Toward a taxonomic model of attention in effortful listening

In recent years, there has been increasing interest in studying listening effort. Research on listening effort intersects with the development of active theories of speech perception and contributes to the broader endeavor of understanding speech perception within the context of neuroscientific theories of perception, attention, and effort. Due to the multidisciplinary nature of the problem, researchers vary widely in their precise conceptualization of the catch-all term listening effort. Very recent consensus work stresses the relationship between listening effort and the allocation of cognitive resources, providing a conceptual link to current cognitive neuropsychological theories associating effort with the allocation of selective attention. By linking listening effort to attentional effort, we enable the application of a taxonomy of external and internal attention to the characterization of effortful listening. More specifically, we use a vectorial model to decompose the demand causing listening effort into its mutually orthogonal external and internal components and map the relationship between demanded and exerted effort by means of a resource-limiting term that can represent the influence of motivation as well as vigilance and arousal. Due to its quantitative nature and easy graphical interpretation, this model can be applied to a broad range of problems dealing with listening effort. As such, we conclude that the model provides a good starting point for further research on effortful listening within a more differentiated neuropsychological framework

Neurodynamic evaluation of hearing aid features using EEG correlates of listening effort

In this study, we propose a novel estimate of listening effort using electroencephalographic data. This method is a translation of our past findings, gained from the evoked electroencephalographic activity, to the oscillatory EEG activity. To test this technique, electroencephalographic data from experienced hearing aid users with moderate hearing loss were recorded, wearing hearing aids. The investigated hearing aid settings were: a directional microphone combined with a noise reduction algorithm in a medium and a strong setting, the noise reduction setting turned off, and a setting using omnidirectional microphones without any noise reduction. The results suggest that the electroencephalographic estimate of listening effort seems to be a useful tool to map the exerted effort of the participants. In addition, the results indicate that a directional processing mode can reduce the listening effort in multitalker listening situations

Reducing the Effect of Spurious Phase Variations in Neural Oscillatory Signals

The phase-reset model of oscillatory EEG activity has received a lot of attention in the last decades for decoding different cognitive processes. Based on this model, the ERPs are assumed to be generated as a result of phase reorganization in ongoing EEG. Alignment of the phase of neuronal activities can be observed within or between different assemblies of neurons across the brain. Phase synchronization has been used to explore and understand perception, attentional binding and considering it in the domain of neuronal correlates of consciousness. The importance of the topic and its vast exploration in different domains of the neuroscience presses the need for appropriate tools and methods for measuring the level of phase synchronization of neuronal activities. Measuring the level of instantaneous phase (IP) synchronization has been used extensively in numerous studies of ERPs as well as oscillatory activity for a better understanding of the underlying cognitive binding with regard to different set of stimulations such as auditory and visual. However, the reliability of results can be challenged as a result of noise artifact in IP. Phase distortion due to environmental noise artifacts as well as different pre-processing steps on signals can lead to generation of artificial phase jumps. One of such effects presented recently is the effect of low envelope on the IP of signal. It has been shown that as the instantaneous envelope of the analytic signal approaches zero, the variations in the phase increase, effectively leading to abrupt transitions in the phase. These abrupt transitions can distort the phase synchronization results as they are not related to any neurophysiological effect. These transitions are called spurious phase variation. In this study, we present a model to remove generated artificial phase variations due to the effect of low envelope. The proposed method is based on a simplified form of a Kalman smoother, that is able to model the IP behavior in narrow-bandpassed oscillatory signals. In this work we first explain the details of the proposed Kalman smoother for modeling the dynamics of the phase variations in narrow-bandpassed signals and then evaluate it on a set of synthetic signals. Finally, we apply the model on ongoing-EEG signals to assess the removal of spurious phase variations

Effects of Agricultural Commercialization on Food Crop Input Use and Productivity in Kenya

The objective of this report is to analyze the effects of smallholder commercialization on food crop input use and productivity in rural Kenya. The main research issues were: (1) To examine the determinants of smallholder fertilizer use on food crops, with a focus on the effects of household and regional agricultural commercialization; (2) To examine the determinants of food crop productivity, again with a focus on the effects of commercialization; and (3) To discuss the implications of the findings for policy and additional research necessary to improve the contribution of cash cropping to rural food productivity growth and food security. A main premise of the paper is that the effects of commercialization are not uniform and cannot be generalized. The effects are hypothesized to differ both according to differences in the institutional/contractual arrangements between firms and smallholders, management decisions, and the level of credit and extension support provided to smallholders by the various private and parastatal firms involved in promoting smallholder cash crops.food security, food policy, food crop productivity, food crop input, Crop Production/Industries, Productivity Analysis, Downloads May 2008 - July 2009: 78, Q18,

Effects of Agricultural Commercialization on Food Crop Input Use and Productivity in Kenya

Crop Production/Industries, Downloads July 2008 - July 2009: 23,

Software for non-parametric image registration of 2-photon imaging data

Functional 2-photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non-rigid movement artifacts which requires high-accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2-photon neuroimaging data. In this work, we present the motion compensation method Flow-Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal-to-noise ratio 2-photon imaging data and is able to compensate high-divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy-to-use ImageJ/FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multi-channel support and compatibility with existing 2-photon

Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition

Motion magnification techniques aim at amplifying and hence revealing subtle motion in videos. There are basically two main approaches to reach this goal, namely via Eulerian or Lagrangian techniques. While the first one magnifies motion implicitly by operating directly on image pixels, the Lagrangian approach uses optical flow techniques to extract and amplify pixel trajectories. Microexpressions are fast and spatially small facial expressions that are difficult to detect. In this paper, we propose a novel approach for local Lagrangian motion magnification of facial micromovements. Our contribution is three-fold: first, we fine-tune the recurrent all-pairs field transforms for optical flows (RAFT) deep learning approach for faces by adding ground truth obtained from the variational dense inverse search (DIS) for optical flow algorithm applied to the CASME II video set of faces. This enables us to produce optical flows of facial videos in an efficient and sufficiently accurate way. Second, since facial micromovements are both local in space and time, we propose to approximate the optical flow field by sparse components both in space and time leading to a double sparse decomposition. Third, we use this decomposition to magnify micro-motions in specific areas of the face, where we introduce a new forward warping strategy using a triangular splitting of the image grid and barycentric interpolation of the RGB vectors at the corners of the transformed triangles. We demonstrate the very good performance of our approach by various examples

Bayesian Modeling of the Dynamics of Phase Modulations and their Application to Auditory Event Related Potentials at Different Loudness Scales

We study the effect of long-term habituation signatures of auditory selective attention reflected in the instantaneous phase information of the auditory event-related potentials (ERPs) at four distinct stimuli levels of 60, 70, 80, and 90 dB SPL. The analysis is based on the single-trial level. The effect of habituation can be observed in terms of the changes (jitter) in the instantaneous phase information of ERPs. In particular, the absence of habituation is correlated with a consistently high phase synchronization over ERP trials. We estimate the changes in phase concentration over trials using a Bayesian approach, in which the phase is modeled as being drawn from a von Mises distribution with a concentration parameter which varies smoothly over trials. The smoothness assumption reflects the fact that habituation is a gradual process. We differentiate between different stimuli based on the relative changes and absolute values of the estimated concentration parameter using the proposed Bayesian model