Short-Term Statistical Learning Mitigates the Ill-Posed Problem of Sound Localization.

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 the...

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Publicado en:Trends in Hearing Vol. 30; pp. 1 - 12
Autores principales: Baumgartner, Robert, Barumerli, Roberto, Brands, Benedikt, Majdak, Piotr
Formato: research tables/charts Journal Article
Publicado: Sage Publications Inc. 7/9/2026
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 7/9/2026
      vid: 30
      pid: 344
      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        10.1177/23312165261465030
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        atl: Short-Term Statistical Learning Mitigates the Ill-Posed Problem of Sound Localization.
      aug:
        au:
          Baumgartner, Robert
          Barumerli, Roberto
          Brands, Benedikt
          Majdak, Piotr
        affil: Acoustics Research Institute, Austrian Academy of Sciences, Vienna, Austria
      sug:
        subj:
          Sound Localization Evaluation
          Learning Methods Evaluation
          Funding Source
          Austria
          Human
          Male
          Female
          Adult
          Audiometry, Pure-Tone
          Analysis of Variance
          Descriptive Statistics
          Data Analysis Software
          Post Hoc Analysis
          Neuronal Plasticity
          Cognition
          Adaptation, Physiological
          Spatial Behavior
          Hearing Aids
          Auditory Perception
          Adult: 19-44 years
          Male
          Female
      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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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