A behaviourally normed database of 1,377 natural sounds for auditory cognition and neuroscience
Abstract
Natural-sound research requires stimulus sets that combine acoustic standardization with detailed behavioural characterization. We present 1,377 two-second sounds representing 240 expert-defined sourceaction classes. We call this database "MaMa Sounds", as it resulted from the collaborative effort of two academic teams in Maastricht and Marseille. The sounds were manually curated, segmented, sampled at 16 kHz, and labelled with a noun identifying the source and a verb identifying the action. We release deidentified trial-level identification and familiarity data together with multiple per-sound norms (e.g., identification accuracy, confidence and agreement; familiarity), along with overall norms derived with principal component analysis. Noun, verb, and joint noun-verb norms are provided as direct means and medians with the number of contributing observations. This battery preserves process-specific information, while two principal-component scores provide compact overall behavioural-identifiability measures derived from response ease, semantic correspondence, agreement, and familiarity. The repository also contains deterministic response-cleaning code, participant and reference Word2Vec representations, and code reproducing the public sound-level tables. The resource supports stimulus selection, matching, and continuous modelling in auditory cognition and neuroscience.
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BibTeX
@misc{plegat2026behaviourally,
title = {{A behaviourally normed database of 1,377 natural sounds for auditory cognition and neuroscience}},
author = {Marie Plegat and Maria Araújo Vitória and Giorgio Marinato and Beatrice Tita and Chris van der Lans and Minne Pijfers and Mia-Sara Bertovic and Michele Esposito and Elia Formisano and Bruno Giordano},
howpublished = {HAL},
year = {2026},
month = aug,
doi = {10.64898/2026.08.25.746933},
url = {https://hal.science/hal-05851310v1},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - A behaviourally normed database of 1,377 natural sounds for auditory cognition and neuroscience
AU - Marie Plegat
AU - Maria Araújo Vitória
AU - Giorgio Marinato
AU - Beatrice Tita
AU - Chris van der Lans
AU - Minne Pijfers
AU - Mia-Sara Bertovic
AU - Michele Esposito
AU - Elia Formisano
AU - Bruno Giordano
PY - 2026
DA - 2026/08/25
PB - HAL
DO - 10.64898/2026.08.25.746933
UR - https://hal.science/hal-05851310v1
AB - Natural-sound research requires stimulus sets that combine acoustic standardization with detailed behavioural characterization. We present 1,377 two-second sounds representing 240 expert-defined sourceaction classes. We call this database "MaMa Sounds", as it resulted from the collaborative effort of two academic teams in Maastricht and Marseille. The sounds were manually curated, segmented, sampled at 16 kHz, and labelled with a noun identifying the source and a verb identifying the action. We release deidentified trial-level identification and familiarity data together with multiple per-sound norms (e.g., identification accuracy, confidence and agreement; familiarity), along with overall norms derived with principal component analysis. Noun, verb, and joint noun-verb norms are provided as direct means and medians with the number of contributing observations. This battery preserves process-specific information, while two principal-component scores provide compact overall behavioural-identifiability measures derived from response ease, semantic correspondence, agreement, and familiarity. The repository also contains deterministic response-cleaning code, participant and reference Word2Vec representations, and code reproducing the public sound-level tables. The resource supports stimulus selection, matching, and continuous modelling in auditory cognition and neuroscience.
ER -