Evidence map›Paper›PMID 37744988›Full record

ArticleAffective science2023

The Affective Dynamics of Everyday Digital Life: Opening Computational Possibility.

Maia L Rocklin, Anna Angelina Garròn Torres, Byron Reeves, Thomas N Robinson, Nilam Ram

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Affective science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. 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

5 authors.

Maia L RocklinDepartment of Psychology, Stanford University, Stanford, CA 94305 USA.
Anna Angelina Garròn TorresDepartment of Psychology, Stanford University, Stanford, CA 94305 USA.
Byron ReevesDepartment of Communication, Stanford University, Stanford, 300-A Building 120, 450 Jane Stanford Way, Stanford, CA 94305 USA.
Thomas N RobinsonDepartment of Pediatrics, Stanford University, Stanford, CA 94305 USA.
Nilam RamDepartment of Psychology, Stanford University, Stanford, CA 94305 USA.ORCID 0000-0003-1671-5257

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Up to now, there was no way to observe and track the affective impacts of the massive amount of complex visual stimuli that people encounter "in the wild" during their many hours of digital life. In this paper, we propose and illustrate how recent advances in AI-trained ensembles of deep neural networks-can be deployed on new data streams that are long sequences of screenshots of study participants' smartphones obtained unobtrusively during everyday life. We obtained affective valence and arousal ratings of hundreds of images drawn from existing picture repositories often used in psychological studies, and a new screenshot repository chronicling individuals' everyday digital life from both Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-023-00202-4.

Indexed as

Intraindividual variabilityLongitudinalMachine learningMedia effectsScreenomics

Identifiers

PMID37744988
PMCPMC10514010

What OpenQuestion holds

Textmetadata
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.