Biology subjects

Ernst, M. O.

Publications and source records attributed to Ernst, M. O..

10 recordsLinked to original sources

bioRxiv2026

Moving Heads, Moving Sounds: Systematic Errors in Auditory Motion Perception

The movement of moving objects seen during self-motion is incorrectly perceived as a result of errors in parsing the visual motion arising from the retina and from information about the movement of the head and eyes in space1. The perception of motion of auditory targets may be even more challenging as previous research suggests that the auditory system lacks dedicated low-level speed processing mechanisms. This study investigates how the perception of a moving auditory sound source is influenced by concurrent head movements, drawing comparisons with established visual motion perception findings and illusions, such as the Filehne Illusion. Participants were asked to move their heads at a fixed speed, either horizontally (left to right or right to left) or vertically (up to down or down to up), while indicating the perceived direction of a moving sound source by using a joystick. A physical sound source was moved silently at a constant speed in fronto-parallel 2D space using a 3x3x3m cable robot. Results showed that head movements created a perceptual bias of the perceived direction of the moving sound source in the opposite direction to the head movement. Responses were less precise during horizontal than vertical head movement, whereas overall directional accuracy did not differ reliably between movement planes. The magnitude of the directional error also depended on the direction of head movement, consistent with the different cues available for horizontal and vertical localization (binaural vs. spectral cues). These findings suggest that similarly to how visual motion perception is distorted by extraretinal signals and the movement of the eyes, so too is auditory motion perception influenced by movement of the ears, revealing errors in the integration of proprioceptive, vestibular and auditory cues that distort the perceived direction of a moving sound source.

animal behavior and cognition↗

bioRxiv2022

System identification reveals multiple interacting states in visuomotor adaptation

Many characteristics of sensorimotor adaptation are well predicted by the Kalman filter, a relatively simple learning algorithm for the optimal estimation of dynamic variables given noisy measurements. Yet not all Kalman filter predictions are confirmed empirically, suggesting that the model might not be sufficient to describe the observed behavior. In our study, we propose that sensorimotor adaptation can be modeled with multiple interacting states, each one described as a Kalman filter, to better reflect the architecture plant of the physical system implementing the behavior (i.e., the different motor and sensory components involved in adaptation). To test our hypothesis, we measured motor error in a series of rapid reaching tasks in which we introduced different conditions of feedback uncertainty and systematic perturbations. We then applied system identification procedures to the resulting adaptation response to test which system architecture would best fit the data. We considered three possible architectures: one with a single Kalman filter, with two filters in series, or two filters in parallel. We found that this latter structure consistently provided a significantly better fit than the others. When evaluating the identified system parameters, we found that the learning rates of both states decreased under higher uncertainty, while the weight assigned to the slower state increased. Moreover, the dual-state parallel system accounted for the presence of a constant residual bias in the adaptation to a step offset, which has been repeatedly reported but is in disaccord with the predictions of the single Kalman filter. We propose that the identified system architecture reflects how the error is assigned to different components of the physical plant responsible for adaptation: namely, as uncertainty increases, the controller assigns a larger contribution in error reduction to the slower state, which is more likely to have been responsible for the measured error.

neuroscience↗

bioRxiv2022

Psychophysics with R: The R Package MixedPsy

Psychophysical methods are widely used in neuroscience to investigate the quantitative relation between a physical property of the world and its perceptual representation provided by the senses. Recent studies introduced the Generalized Linear Mixed Model (GLMM) to fit the responses of multiple participants in psychophysical experiments. Another approach (two-level approach) requires fitting psychometric functions to each individual participant data using a Generalized Linear Model (GLM), and then testing the hypotheses on the multiple participants by means of a second level analysis. For either options, the implementation of the statistical analysis in R is possible and beneficial. Here, we introduce the package MixedPsy to model and fit psychometric data in R, either with two-level and GLMM approaches. The package, freely available in the CRAN repository, uses different methods for the estimation of Point of Subjective Equivalence (PSE) and Just Noticeable Difference (JND), and provides utilities for immediate visualization and plotting of the fitted results. This manuscript aims to provide researchers with a practical tutorial for implementing a complete analysis pipeline for psychophysical data using MixedPsy and other packages and basic functionalities of the R programming environment.

neuroscience↗

bioRxiv2022

Short-term monocular deprivation boosts neural responsiveness to audio-visual events for the undeprived eye

A brief period of monocular deprivation (MD) induces short-term plasticity of the adult visual system. Whether MD elicits changes beyond visual processing is yet unclear. Here, we assessed the specific impact of MD on multisensory processes. Neural oscillations associated with visual and audio-visual processing were measured for both the deprived and the undeprived eye. Results revealed that MD changed neural activities associated with unimodal and multisensory processes in an eye-specific manner. Selectively for the deprived eye, alpha activity was reduced within the first 150 ms of visual processing. Conversely, gamma activity was enhanced in response to audio-visual events only for the undeprived eye within 100-300 ms after stimulus onset. The analysis of gamma responses to unimodal auditory events revealed that MD elicited a crossmodal upweighting for the undeprived eye. Distributed source modeling suggested that the right parietal cortex played a major role in all neural effects induced by MD. Finally, visual and audio-visual processing alterations emerged selectively for the induced (but not the evoked) component of the neural oscillations, indicating a major role of feedback connectivity. These findings support a model in which MD increases excitability to visual events for the deprived eye and to audio-visual and auditory events for the undeprived eye. On the one hand, these results reveal the causal impact of MD on both unisensory and multisensory processes but with distinct frequency-specific profiles. On the other hand, they highlight the feedback nature of short-term neural plasticity. Overall this study shed light on the high flexibility and interdependence of unimodal and multisensory functions. Highlights- We unveiled the impact of temporary MD on visual and audio-visual processing - MD enhanced visual excitability for the deprived eye - MD boosted neural responses to audio-visual events for the undeprived eye - Analyses of auditory processing revealed crossmodal effects following MD - Short-term MD primarily affects induced, non-phase-locked, oscillatory activity

neuroscience↗

bioRxiv2021

Multisensory correlation computations in the human brain uncovered by a time-resolved encoding model

Neural mechanisms that arbitrate between integrating and segregating multisensory information are essential for complex scene analysis and for the resolution of the multisensory correspondence problem. However, these mechanisms and their dynamics remain largely unknown, partly because classical models of multisensory integration are static. Here, we used the Multisensory Correlation Detector, a model that provides a good explanatory power for human behavior while incorporating dynamic computations. Participants judged whether sequences of auditory and visual signals originated from the same source (causal inference) or whether one modality was leading the other (temporal order), while being recorded with magnetoencephalography. To test the match between the Multisensory Correlation Detector dynamics and the magnetoencephalographic recordings, we developed a novel dynamic encoding-model approach of electrophysiological activity, which relied on temporal response functions. First, we confirm that the Multisensory Correlation Detector explains causal inference and temporal order patterns well. Second, we found strong fits of brain activity to the two outputs of the Multisensory Correlation Detector in temporo-parietal cortices, a region with known multisensory integrative properties. Finally, we report an asymmetry in the goodness of the fits, which were more reliable during the causal inference than during the temporal order judgment task. Overall, our results suggest the plausible existence of multisensory correlation detectors in the human brain, which explain why and how causal inference is strongly driven by the temporal correlation of multisensory signals.

neuroscience↗

bioRxiv2020

Binaural Signal Integration Improves Vertical Sound Source Localization

Early studies have shown that the localization of a sound source in the vertical plane can be accomplished with only a single ear, thus assumed the localization mechanism to be based on monaural cues. Such cues are induced by the pinna and consist of notches and peaks in the perceived spectrum which vary systematically with the elevation of sound sources. These processes pose several problems to the auditory system like identifying and extracting spectral cues on a neural level, as well as, distinguishing pinna induced peaks and notches from features already present in the source spectrum. Interestingly, at the stage of elevation estimate binaural information from both ears is already available and it seems plausible that the auditory system takes advantage of this information. Especially, since such a binaural integration can improve the localization performance dramatically as we demonstrate in the current study. For that, we first introduce a computational model architecture that takes advantage of binaural signal integration to localize sound sources in the median plane. Model performance is tested under different conditions which reveal that localization of monaural, as well as binaural inputs is best when the model is trained with binaural inputs. Furthermore, modeling results lead to the hypothesis that sound type specific prior information is taken into account to further improve localization quality. This deduced hypothesis about vertical sound source localization is confirmed in a behavioral experiment. Based on these results, we propose that elevation estimation of sound sources is facilitated by an early binaural signal integration and can incorporate sound type specific prior information for higher accuracy.

neuroscience↗

bioRxiv2020

When vision lags, motor prediction follows

When we act, our brains register the visual feedback of the outcome of our actions only approximately 150 ms after sending the motor commands to the muscles. By anticipating the sensory consequences of our actions, temporal prediction mechanisms help us to accurately perform time-critical motor actions, such as catching a ball, and to experience the world as temporally coherent. It remains unclear whether these prediction mechanisms can adapt in a general way to changing sensory feedback delays, as they occur, for instance, when neural processing times increase during development. We here used Augmented Reality to train participants for one hour in a range of everyday life tasks with different levels of artificial visual feedback delay: 0 ms (No Delay), 100 ms (Small Delay) and 250 ms (Large Delay). We tested whether temporal aftereffects (increased motor-anticipation) generalize across different perceptual and motor tasks that participants did not practice during training and that involve different aspects of sensorimotor integration (motor planning, feedback control, sensorimotor time perception). Our results show a consistent temporal aftereffect of 33% across tasks and levels of delay. This suggests that the human sensorimotor system can adapt to the presence of visual feedback delays in a that affects sensorimotor prediction in general.

neuroscience↗

bioRxiv2020

Computational Investigation of Visually Guided Learningof Spatially Aligned Auditory Maps in the Colliculus

The development of spatially registered auditory maps in the external nucleus of the inferior colliculus in young owls and their maintenance in adult animals is visually guided and evolves dynamically. To investigate the underlying neural mechanisms of this process, we developed a model of stabilized neoHebbian correlative learning which is augmented by an eligibility signal and a temporal trace of activations. This 3-component learning algorithm facilitates stable, yet flexible, formation of spatially registered auditory space maps composed of conductance-based topographically organized neu- ral units. Spatially aligned maps are learned for visual and auditory input stimuli that arrive in temporal and spatial registration. The reliability of visual sensory inputs can be used to regulate the learning rate in the form of an eligibility trace. We show that by shifting visual sensory inputs at the onset of learning the topography of auditory space maps is shifted accordingly. Simulation results explain why a shift of auditory maps in mature animals is possible only if corrections are induced in small steps. We conclude that learning spatially aligned auditory maps is flexibly controlled by reliable visual sensory neurons and can be formalized by a biological plausible unsupervised learning mechanism.

neuroscience↗

bioRxiv2019

Computational principles of neural adaptation for binaural signal integration

Adaptation to statistics of sensory inputs is an essential ability of neural systems and extends their effective operational range. Having a broad operational range facilitates to react to sensory inputs of different granularities, thus is a crucial factor for survival. The computation of auditory cues for spatial localization of sound sources, particularly the interaural level difference (ILD), has long been considered as a static process. Novel findings suggest that this process of ipsi- and contra-lateral signal integration is highly adaptive and depends strongly on recent stimulus statistics. Here, adaptation aids the encoding of auditory perceptual space of various granularities. To investigate the mechanism of auditory adaptation in binaural signal integration in detail, we developed a neural model architecture for simulating functions of lateral superior olive (LSO) and medial nucleus of the trapezoid body (MNTB) composed of single compartment conductance-based neurons. Neurons in the MNTB serve as an intermediate relay population. Their signal is integrated by the LSO population on a circuit level to represent excitatory and inhibitory interactions of input signals. The circuit incorporates an adaptation mechanism operating at the synaptic level based on local inhibitory feedback signals. The models predictive power is demonstrated in various simulations replicating physiological data. Incorporating the innovative adaptation mechanism facilitates a shift in neural responses towards the most effective stimulus range based on recent stimulus history. The model demonstrates that a single LSO neuron quickly adapts to these stimulus statistics and, thus, can encode an extended range of ILDs in the ipsilateral hemisphere. Most significantly, we provide a unique measurement of the adaptation efficacy of LSO neurons. Prerequisite of normal function is an accurate interaction of inhibitory and excitatory signals, a precise encoding of time and a well-tuned local feedback circuit. We suggest that the mechanisms of temporal competitive-cooperative interaction and the local feedback mechanism jointly sensitize the circuit to enable a response shift towards contra-lateral and ipsi-lateral stimuli, respectively. Author summaryWhy are we more precise in localizing a sound after hearing it several times? Adaptation to the statistics of a stimulus plays a crucial role in this. The present article investigates the abilities of a neural adaptation mechanism for improved localization skills based on a neural network model. Adaptation to stimulus statistics is very prominent in sensory systems of animals and allows them to respond to a wide range of stimuli, thus is a crucial factor for survival. For example, humans are able to navigate under suddenly changing illumination conditions (driving a car into and out of a tunnel). This is possible by courtesy of adaptation abilities of our sensory organs and pathways. Certainly, adaptation is not confined to a single sense like vision but also affects other senses like audition. Especially the perception of sound source location. Compared to vision, the localization of a sound source in the horizontal plane is a rather complicated task since the location cannot be read out from the receptor surface but needs to be computed. This requires the underlying neural system to calculate differences of the intensity between the two ears which provide a distinct cue for the location of a sound source. Here, adaptation to this cue allows to focus on a specific part of auditory space and thereby facilitates improved localisation abilities. Based on recent findings that suggest that the intensity difference computation is a flexible process with distinct adaptation mechanisms, we developed a neural model that computes the intensity difference to two incoming sound signals. The model comprises a novel mechanism for adaptation to sound source locations and provides a means to investigate underlying neural principles of adaptation and compare their effectivenesses. We demonstrate that due this mechanism the perceptual range is extended and a finer resolution of auditory space is obtained. Results explain the neural basis for adaptation and indicate that the interplay between different adaptation mechanisms facilitate highly precise sound source localization in a wide range of locations.

neuroscience↗

bioRxiv2019

The discrimination of tactile speed in absence of texture elements: Role of deformation and vibratory cues

Motion encoding in touch relies on multiple cues, such as displacements of traceable texture elements, friction-induced vibrations, and gross fingertip deformations by shear force. We evaluated the role of deformation and vibration cues in tactile speed discrimination. To this end, we tested the discrimination of speed of a moving smooth glass plate, and compared the precision of the responses when the same task was performed with a plate having a fine texture. Participants performed the task with and without masking vibrations. Speed discrimination was nearly as precise among the two surface types, as assessed by the steep slope of the psychometric function. Consistent with our previous work, high-frequency vibrations impaired the ability of the participants in discriminating surface speed. Results of the current study showed that it is possible to discriminate motion speed even in absence of a raised texture. HighlightsO_LIOn a smooth surface, humans are able to discriminate the speed of a moving surface by frictional motion cues C_LIO_LIThe precision of speed discrimination is nearly the same with smooth and fine-textured surface types C_LIO_LIHigh frequency vibrations impair the ability to discriminate speed of moving surfaces C_LI

neuroscience↗