In this section
@article{Baumgartner:2026:10.1177/23312165261465030,
author = {Baumgartner, R and Barumerli, R and Brands, B and Majdak, P},
doi = {10.1177/23312165261465030},
journal = {Trends Hear},
title = {Short-Term Statistical Learning Mitigates the Ill-Posed Problem of Sound Localization.},
url = {http://dx.doi.org/10.1177/23312165261465030},
volume = {30},
year = {2026}
}
TY - JOUR
AB - The dynamic interplay between source-specific spectral features and spatial cues is central to auditory inference. While sagittal-plane localization relies on direction-dependent spectral cues shaped by the listener's anatomy, sound sources themselves introduce spectral patterns that can obscure these cues, creating an ill-posed inference problem. We tested whether listeners can mitigate that problem by statistically learning a source's spectral shape over the short term. In a free-field localization task, participants localized ripple-spectrum sounds under two conditions: within a block, source spectra were either fixed (predictable) or randomized (unpredictable). Predictability reduced large-scale localization errors - such as front-back reversals and quadrant confusions - by up to 5% within minutes. These findings demonstrate that listeners exploit spectral consistency across stimulus history to adapt spatial decoding, providing empirical evidence for short-term updating of spectral priors and underscoring the adaptive nature of auditory inference.
AU - Baumgartner,R
AU - Barumerli,R
AU - Brands,B
AU - Majdak,P
DO - 10.1177/23312165261465030
PY - 2026///
TI - Short-Term Statistical Learning Mitigates the Ill-Posed Problem of Sound Localization.
T2 - Trends Hear
UR - http://dx.doi.org/10.1177/23312165261465030
UR - https://www.ncbi.nlm.nih.gov/pubmed/42422899
VL - 30
ER -