Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 382

The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.

Happy, sad, scary and peaceful musical excerpts for research on emotions

Vieillard, Sandrine, Peretz, Isabelle, Gosselin, Nathalie, Khalfa, Stéphanie, Gagnon, Lise, Bouchard, Bernard·2008·Cognition & Emotion

Abstract

Three experiments were conducted in order to validate 56 musical excerpts that conveyed four intended emotions (happiness, sadness, threat and peacefulness). In Experiment 1, the musical clips were rated in terms of how clearly the intended emotion was portrayed, and for valence and arousal. In Experiment 2, a gating paradigm was used to evaluate the course for emotion recognition. In Experiment 3, a dissimilarity judgement task and multidimensional scaling analysis were used to probe emotional content with no emotional labels. The results showed that emotions are easily recognised and discriminated on the basis of valence and arousal and with relative immediacy. Happy and sad excerpts were identified after the presentation of fewer than three musical events. With no labelling, emotion discrimination remained highly accurate and could be mapped on energetic and tense dimensions. The present study provides suitable musical material for research on emotions.Keywords.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/02699930701503567

Cites

58 of 58 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 58 references

Cited by

See all 382

Metadata

Authors
Vieillard, S., Peretz, I., Gosselin, N., Khalfa, S., Gagnon, L., & Bouchard, B.
Venue
Cognition & Emotion
Published
2008-06
Volume
22
Issue
4
Pages
720-752
ISSN
  • 0269-9931 (ISSN-L)
  • 1464-0600
OpenAlex
W2166067393
Cited by
382
References
58
FWCI
7.30
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Happy, sad, scary and peaceful musical excerpts for research on emotions. https://app.cochat.ai/papers/10.1080/02699930701503567
The paper
Vieillard, S., Peretz, I., Gosselin, N., Khalfa, S., Gagnon, L., & Bouchard, B. (2008). Happy, sad, scary and peaceful musical excerpts for research on emotions. Cognition & Emotion. https://doi.org/10.1080/02699930701503567

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.