Reasons for ceiling ratings in real-life evaluations of hearing aids: the relationship between SNR and hearing aid ratings

IntroductionIn past Ecological Momentary Assessment (EMA) studies, hearing aid outcome ratings have often been close to ceiling.MethodsTo analyze the underlying reasons for the very positive ratings, we conducted a study with 17 experienced hearing aid wearers who were fitted with study hearing aids. The acceptable noise level and the noise level where participants were unable to follow speech were measured. The participants then rated hearing aid satisfaction, speech understanding and listening effort for pre-defined SNRs between −10 and +20 dB SPL in the laboratory. These ratings were compared to ratings of a two-week EMA trial. Additionally, estimates of SNRs were collected from hearing aids during the EMA trial and we assessed whether the participants experienced those SNRs rated poorly in the laboratory in real life.ResultsThe results showed that for hearing aid satisfaction and speech understanding, the full rating scale was used in the laboratory, while the ratings in real life were strongly skewed towards the positive end of the scale. In the laboratory, SNRs where participants indicated they could not follow the narrator (“unable to follow” noise level) were rated clearly better than the lowest possible ratings. This indicates that very negative ratings may not be applicable in real-life testing. The lower part of the distribution of real-life SNR estimates was related to participants’ individual “unable to follow” noise levels and the SNRs which were rated poorly in the laboratory made up less than 10% of the speech situations experienced in real life.DiscussionThis indicates that people do not seem to frequently experience listening situations at SNRs where they are dissatisfied with their hearing aids and this could be the reason for the overly positive hearing aid outcome ratings in EMA studies. It remains unclear to what extent the scarcity of such situations is due lack of encounters or intentional avoidance

An intuitive model of perceptual grouping for HCI design

ABSTRACT Understanding and exploiting the abilities of the human visual system is an important part of the design of usable user interfaces and information visualizations. Good design enables quick, easy and veridical perception of key components of that design. An important facet of human vision is its ability to seemingly effortlessly perform "perceptual organization"; it transforms individual feature estimates into perception of coherent regions, structures, and objects. We perceive regions grouped by proximity and feature similarity, grouping of curves by good continuation, and grouping of regions of coherent texture. In this paper, we discuss a simple model for a broad range of perceptual grouping phenomena. It takes as input an arbitrary image, and returns a structure describing the predicted visual organization of the image. We demonstrate that this model can capture aspects of traditional design rules, and predicts visual percepts in classic perceptual grouping displays

Optimality of Human Contour Integration

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy

Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation

Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far—ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or “priors.” In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available

Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina

When visual contrast changes, retinal ganglion cells adapt by adjusting their sensitivity as well as their temporal filtering characteristics. The latter has classically been described by contrast-induced gain changes that depend on temporal frequency. Here, we explored a new perspective on contrast-induced changes in temporal filtering by using spike-triggered covariance analysis to extract multiple parallel temporal filters for individual ganglion cells. Based on multielectrode-array recordings from ganglion cells in the isolated salamander retina, we found that contrast adaptation of temporal filtering can largely be captured by contrast-invariant sets of filters with contrast-dependent weights. Moreover, differences among the ganglion cells in the filter sets and their contrast-dependent contributions allowed us to phenomenologically distinguish three types of filter changes. The first type is characterized by newly emerging features at higher contrast, which can be reproduced by computational models that contain response-triggered gain-control mechanisms. The second type follows from stronger adaptation in the Off pathway as compared to the On pathway in On-Off-type ganglion cells. Finally, we found that, in a subset of neurons, contrast-induced filter changes are governed by particularly strong spike-timing dynamics, in particular by pronounced stimulus-dependent latency shifts that can be observed in these cells. Together, our results show that the contrast dependence of temporal filtering in retinal ganglion cells has a multifaceted phenomenology and that a multi-filter analysis can provide a useful basis for capturing the underlying signal-processing dynamics

Contour Integration Models Predicting Human Behavior

Contour integration is believed to be a fundamental process inobject recognition and image segmentation. However, its neuronalmechanisms are still not well understood. Psychophysical experimentsshowed that humans are remarkably efficient in integrating contourseven if these are jittered or partially occluded. Therefore thebrain requires a reliable algorithm for extracting contours fromstimuli. Several recent publications demonstrated that the brainoften uses optimal strategies to integrate sensory information.Hence in this thesis I want to tackle the question which contourintegration model describes human contour integration best.Mathematically, contour ensembles can be characterized by aconditional link probability density between oriented edge elements,termed an association field. This association field can be used togenerate contours or vice versa to extract a contour from astimulus. While in most neuronal network models all inputs to aneuron are summed up, in such a probabilistically motivated neuralnetwork for contour integration the afferent input due to the visualstimuli and the lateral input from horizontal network interactionsare multiplied.Long-range horizontal interactions in primary visual cortex linkorientation columns with similar preferred orientations and areoftenassumed to be the neuronal substrate for the association field. Experimental findings in monkeys suggest isotropic long-rangehorizontal connections, spreading symmetrically into all directionsfrom an orientation column. In contrast, probabilistic modelsrequire unidirectional lateral interactions, linking orientationcolumns in only one direction, in order to get optimal contourdetection performance.Using stimuli generated from given association fields, our numericalsimulations show that contour detection performance for both,probabilistic-multiplicative as well as additive models reacheshuman performance. Hence detection performance alone is insufficientto rule out either model class. However, psychophysical experimentswith humans reveal that contour detection errors are not maderandomly, but are highly correlated among different subjects. Thus amodel describing contour integration in the brain should not onlyexplain human contour detection performance, but should alsoreproduce these systematic errors made by humans. Comparison betweenmisdetections of humans and mispredictions of the models on atrial-by-trial basis was used to evaluate different model dynamicsand association fields. This suggests that unidirectionalmultiplicatively coupled horizontal interactions are required inorder to explain human behavior. Furthermore, cortical magnificationfactors have to be taken into account and a fixed association fieldgeometry for all stimuli is preferable instead of using for eachcontour the association field employed for the generation of thiscontour

Training listeners for multi-channel audio quality evaluation in MUSHRA with a special focus on loop setting

Audio quality evaluation for audio material of intermediate and high quality requires expert listeners. In comparison to non-experts, these are not only more critical in their ratings, but also employ different strategies in their evaluation. In particular they concentrate on shorter sections of the audio signal and compare more to the reference than inexperienced listeners. We created a listener training for detecting coding artifacts in multi-channel audio quality evaluation. Our training is targeted at listeners without technical background. For this training, expert listeners commented on smaller sections of an audio signal they focused on in the listening test and provided a description of the artifacts they perceived. The non-expert listeners participating in the training were provided with general advice for helpful strategies in MUSHRA tests (Multi Stimulus Tests with Hidden Reference and Anchor), with the comments on specific sections of the stimulus by the experts, and with feedback after rating. Listener's performance improved in the course of the training session. Afterwards they performed the same test without the training material and a further test with different items. Performance did not decrease in these tests, showing that they could transfer what they had learned to other stimuli. After the training they also set more loops and compared more to the reference

Konturintegrationsmodelle zur Vorhersage menschlichen Verhaltens

Contour integration is believed to be a fundamental process inobject recognition and image segmentation. However, its neuronalmechanisms are still not well understood. Psychophysical experimentsshowed that humans are remarkably efficient in integrating contourseven if these are jittered or partially occluded. Therefore thebrain requires a reliable algorithm for extracting contours fromstimuli. Several recent publications demonstrated that the brainoften uses optimal strategies to integrate sensory information.Hence in this thesis I want to tackle the question which contourintegration model describes human contour integration best.Mathematically, contour ensembles can be characterized by aconditional link probability density between oriented edge elements,termed an association field. This association field can be used togenerate contours or vice versa to extract a contour from astimulus. While in most neuronal network models all inputs to aneuron are summed up, in such a probabilistically motivated neuralnetwork for contour integration the afferent input due to the visualstimuli and the lateral input from horizontal network interactionsare multiplied.Long-range horizontal interactions in primary visual cortex linkorientation columns with similar preferred orientations and areoftenassumed to be the neuronal substrate for the association field. Experimental findings in monkeys suggest isotropic long-rangehorizontal connections, spreading symmetrically into all directionsfrom an orientation column. In contrast, probabilistic modelsrequire unidirectional lateral interactions, linking orientationcolumns in only one direction, in order to get optimal contourdetection performance.Using stimuli generated from given association fields, our numericalsimulations show that contour detection performance for both,probabilistic-multiplicative as well as additive models reacheshuman performance. Hence detection performance alone is insufficientto rule out either model class. However, psychophysical experimentswith humans reveal that contour detection errors are not maderandomly, but are highly correlated among different subjects. Thus amodel describing contour integration in the brain should not onlyexplain human contour detection performance, but should alsoreproduce these systematic errors made by humans. Comparison betweenmisdetections of humans and mispredictions of the models on atrial-by-trial basis was used to evaluate different model dynamicsand association fields. This suggests that unidirectionalmultiplicatively coupled horizontal interactions are required inorder to explain human behavior. Furthermore, cortical magnificationfactors have to be taken into account and a fixed association fieldgeometry for all stimuli is preferable instead of using for eachcontour the association field employed for the generation of thiscontour

Audio quality evaluation in MUSHRA tests - influences between loop setting and a listener's ratings

In many listening tests for audio quality evaluation the participants have the possibility to set loops, meaning they can focus on smaller parts of the audio signal and listen to those parts repeatedly. In previous papers, we already showed that experienced listeners set more loops than untrained ones and that learning to set loops in-creases the ability of a listener to perceive artifacts. Now we analyze to what extent the chosen loops vary from listener to listener and whether the ratings are influenced by the choice of loops. We show that - depending on the stimulus - listeners who set different loops may also give significantly different ratings