Evidence map›Paper›PMID 28981910›Full record

ReviewSocial cognitive and affective neuroscience2017

Explicit and implicit emotion regulation: a multi-level framework.

Laura Martin Braunstein, James J Gross, Kevin N Ochsner

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Social cognitive and affective neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 203 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
203citing papers in PubMed, 5 pooled it
–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

203 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Trial
  8. Trial
  9. Trial
  10. Trial
  11. Trial
  12. Trial
  13. Trial
  14. Trial
  15. Trial
  16. Trial
  17. Trial
  18. Trial
  19. Trial
  20. Trial

143 more citing papers are in PubMed but not listed here.

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.

Laura Martin BraunsteinDepartment of Psychology, Columbia University, New York, NY 10027, USA.
James J GrossDepartment of Psychology, Stanford University, Stanford, CA 94305, USA.
Kevin N OchsnerDepartment of Psychology, Columbia University, New York, NY 10027, USA.

Funding

The Development of Emotion Regulation Mechanisms Impacting HealthR01HD069178 · NICHD · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI OCHSNER, KEVIN N · 2010 to 2014
$2.8M
NICHD NIH HHS R01 HD069178
6 · The paper itself

Abstract

The ability to adaptively regulate emotion is essential for mental and physical well-being. How should we organize the myriad ways people attempt to regulate their emotions? We explore the utility of a framework that distinguishes among four fundamental classes of emotion regulation strategies. The framework describes each strategy class in terms their behavioral characteristics, underlying psychological processes and supporting neural systems. A key feature of this multi-level framework is its conceptualization of the psychological processes in terms of two orthogonal dimensions that describe (i) the nature of the emotion regulation goal (ranging from to implicit to explicit) and (ii) the nature of the emotion change process (ranging from more automatic to more controlled). After describing the core elements of the framework, we use it to review human and animal research on the neural bases of emotion regulation and to suggest key directions for future research on emotion regulation.

Indexed as

AnimalsBehaviorEmotionsHumansamygdalaemotion regulationexplicitimplicitprefrontal cortex

Identifiers

PMID28981910
PMCPMC5647798

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.