Evidence map›Paper›PMID 29789504›Full record

ArticleBrain sciences2018

The Sound of Words Evokes Affective Brain Responses.

Arash Aryani, Chun-Ting Hsu, Arthur M Jacobs

Abstract read
In one paragraph

Article in Brain sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Neuroimaging of valence decisions in children and adults.Developmental cognitive neuroscience · 2021
    Article
  7. Article
  8. Affective Arousal Links Sound to Meaning.Psychological science · 2020
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. 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

3 authors.

Arash AryaniDepartment of Experimental and Neurocognitive Psychology, Freie Universität Berlin, Habelschwerdter Allee 45, D⁻14195 Berlin, Germany. arash.aryani@fu-berlin.de.ORCID 0000-0002-0246-9422
Chun-Ting HsuDepartment of Psychology, Pennsylvania State University, PA 16802, USA. hsuchunting@gmail.com.ORCID 0000-0002-1829-6642
Arthur M JacobsDepartment of Experimental and Neurocognitive Psychology, Freie Universität Berlin, Habelschwerdter Allee 45, D⁻14195 Berlin, Germany. ajacobs@zedat.fu-berlin.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The long history of poetry and the arts, as well as recent empirical results suggest that the way a word sounds (e.g., soft vs. harsh) can convey affective information related to emotional responses (e.g., pleasantness vs. harshness). However, the neural correlates of the affective potential of the sound of words remain unknown. In an fMRI study involving passive listening, we focused on the affective dimension of arousal and presented words organized in two discrete groups of sublexical (i.e., sound) arousal (high vs. low), while controlling for lexical (i.e., semantic) arousal. Words sounding high arousing, compared to their low arousing counterparts, resulted in an enhanced BOLD signal in bilateral posterior insula, the right auditory and premotor cortex, and the right supramarginal gyrus. This finding provides first evidence on the neural correlates of affectivity in the sound of words. Given the similarity of this neural network to that of nonverbal emotional expressions and affective prosody, our results support a unifying view that suggests a core neural network underlying any type of affective sound processing.

Indexed as

affective potential of soundaffective soundneurocognitive poeticsphonoaestheticssound-meaningsublexical arousalunifying neural network

Identifiers

PMID29789504
PMCPMC6025608

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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.