Evidence map›Paper›PMID 30570275›Full record

ReviewExperimental and clinical psychopharmacology2019

Examining the neurochemical underpinnings of animal models of risky choice: Methodological and analytic considerations.

Justin R Yates

Abstract readReview
In one paragraph

Review in Experimental and clinical psychopharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. The enduring legacy of Peter B. Dews: Rate-dependency and impulsive choice.The Journal of pharmacology and experimental therapeutics · 2025
    Review
  2. Article
  3. Article
  4. Effects of 5-HTPsychopharmacology · 2020
    Article
  5. Article
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

1 author.

Justin R YatesNorthern Kentucky University.

Funding

WKU Lead Faculty AwardP20GM103436 · NIGMS · UNIVERSITY OF LOUISVILLE · PI ERIC C ROUCHKA · 2012 to 2026
$60.1M
National Institute of General Medical SciencesNIGMS NIH HHS P20 GM103436
6 · The paper itself

Abstract

Because risky choice is associated with several psychiatric conditions, recent research has focused on examining the underlying neurochemical processes that control risk-based decision-making. Not surprisingly, several tasks have been developed to study the neural mechanisms involved in risky choice. The current review will briefly discuss the major tasks used to measure risky choice and will summarize the contribution of several major neurotransmitter systems to this behavior. To date, the most common measures of risky choice are the probability discounting task, the risky decision task, and the rat gambling task. Across these three tasks, the contribution of the dopaminergic system has been most studied, although the effects of serotonergic, adrenergic, cholinergic, and glutamatergic ligands will be discussed. Drug effects across these tasks have been inconsistent, which makes determining the precise role of neurotransmitter systems in risky choice somewhat difficult. Furthermore, procedural differences can modulate drug effects in these procedures, and the way data are analyzed can alter the interpretations one makes concerning pharmacological manipulations. By taking these methodological/analytic considerations into account, we may better elucidate the neurochemistry of risky decision-making. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Indexed as

Risk-TakingAnimalsChoice BehaviorDecision MakingGamblingHumansModels, AnimalNeurotransmitter AgentsProbabilityRatsNeurotransmitter Agents

Identifiers

PMID30570275
PMCPMC6467223

What OpenQuestion holds

Textmetadata
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Read underepoch 390

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.