ReviewTrends in cognitive sciences2025
The devilish details affecting TDRL models in dopamine research.
Review in Trends in cognitive sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- A habit and working memory model as an alternative account of human reward-based learning.Nature human behaviour · 2026Article
- Memory erasure by dopamine-gated retrospective learning.bioRxiv : the preprint server for biology · 2026Article
- A brief history of dopamine prediction errors.Frontiers in computational neuroscience · 2026Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Over recent decades, temporal difference reinforcement learning (TDRL) models have successfully explained much dopamine (DA) activity. This success has invited heightened scrutiny of late, with many studies challenging the validity of TDRL models of DA function. Yet, when evaluating the validity of these models, the devil is truly in the details. TDRL is a broad class of algorithms sharing core ideas but differing greatly in implementation and predictions. Thus, it is important to identify the defining aspects of the TDRL framework being tested and to use state spaces and model architectures that capture the known complexity of the behavioral representations and neural systems involved. Here, we discuss several examples that illustrate the importance of these considerations.
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What Socratic 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 Socratic graph.