scholarly journals Human Sound Localization Depends on Sound Intensity: Implications for Sensory Coding

2018 ◽  
Author(s):  
Antje Ihlefeld ◽  
Nima Alamatsaz ◽  
Robert M Shapley
Keyword(s):  
Sound Localization ◽  
Sound Intensity ◽  
Human Perception ◽  
Spatial Location ◽  
Spike Rate ◽  
Hemispheric Difference ◽  
Spatial Cues ◽  
Difference Model ◽  
Line Model ◽  
The Brain

A fundamental question of human perception is how we perceive target locations in space. Through our eyes and skin, the activation patterns of sensory organs provide rich spatial cues. However, for other sensory dimensions, including sound localization and visual depth perception, spatial locations must be computed by the brain. For instance, interaural time differences (ITDs) of the sounds reaching the ears allow listeners to localize sound in the horizontal plane. Our experiments tested two prevalent theories on how ITDs affect human sound localization: 1) the labelled-line model, encoding space through tuned representations of spatial location; versus 2) the hemispheric-difference model, representing space through spike-rate distances relative to a perceptual anchor. Unlike the labelled-line model, the hemispheric-difference model predicts that with decreasing intensity, sound localization should collapse toward midline reference, and this is what we observed behaviorally. These findings cast doubt on models of human sound localization that rely on a spatially tuned map. Moreover, analogous experimental results in vision indicate that perceived depth depends upon the contrast of the target. Based on our findings, we propose that the brain uses a canonical computation of location across sensory modalities: perceived location is encoded through population spike rate relative to baseline.

scholarly journals Population rate-coding predicts correctly that human sound localization depends on sound intensity

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Antje Ihlefeld ◽  
Nima Alamatsaz ◽  
Robert M Shapley
Keyword(s):  
Sound Localization ◽  
Sound Intensity ◽  
Spatial Location ◽  
Spike Rate ◽  
Behavioral Experiments ◽  
Interaural Time Differences ◽  
Sound Levels ◽  
Rate Coding ◽  
The Brain ◽  
Population Rate

Human sound localization is an important computation performed by the brain. Models of sound localization commonly assume that sound lateralization from interaural time differences is level invariant. Here we observe that two prevalent theories of sound localization make opposing predictions. The labelled-line model encodes location through tuned representations of spatial location and predicts that perceived direction is level invariant. In contrast, the hemispheric-difference model encodes location through spike-rate and predicts that perceived direction becomes medially biased at low sound levels. Here, behavioral experiments find that softer sounds are perceived closer to midline than louder sounds, favoring rate-coding models of human sound localization. Analogously, visual depth perception, which is based on interocular disparity, depends on the contrast of the target. The similar results in hearing and vision suggest that the brain may use a canonical computation of location: encoding perceived location through population spike rate relative to baseline.

Context dependence of receptive field remapping in superior colliculus

2011 ◽  
Vol 106 (4) ◽  
pp. 1862-1874 ◽  
Author(s):  
Jan Churan ◽  
Daniel Guitton ◽  
Christopher C. Pack
Keyword(s):  
Receptive Field ◽  
Superior Colliculus ◽  
Visual Stimulation ◽  
Receptive Fields ◽  
Spatial Location ◽  
Macaque Monkey ◽  
Visual Signals ◽  
Perceptual Stability ◽  
Visual Background ◽  
The Brain

Our perception of the positions of objects in our surroundings is surprisingly unaffected by movements of the eyes, head, and body. This suggests that the brain has a mechanism for maintaining perceptual stability, based either on the spatial relationships among visible objects or internal copies of its own motor commands. Strong evidence for the latter mechanism comes from the remapping of visual receptive fields that occurs around the time of a saccade. Remapping occurs when a single neuron responds to visual stimuli placed presaccadically in the spatial location that will be occupied by its receptive field after the completion of a saccade. Although evidence for remapping has been found in many brain areas, relatively little is known about how it interacts with sensory context. This interaction is important for understanding perceptual stability more generally, as the brain may rely on extraretinal signals or visual signals to different degrees in different contexts. Here, we have studied the interaction between visual stimulation and remapping by recording from single neurons in the superior colliculus of the macaque monkey, using several different visual stimulus conditions. We find that remapping responses are highly sensitive to low-level visual signals, with the overall luminance of the visual background exerting a particularly powerful influence. Specifically, although remapping was fairly common in complete darkness, such responses were usually decreased or abolished in the presence of modest background illumination. Thus the brain might make use of a strategy that emphasizes visual landmarks over extraretinal signals whenever the former are available.

scholarly journals Spontaneous perception: a framework for task-free, self-paced perception

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Shira Baror ◽  
Biyu J He
Keyword(s):  
Time Course ◽  
Visual Experience ◽  
Brain Activity ◽  
Human Perception ◽  
Future Research ◽  
Task Constraints ◽  
Spontaneous Brain Activity ◽  
The Brain ◽  
Coarse To Fine ◽  
Organizing Principles

Abstract Flipping through social media feeds, viewing exhibitions in a museum, or walking through the botanical gardens, people consistently choose to engage with and disengage from visual content. Yet, in most laboratory settings, the visual stimuli, their presentation duration, and the task at hand are all controlled by the researcher. Such settings largely overlook the spontaneous nature of human visual experience, in which perception takes place independently from specific task constraints and its time course is determined by the observer as a self-governing agent. Currently, much remains unknown about how spontaneous perceptual experiences unfold in the brain. Are all perceptual categories extracted during spontaneous perception? Does spontaneous perception inherently involve volition? Is spontaneous perception segmented into discrete episodes? How do different neural networks interact over time during spontaneous perception? These questions are imperative to understand our conscious visual experience in daily life. In this article we propose a framework for spontaneous perception. We first define spontaneous perception as a task-free and self-paced experience. We propose that spontaneous perception is guided by four organizing principles that grant it temporal and spatial structures. These principles include coarse-to-fine processing, continuity and segmentation, agency and volition, and associative processing. We provide key suggestions illustrating how these principles may interact with one another in guiding the multifaceted experience of spontaneous perception. We point to testable predictions derived from this framework, including (but not limited to) the roles of the default-mode network and slow cortical potentials in underlying spontaneous perception. We conclude by suggesting several outstanding questions for future research, extending the relevance of this framework to consciousness and spontaneous brain activity. In conclusion, the spontaneous perception framework proposed herein integrates components in human perception and cognition, which have been traditionally studied in isolation, and opens the door to understand how visual perception unfolds in its most natural context.

5 Models of the world: from perception to hallucination

2020 ◽  
Vol 91 (8) ◽  
pp. e2.3-e2
Author(s):  
Paul Fletcher
Keyword(s):  
Model Building ◽  
Processing System ◽  
Human Perception ◽  
Predictive Processing ◽  
External Reality ◽  
Consultant Psychiatrist ◽  
University Of Cambridge ◽  
The World ◽  
The University ◽  
The Brain

Paul Fletcher is Wellcome Investigator and Bernard Wolfe Professor of Health Neuroscience at the University of Cambridge. He is also Director of Studies for Preclinical Medicine at Clare College and Honorary Consultant Psychiatrist with the Cambridgeshire and Peterborough NHS Foundation Trust. He studied Medicine, before carrying out specialist training in Psychiatry and taking a PhD in cognitive neuroscience. He researches human perception, learning and decision-making in health and mental illness.We do not have direct contact with external reality. We must rely on messages from the sense organs, conveying information about the state of the world and our bodies. These messages are not easy to decipher, being noisy and ambiguous, but from them we have to construct models of the world. I will discuss this challenge and how we are very adept at creating a model of reality based on achieving a balance between what our senses are telling us and our expectations of what should be the case. This is often referred to as the predictive processing framework.Relying on this balance comes at a cost, rendering us vulnerable to illusions and biases and, in more extreme cases, to creating a reality that diverges from that experienced by others. This can arise for a variety of reasons but, at the root, I suggest, lies the nature of the brain as a model-building organ. Though this divergence from reality – psychosis – often seems inexplicable and incomprehensible, I suggest that a few core principles can help us to understand it and offers ways of thinking about how phenomena like hallucinations can be understood. Interestingly, the framework suggests ways in which apparently similar phenomena like hallucinations can arise from distinct alterations to the function of a predictive processing system.

Interdependence of Spatial and Temporal Coding in the Auditory Midbrain

2000 ◽  
Vol 83 (4) ◽  
pp. 2300-2314 ◽  
Author(s):  
U. Koch ◽  
B. Grothe
Keyword(s):  
Sound Localization ◽  
Feature Detection ◽  
Transfer Functions ◽  
Temporal Structure ◽  
Temporal Coding ◽  
Sound Recognition ◽  
Spatial Cues ◽  
Auditory Midbrain ◽  
Binaural Cues ◽  
Binaural Processing

To date, most physiological studies that investigated binaural auditory processing have addressed the topic rather exclusively in the context of sound localization. However, there is strong psychophysical evidence that binaural processing serves more than only sound localization. This raises the question of how binaural processing of spatial cues interacts with cues important for feature detection. The temporal structure of a sound is one such feature important for sound recognition. As a first approach, we investigated the influence of binaural cues on temporal processing in the mammalian auditory system. Here, we present evidence that binaural cues, namely interaural intensity differences (IIDs), have profound effects on filter properties for stimulus periodicity of auditory midbrain neurons in the echolocating big brown bat, Eptesicus fuscus. Our data indicate that these effects are partially due to changes in strength and timing of binaural inhibitory inputs. We measured filter characteristics for the periodicity (modulation frequency) of sinusoidally frequency modulated sounds (SFM) under different binaural conditions. As criteria, we used 50% filter cutoff frequencies of modulation transfer functions based on discharge rate as well as synchronicity of discharge to the sound envelope. The binaural conditions were contralateral stimulation only, equal stimulation at both ears (IID = 0 dB), and more intense at the ipsilateral ear (IID = −20, −30 dB). In 32% of neurons, the range of modulation frequencies the neurons responded to changed considerably comparing monaural and binaural (IID =0) stimulation. Moreover, in ∼50% of neurons the range of modulation frequencies was narrower when the ipsilateral ear was favored (IID = −20) compared with equal stimulation at both ears (IID = 0). In ∼10% of the neurons synchronization differed when comparing different binaural cues. Blockade of the GABAergic or glycinergic inputs to the cells recorded from revealed that inhibitory inputs were at least partially responsible for the observed changes in SFM filtering. In 25% of the neurons, drug application abolished those changes. Experiments using electronically introduced interaural time differences showed that the strength of ipsilaterally evoked inhibition increased with increasing modulation frequencies in one third of the cells tested. Thus glycinergic and GABAergic inhibition is at least one source responsible for the observed interdependence of temporal structure of a sound and spatial cues.

scholarly journals Accurate Sound Localization in Reverberant Environments Is Mediated by Robust Encoding of Spatial Cues in the Auditory Midbrain

Neuron ◽  
2009 ◽  
Vol 62 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Sasha Devore ◽  
Antje Ihlefeld ◽  
Kenneth Hancock ◽  
Barbara Shinn-Cunningham ◽  
Bertrand Delgutte
Keyword(s):  
Sound Localization ◽  
Spatial Cues ◽  
Auditory Midbrain ◽  
Reverberant Environments

scholarly journals Bimodal Cochlear Implant Listeners’ Ability to Perceive Minimal Audible Angle Differences

2019 ◽  
Vol 30 (08) ◽  
pp. 659-671 ◽  
Author(s):  
Ashley Zaleski-King ◽  
Matthew J. Goupell ◽  
Dragana Barac-Cikoja ◽  
Matthew Bakke
Keyword(s):  
Cochlear Implant ◽  
Sound Localization ◽  
Repeated Measures ◽  
Free Field ◽  
Low Frequency ◽  
Spatial Location ◽  
Speech Understanding ◽  
Repeated Measures Design ◽  
Acoustic Environments ◽  
The Right

AbstractBilateral inputs should ideally improve sound localization and speech understanding in noise. However, for many bimodal listeners [i.e., individuals using a cochlear implant (CI) with a contralateral hearing aid (HA)], such bilateral benefits are at best, inconsistent. The degree to which clinically available HA and CI devices can function together to preserve interaural time and level differences (ITDs and ILDs, respectively) enough to support the localization of sound sources is a question with important ramifications for speech understanding in complex acoustic environments.To determine if bimodal listeners are sensitive to changes in spatial location in a minimum audible angle (MAA) task.Repeated-measures design.Seven adult bimodal CI users (28–62 years). All listeners reported regular use of digital HA technology in the nonimplanted ear.Seven bimodal listeners were asked to balance the loudness of prerecorded single syllable utterances. The loudness-balanced stimuli were then presented via direct audio inputs of the two devices with an ITD applied. The task of the listener was to determine the perceived difference in processing delay (the interdevice delay [IDD]) between the CI and HA devices. Finally, virtual free-field MAA performance was measured for different spatial locations both with and without inclusion of the IDD correction, which was added with the intent to perceptually synchronize the devices.During the loudness-balancing task, all listeners required increased acoustic input to the HA relative to the CI most comfortable level to achieve equal interaural loudness. During the ITD task, three listeners could perceive changes in intracranial position by distinguishing sounds coming from the left or from the right hemifield; when the CI was delayed by 0.73, 0.67, or 1.7 msec, the signal lateralized from one side to the other. When MAA localization performance was assessed, only three of the seven listeners consistently achieved above-chance performance, even when an IDD correction was included. It is not clear whether the listeners who were able to consistently complete the MAA task did so via binaural comparison or by extracting monaural loudness cues. Four listeners could not perform the MAA task, even though they could have used a monaural loudness cue strategy.These data suggest that sound localization is extremely difficult for most bimodal listeners. This difficulty does not seem to be caused by large loudness imbalances and IDDs. Sound localization is best when performed via a binaural comparison, where frequency-matched inputs convey ITD and ILD information. Although low-frequency acoustic amplification with a HA when combined with a CI may produce an overlapping region of frequency-matched inputs and thus provide an opportunity for binaural comparisons for some bimodal listeners, our study showed that this may not be beneficial or useful for spatial location discrimination tasks. The inability of our listeners to use monaural-level cues to perform the MAA task highlights the difficulty of using a HA and CI together to glean information on the direction of a sound source.

Binaural Speech Understanding With Bilateral Cochlear Implants in Reverberation

2018 ◽  
Vol 27 (1) ◽  
pp. 85-94 ◽  
Author(s):  
Kostas Kokkinakis
Keyword(s):  
Sound Field ◽  
Spatial Location ◽  
Speech Understanding ◽  
Spatial Cues ◽  
Bilateral Cochlear Implants ◽  
Positive Effects ◽  
Spatial Release ◽  
Listening Environments ◽  
Speech Reception Thresholds ◽  
Release From Masking

PurposeThe purpose of this study was to investigate whether bilateral cochlear implant (CI) listeners who are fitted with clinical processors are able to benefit from binaural advantages under reverberant conditions. Another aim of this contribution was to determine whether the magnitude of each binaural advantage observed inside a highly reverberant environment differs significantly from the magnitude measured in a near-anechoic environment.MethodTen adults with postlingual deafness who are bilateral CI users fitted with either Nucleus 5 or Nucleus 6 clinical sound processors (Cochlear Corporation) participated in this study. Speech reception thresholds were measured in sound field and 2 different reverberation conditions (0.06 and 0.6 s) as a function of the listening condition (left, right, both) and the noise spatial location (left, front, right).ResultsThe presence of the binaural effects of head-shadow, squelch, summation, and spatial release from masking in the 2 different reverberation conditions tested was determined using nonparametric statistical analysis. In the bilateral population tested, when the ambient reverberation time was equal to 0.6 s, results indicated strong positive effects of head-shadow and a weaker spatial release from masking advantage, whereas binaural squelch and summation contributed no statistically significant benefit to bilateral performance under this acoustic condition. These findings are consistent with those of previous studies, which have demonstrated that head-shadow yields the most pronounced advantage in noise. The finding that spatial release from masking produced little to almost no benefit in bilateral listeners is consistent with the hypothesis that additive reverberation degrades spatial cues and negatively affects binaural performance.ConclusionsThe magnitude of 4 different binaural advantages was measured on the same group of bilateral CI subjects fitted with clinical processors in 2 different reverberation conditions. The results of this work demonstrate the impeding properties of reverberation on binaural speech understanding. In addition, results indicate that CI recipients who struggle in everyday listening environments are also more likely to benefit less in highly reverberant environments from their bilateral processors.

scholarly journals Incentive value and spatial certainty combine additively to determine visual priorities

2017 ◽  
Author(s):  
K.G. Garner ◽  
H. Bowman ◽  
J.E. Raymond
Keyword(s):  
Expected Value ◽  
Visual Selection ◽  
Visually Guided ◽  
Spatial Cues ◽  
Incentive Value ◽  
Model Selection Approach ◽  
Selection Approach ◽  
Combine Information ◽  
The Brain ◽  
Behavioural Evidence

AbstractHow does the brain combine information predictive of the value of a visually guided task (incentive value) with information predictive of where task relevant stimuli may occur (spatial certainty)? Human behavioural evidence indicates that these two predictions may be combined additively to bias visual selection (additive hypothesis), whereas neuroeconomic studies posit that they may be multiplicatively combined (expected value hypothesis). We sought to adjudicate between these two alternatives. Participants viewed two coloured placeholders that specified the potential value of correctly identifying an imminent letter target if it appeared in that placeholder. Then, prior to the target’s presentation, an endogenous spatial cue was presented indicating the target’s more likely location. Spatial cues were parametrically manipulated with regards to the information gained (in bits). Across two experiments, performance was better for targets appearing in high versus low value placeholders and better when targets appeared in validly cued locations. Interestingly, as shown with a Bayesian model selection approach, these effects did not interact, clearly supporting the additive hypothesis. Even when conditions were adjusted to increase the optimality of a multiplicative operation, support for it remained. These findings refute recent theories that expected value computations are the singular mechanism driving the deployment of endogenous spatial attention. Instead, incentive value and spatial certainty seem to act independently to influence visual selection.

scholarly journals Electrophysiological signatures of hierarchical learning

2021 ◽  
Author(s):  
Meng Liu ◽  
Wenshan Dong ◽  
Shaozheng Qin ◽  
Tom Verguts ◽  
Qi Chen
Keyword(s):  
Cognitive Process ◽  
Human Perception ◽  
Learning Task ◽  
Prediction Errors ◽  
Neural Basis ◽  
Hierarchical Learning ◽  
Low Level ◽  
High Level ◽  
The Brain ◽  
Better Than

AbstractHuman perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision making, remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter, HGF) with electrophysiological (EEG) recording, while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, non-hierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4-8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning, and encodes both low- and high-level learning signals separately and adaptively.

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