Neural Noise

fNIRS dataset of natural conversations about appearance

2 min read

  • A dataset of natural conversations about appearance using fNIRS has been reported (Nature Data, tier-2).1
  • It supports fNIRS-based decoding and neuroimaging methods for naturalistic dialogue.1 1

Gardner updates

  • A dataset of natural conversations about appearance using fNIRS was published in Nature (tier-2), supporting fNIRS-based decoding and neuroimaging methods. 1

Weekly enrichment (2026-07-20)

  • The data descriptor “Dataset of natural conversations about appearance using fNIRS” was published in Scientific Data (Nature portfolio; tier-2), DOI 10.1038/s41597-025-05574-9.2 3
  • Investigators recorded 40-channel fNIRS from 31 healthy female participants aged 18–21 years (mean 19.55 ± 0.89 years) using a portable near-infrared device covering primarily the frontal (prefrontal) and frontoparietal regions.2 3
  • The protocol induced self-objectification via “fat talk” (self-deprecating remarks about body/weight), bracketed before and after by appearance-unrelated conversations on topics such as travel experiences and room decoration.2
  • After each of the three conversations, participants completed questionnaires assessing body surveillance, body dissatisfaction, and emotional experience, and analyses showed increased body surveillance and dissatisfaction consistent with an activated self-objectification state.2 3
  • The optical recordings used dual wavelengths (760 nm and 850 nm) and are provided as oxygenated (HbO) and deoxygenated (HbR) hemoglobin time courses; reported analyses flagged altered activity in channels 7, 11, 12, and 27 after the fat talk.3
  • The released dataset comprises four data types—subjective reports, fNIRS data, behavioral data, and conversation transcripts—each spanning all 31 participants, and is shared open-access on the Open Science Framework (DOI 10.17605/OSF.IO/HMVSU).2 4
  • fNIRS is well suited to this naturalistic, conversational paradigm because it tolerates head and body motion far better than fMRI or EEG and is portable, enabling recording during free-flowing speech and social interaction.5
  • Such naturalistic fNIRS data support emerging analysis methods for interpersonal (hyperscanning) neuroscience—e.g., cross-recurrence quantification analysis capturing time-lagged, nonlinear inter-brain coupling during unscripted dialogue—extending decoding of social and affective states.6

Footnotes

  1. https://news.google.com/rss/articles/CBMiX0FVX3lxTFA1V3F1YjZ6T2Z4VDJ4SDlfR3c0dENFUzAxdE1PUVYtcDZIc00yZVItbVRmeXpkakhhVmhtNmx3OE9heTRKc1FmeEtPNEpia29NRUgwbzZpT2Q4N1k0NjA0?oc=5 ↩ ↩2 ↩3 ↩4

  2. https://www.nature.com/articles/s41597-025-05574-9 ↩ ↩2 ↩3 ↩4 ↩5

  3. https://pubmed.ncbi.nlm.nih.gov/40858626/ ↩ ↩2 ↩3 ↩4

  4. https://doi.org/10.17605/osf.io/hmvsu ↩

  5. https://pmc.ncbi.nlm.nih.gov/articles/PMC6367070/ ↩

  6. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1713357/full ↩

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