Pupil-linked arousal does not differ between ‘white’, ‘pink’ and ‘brown’ noises

DOI

Pupillometry data associated with the paper: Erfanian et al (2025) "Pupil-linked arousal does not differ between ‘white’, ‘pink’ and ‘brown’ noises".See ReadMe file for details explaining the data.Abstract: ‘Coloured’ noises, such as white, pink, and brown noise, have gained attention in popular media as potential tools for enhancing memory consolidation, sleep quality, attentional focus, and more. These terms refer to distinct spectral slopes, which give rise to perceptually different noise stimuli. Although empirical research on their effects remains limited, a prevailing hypothesis suggests that their influence may be mediated by differential effects on arousal. In this study, we investigated this hypothesis using pupillometry, a physiological marker of autonomic nervous system activity. Thirty-eight participants were recruited, with data from 31 included in the analysis. Participants listened to three types of noise (white, pink, and brown), each presented for 10 s, while their pupil diameter was recorded. The results showed no significant modulation of pupil size across noise conditions. These findings suggest that, despite widespread claims about the distinct arousing or calming properties of coloured noises, they do not differentially affect sustained pupil-linked arousal in naïve listeners.

Identifier
DOI https://doi.org/10.5522/04/30284788.v1
Related Identifier HasPart https://ndownloader.figshare.com/files/58533556
Related Identifier HasPart https://ndownloader.figshare.com/files/58533577
Metadata Access https://api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:figshare.com:article/30284788
Provenance
Creator Chait, Maria ORCID logo; Erfanianghasab, Mercede
Publisher University College London UCL
Contributor Figshare
Publication Year 2025
Rights https://creativecommons.org/licenses/by/4.0/
OpenAccess true
Contact researchdatarepository(at)ucl.ac.uk
Representation
Language English
Resource Type Dataset
Discipline Other