Evidence map›Paper›PMID 41815739›Full record

Articlei-Perception

Context shapes evaluation of emotional valence, not emotional categorization: A fresh look at the Kuleshov effect.

Bence Szaszkó, Mark Andrej Loebus

Abstract read
In one paragraph

Article in i-Perception. 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

2 authors.

Bence SzaszkóPublic Mental Health Research Unit, Department of Social and Preventive Medicine, Medical University of Vienna, Austria.ORCID https://orcid.org/0000-0002-3173-3325
Mark Andrej LoebusDepartment of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In filmmaking, a neutral face paired with an emotional context can convey a congruent emotion-a phenomenon known as the Kuleshov effect. However, past research has yielded mixed findings on the existence of the Kuleshov effect, possibly due to methodological variability; we therefore implemented a boundary-test paradigm that combined normed static context images with dynamic facial stimuli separated by an explicit context-rating step to examine how visual context shapes the evaluation and categorization of faces under such conditions. We hypothesized that positive context would elevate facial valence ratings (and vice versa), while evoking different-than-neutral emotions in a neutral face when categorized. Thirty-two participants first rated the valence of a context image on a 7-point Likert scale. They then viewed a video of a neutral face, evaluated its valence on the same scale, and explicitly categorized the expression by selecting one of five predefined emotion labels (neutral, angry, happy, sad, disgusted). Analyses using linear mixed-effects models confirmed that positive contexts led to higher facial valence ratings, while negative contexts led to lower ones; by contrast, multinomial regression revealed no effect of context on emotional categorization. Our findings suggest that while visual context can bias evaluative judgments of facial expressions, it does not necessarily alter their emotional categorization under conditions where cinematic continuity is absent, highlighting the boundaries of the Kuleshov effect in line with previous studies.

Indexed as

contextemotion attributionfacial expressionKuleshov effect

Identifiers

PMID41815739
PMCPMC12972553

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.