Biology subjects

Majdak, P.

Publications and source records attributed to Majdak, P..

3 recordsLinked to original sources

bioRxiv2024

Insights into dynamic sound localisation: A direction-dependent comparison between human listeners and a Bayesian model.

Self-motion is an essential but often overlooked component of sound localisation. While the directional information of a source is implicitly contained in head-centred acoustic cues, that acoustic input needs to be continuously combined with sensorimotor information about the head orientation in order to decode these cues to a world-centred frame of reference. On top of that, the use of head movement significantly reduces ambiguities in the directional information provided by the incoming sound. In this work, we evaluate a Bayesian model that predicts dynamic sound localisation, by comparing its predictions to human performance measured in a behavioural sound-localisation experiment. Model parameters were set a-priori, based on results from various psychoacoustic and sensorimotor studies, i.e., without any post-hoc parameter fitting to behavioral results. In a spatial analysis, we evaluated the models capability to predict spatial localisation responses. Further, we investigated specific effects of the stimulus duration, the spatial prior and sizes of various model uncertainties on the predictions. The spatial analysis revealed general agreement between the predictions and the actual behaviour. The altering of the model uncertainties and stimulus duration revealed a number of interesting effects providing new insights on modelling the human integration of acoustic and sensorimotor information in a localisation task. Author summaryIn everyday life, sound localisation requires both interaural and monaural acoustic information. In addition to this, sensorimotor information about the position of the head is required to create a stable and accurate representation of our acoustic environment. Bayesian inference is an effective mathematical framework to model how humans combine information from different sources and form beliefs about the world. Here, we compare the predictions from a Bayesian model for dynamic sound localisation with data from a localisation experiment. We show that we can derive the model parameter values from previous psychoacoustic and sensorimotor experiments and that the model without any post-hoc fitting, can predict general dynamic localisation performance. Finally, the discrepancies between the modelled data and behavioural data are analysed by testing the effects of adjusting the model parameters.

neuroscience↗

bioRxiv2022

A Bayesian model for human directional localization of broadband and static sound sources

Humans estimate sound-source directions by combining prior beliefs with sensory evidence. Prior beliefs represent statistical knowledge about the environment while sensory evidence is acquired from auditory features such as interaural disparities and monaural spectral shapes. Models of directional sound localization often impose constraints on the contribution of these features to either the horizontal or vertical dimension. Instead, we propose a Bayesian model that more flexibly incorporates each feature according to its spatial precision and integrates prior beliefs in the inference process. We applied the model to directional localization of a single, broadband, stationary sound source presented to a static human listener in an anechoic environment. We simplified interaural features to be broadband and compared two model variants, each considering a different type of monaural spectral features: magnitude profiles and gradient profiles. Both model variants were fitted to the baseline performance of five listeners and evaluated on the effects of localizing with non-individual head-related transfer functions (HRTFs) and sounds with rippled spectrum. The model variant with spectral gradient profiles outperformed other localization models. This model variant appears particularly useful for the evaluation of HRTFs and may serve as a basis for future extensions towards modeling dynamic listening conditions.

neuroscience↗

bioRxiv2020

Decision making in auditory externalization perception

Under natural conditions, listeners perceptually attribute sounds to external objects in their environment. This core function of perceptual inference is often distorted when sounds are produced via hearing devices such as headphones or hearing aids, resulting in sources being perceived unrealistically close or even inside the head. Psychoacoustic studies suggest a mixed role of various monaural and interaural cues contributing to the externalization process. We developed a model framework for perceptual externalization able to probe the contribution of cue-specific expectation errors and to contrast dynamic versus static strategies for combining those errors within static listening environments. Effects of reverberation and visual information were not considered. The model was applied to various acoustic distortions as tested under various spatially static conditions in five previous experiments. Most accurate predictions were obtained for the combination of monaural and interaural spectral cues with a fixed relative weighting (approximately 60% of monaural and 40% of interaural). That model version was able to reproduce the externalization rating of the five experiments with an average error of 12% (relative to the full rating scale). Further, our results suggest that auditory externalization in spatially static listening situations underlie a fixed weighting of monaural and interaural spectral cues, rather than a dynamic selection of those auditory cues.

neuroscience↗