ArticleiScience2026
Belief updating in uncertain environments is differentially sensitive to reward and punishment learning: Evidence from ERP.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Learning from rewards and punishments relies on prediction errors and belief updating, yet it remains unclear how motivational context reshapes the precision-weighting of prediction errors and neural mechanism while learning in volatile environments. We employed a probabilistic classification task with electroencephalography (EEG) within a hierarchical Bayesian framework to compare reward and punishment learning. Our findings indicate that participants performed better in reward context compared to punishment context. Fitting the hierarchical Bayesian model revealed that punishment drives faster Bayesian belief updates, although these did not translate into improved behavioral outcomes. At the neural level, higher-level precision-weighted prediction error (pwPE
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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.