ArticleiScience2024
Decomposing loss aversion from a single neural signal.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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Who cites it
1 citing paper in PubMed.
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
No grant is acknowledged in the PubMed record.
Abstract
People often display stronger aversion to losses than appetite for equivalent gains, a widespread phenomenon known as loss aversion. The prevailing theory attributes loss aversion to a valuation bias that amplifies losses relative to gains. An alternative account attributes loss aversion to a response bias that avoids choices that might result in loss. By modeling the temporal dynamics of scalp electrical activity during decisions to accept or reject gambles within a sequential sampling framework, we decomposed valuation bias and response bias from a single event-related neural signal, the P3. Specifically, we found valuation bias manifested as larger sensitivity of P3 to losses than gains, which was localizable to reward-related brain regions. By contrast, response bias manifested as larger P3 preceding gamble acceptance than rejection and was localizable to motor cortex. Our study reveals the dissociable neural biomarkers of response bias and valuation bias underpinning loss-averse decisions.
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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 OpenQuestion graph.