Dataset Information

A novel concept for dynamic adjustment of auditory space.

ABSTRACT: Traditionally, the auditory system is thought to serve reliable sound localization. Stimulus-history driven feedback circuits in the early binaural pathway, however, contradict this canonical concept and raise questions about their functional significance. Here we show that stimulus-history dependent changes in absolute space perception are poorly captured by the traditional labeled-line and hemispheric-difference models of auditory space coding. We therefore developed a new decoding model incorporating recent electrophysiological findings in which sound location is initially computed in both brain hemispheres independently and combined to yield a hemispherically balanced code. This model closely captures the observed absolute localization errors caused by stimulus history, and furthermore predicts a selective dilation and compression of perceptional space. These model predictions are confirmed by improvement and degradation of spatial resolution in human listeners. Thus, dynamic perception of auditory space facilitates focal sound source segregation at the expense of absolute sound localization, questioning existing concepts of spatial hearing.

SUBMITTER: Lingner A

PROVIDER: S-EPMC5974081 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

A novel concept for dynamic adjustment of auditory space.

Lingner A A Pecka M M Leibold C C Grothe B B

Scientific reports 20180529 1

Traditionally, the auditory system is thought to serve reliable sound localization. Stimulus-history driven feedback circuits in the early binaural pathway, however, contradict this canonical concept and raise questions about their functional significance. Here we show that stimulus-history dependent changes in absolute space perception are poorly captured by the traditional labeled-line and hemispheric-difference models of auditory space coding. We therefore developed a new decoding model incor ...[more]

PMID: 29844516

Similar Datasets

Project description:Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and to utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting. We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters from state-space observations. We apply our algorithm to simulated and experimentally-recorded MEG data from auditory experiments in the cocktail party paradigm to estimate the underlying dynamic Temporal Response Functions (TRFs). Our simulation results show that the richer representation of the process noise as a Gaussian mixture significantly improves state estimation and capturing the heterogeneity of the TRF dynamics. Application to MEG data reveals improvements over existing TRF estimation techniques, and provides a reliable alternative to current approaches for probing neural dynamics in a cocktail party scenario, as well as attention decoding in emerging applications such as smart hearing aids. Our proposed methodology provides a framework for efficient inference of Gaussian mixture process noise models, with application to a wide range of biological data with underlying heterogeneous and latent dynamics.

Project description:BackgroundSpatial inputs from the auditory periphery can be changed with movements of the head or whole body relative to the sound source. Nevertheless, humans can perceive a stable auditory environment and appropriately react to a sound source. This suggests that the inputs are reinterpreted in the brain, while being integrated with information on the movements. Little is known, however, about how these movements modulate auditory perceptual processing. Here, we investigate the effect of the linear acceleration on auditory space representation.Methodology/principal findingsParticipants were passively transported forward/backward at constant accelerations using a robotic wheelchair. An array of loudspeakers was aligned parallel to the motion direction along a wall to the right of the listener. A short noise burst was presented during the self-motion from one of the loudspeakers when the listener's physical coronal plane reached the location of one of the speakers (null point). In Experiments 1 and 2, the participants indicated which direction the sound was presented, forward or backward relative to their subjective coronal plane. The results showed that the sound position aligned with the subjective coronal plane was displaced ahead of the null point only during forward self-motion and that the magnitude of the displacement increased with increasing the acceleration. Experiment 3 investigated the structure of the auditory space in the traveling direction during forward self-motion. The sounds were presented at various distances from the null point. The participants indicated the perceived sound location by pointing a rod. All the sounds that were actually located in the traveling direction were perceived as being biased towards the null point.Conclusions/significanceThese results suggest a distortion of the auditory space in the direction of movement during forward self-motion. The underlying mechanism might involve anticipatory spatial shifts in the auditory receptive field locations driven by afferent signals from vestibular system.

Project description:BackgroundPrevious findings have shown that humans can learn to localize with altered auditory space cues. Here we analyze such learning processes and their effects up to one month on both localization accuracy and sound externalization. Subjects were trained and retested, focusing on the effects of stimulus type in learning, stimulus type in localization, stimulus position, previous experience, externalization levels, and time.MethodWe trained listeners in azimuth and elevation discrimination in two experiments. Half participated in the azimuth experiment first and half in the elevation first. In each experiment, half were trained in speech sounds and half in white noise. Retests were performed at several time intervals: just after training and one hour, one day, one week and one month later. In a control condition, we tested the effect of systematic retesting over time with post-tests only after training and either one day, one week, or one month later.ResultsWith training all participants lowered their localization errors. This benefit was still present one month after training. Participants were more accurate in the second training phase, revealing an effect of previous experience on a different task. Training with white noise led to better results than training with speech sounds. Moreover, the training benefit generalized to untrained stimulus-position pairs. Throughout the post-tests externalization levels increased. In the control condition the long-term localization improvement was not lower without additional contact with the trained sounds, but externalization levels were lower.ConclusionOur findings suggest that humans adapt easily to altered auditory space cues and that such adaptation spreads to untrained positions and sound types. We propose that such learning depends on all available cues, but each cue type might be learned and retrieved differently. The process of localization learning is global, not limited to stimulus-position pairs, and it differs from externalization processes.

Dataset Information

A novel concept for dynamic adjustment of auditory space.

Publications

A novel concept for dynamic adjustment of auditory space.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure