Evidence map›Paper›PMID 42699181›Full record

ArticleComputational psychiatry (Cambridge, Mass.)2026

Associations Between Steep Delay Discounting and Punishment Learning.

Jeremy Haynes, Peter Kvam, Thomas Olino

Abstract read
In one paragraph

Article in Computational psychiatry (Cambridge, Mass.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jeremy HaynesGeorgia Southern University, US.ORCID https://orcid.org/0000-0002-0811-3849
Peter KvamOhio State University, US.ORCID https://orcid.org/0000-0002-3195-8452
Thomas OlinoTemple University, US.ORCID https://orcid.org/0000-0001-5139-8571

Funding

Developmental changes in reward responsivity: Associations with depression risk markersR01MH107495 · NIMH · TEMPLE UNIV OF THE COMMONWEALTH · PI OLINO, THOMAS M · 2016 to 2020
$3.6M
Enhancing evaluation of reward learning using computational modeling methodsR21MH130792 · NIMH · TEMPLE UNIV OF THE COMMONWEALTH · PI OLINO, THOMAS M · 2022 to 2023
$427k
NIMH NIH HHS R01 MH107495NIMH NIH HHS R21 MH130792
6 · The paper itself

Abstract

Reward valuation and reward and punishment learning are important constructs for understanding and intervening on mental illness. These constructs are frequently measured with behavioral tasks such as intertemporal choice tasks, measuring delay discounting, and the Iowa Gambling Task (IGT), measuring reward and punishment learning. Delay discounting refers to how people value delayed outcomes, with steep discounting (i.e., delayed outcomes holding little value) considered a form of impulsive decision-making. Reward and punishment learning refers to how people adjust their decision behavior in response to rewards and punishments, with deficits indicating atypical reward and punishment processing related to clinical outcomes such as substance abuse. In this study, we tested the association between delay discounting and computational measures of reward and punishment learning in adults (

Indexed as

Computational ModelingDelay DiscountingHierarchical Bayesian AnalysisIowa Gambling TaskReward & Punishment Learning

Identifiers

PMID42699181
PMCPMC13544050

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.