally measure and manipulate dorsal striatal dopamine signals in mice learning a decision task from naive to expert. Mice learning trajectories transitioned through sequences of strategies, showing substantial individual diversity. Remarkably, the transitions were systematic; each mouse's early strategy determined its strategy weeks later. Dopamine signals reflected strategies each animal transitioned through, encoding a subset of stimulus-choice associations. Optogenetic manipulations selectively updated these associations, leading to learning effects distinct from that of reward. A deep neural network using heterogeneous teaching signals, each updating a subset of network association weights, captured our results. Analyzing the model's fixed points explained learning diversity and systematicity. Altogether, this work provides insights into the biological and mathematical principles underlying individual long-term learning trajectories.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.