Evidence map›Paper›PMID 37205049›Full record

ArticleFrontiers in neuroscience2023

Automatic facial coding predicts self-report of emotion, advertisement and brand effects elicited by video commercials.

T Tim A Höfling, Georg W Alpers

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 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. Article
  3. Article
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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.

T Tim A HöflingDepartment of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany.
Georg W AlpersDepartment of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Consumers' emotional responses are the prime target for marketing commercials. Facial expressions provide information about a person's emotional state and technological advances have enabled machines to automatically decode them. Method: With automatic facial coding we investigated the relationships between facial movements (i.e., action unit activity) and self-report of commercials advertisement emotion, advertisement and brand effects. Therefore, we recorded and analyzed the facial responses of 219 participants while they watched a broad array of video commercials. Results: Facial expressions significantly predicted self-report of emotion as well as advertisement and brand effects. Interestingly, facial expressions had incremental value beyond self-report of emotion in the prediction of advertisement and brand effects. Hence, automatic facial coding appears to be useful as a non-verbal quantification of advertisement effects beyond self-report. Discussion: This is the first study to measure a broad spectrum of automatically scored facial responses to video commercials. Automatic facial coding is a promising non-invasive and non-verbal method to measure emotional responses in marketing.

Indexed as

action units (AU)advertisementautomatic facial codingbrandemotionfacial expressionFACSsemiparametric additive mixed models

Identifiers

PMID37205049
PMCPMC10185761

What OpenQuestion holds

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Registered trials

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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.