Selective attention and speech processing in the cortex

In noisy and complex environments, human listeners must segregate the mixture of sound sources arriving at their ears and selectively attend a single source, thereby solving a computationally difficult problem called the cocktail party problem. However, the neural mechanisms underlying these computations are still largely a mystery. Oscillatory synchronization of neuronal activity between cortical areas is thought to provide a crucial role in facilitating information transmission between spatially separated populations of neurons, enabling the formation of functional networks. In this thesis, we seek to analyze and model the functional neuronal networks underlying attention to speech stimuli and find that the Frontal Eye Fields play a central 'hub' role in the auditory spatial attention network in a cocktail party experiment. We use magnetoencephalography (MEG) to measure neural signals with high temporal precision, while sampling from the whole cortex. However, several methodological issues arise when undertaking functional connectivity analysis with MEG data. Specifically, volume conduction of electrical and magnetic fields in the brain complicates interpretation of results. We compare several approaches through simulations, and analyze the trade-offs among various measures of neural phase-locking in the presence of volume conduction. We use these insights to study functional networks in a cocktail party experiment. We then construct a linear dynamical system model of neural responses to ongoing speech. Using this model, we are able to correctly predict which of two speakers is being attended by a listener. We then apply this model to data from a task where people were attending to stories with synchronous and scrambled videos of the speakers' faces to explore how the presence of visual information modifies the underlying neuronal mechanisms of speech perception. This model allows us to probe neural processes as subjects listen to long stimuli, without the need for a trial-based experimental design. We model the neural activity with latent states, and model the neural noise spectrum and functional connectivity with multivariate autoregressive dynamics, along with impulse responses for external stimulus processing. We also develop a new regularized Expectation-Maximization (EM) algorithm to fit this model to electroencephalography (EEG) data

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

Brain oscillations and connectivity in autism spectrum disorders (ASD):new approaches to methodology, measurement and modelling

Although atypical social behaviour remains a key characterisation of ASD, the presence ofsensory and perceptual abnormalities has been given a more central role in recentclassification changes. An understanding of the origins of such aberrations could thus prove afruitful focus for ASD research. Early neurocognitive models of ASD suggested that thestudy of high frequency activity in the brain as a measure of cortical connectivity mightprovide the key to understanding the neural correlates of sensory and perceptual deviations inASD. As our review shows, the findings from subsequent research have been inconsistent,with a lack of agreement about the nature of any high frequency disturbances in ASD brains.Based on the application of new techniques using more sophisticated measures of brainsynchronisation, direction of information flow, and invoking the coupling between high andlow frequency bands, we propose a framework which could reconcile apparently conflictingfindings in this area and would be consistent both with emerging neurocognitive models ofautism and with the heterogeneity of the condition

From features to concepts: tracking the neural dynamics of visual perception

The visual system is thought to accomplish categorization through a series of hi- erarchical feature extraction steps, ending with the formation of high-level cate- gory representations in occipitotemporal cortex; however, recent evidence has chal- lenged these assumptions. The experiments described in this thesis address the question of categorization in face and scene perception using magnetoencephalog- raphy and multivariate analysis methods. The first three chapters investigate neural responses to emotional faces from different perspectives, by varying their relevance to task. First, in a passive view- ing paradigm, angry faces elicit differential patterns within 100 ms in visual cortex, consistent with a threat-related bias in feedforward processing. The next chap- ter looks at rapid face perception in the context of an expression discrimination task which also manipulates subjective awareness. A neural response to faces, but not expressions is detected outside awareness. Furthermore, neural patterns and behavioural responses are shown to reflect both facial features and facial config- uration. Finally, the third chapter employs emotional faces as distractors during an orientation discrimination task, but finds no evidence of expression processing outside of attention. The fourth chapter focuses on natural scene perception, using a passive view- ing paradigm to study the contribution of low-level features and high-level cat- egories to MEG patterns. Multivariate analyses reveal a categorical response to scenes emerging within 200 ms, despite ongoing processing of low-level features. Together, these results suggest that feature-based coding of categories, opti- mized for both stimulus relevance and task demands, underpins dynamic high- level representations in the visual system. The findings highlight new avenues in vision research, which may be best pursued by bridging the neural and behavioural levels within a common computational framework

An Introduction to Zooarchaeology

zooarchaeology is a self-reproducing field taught in many university departments of anthropology or archaeology. As archaeologists have literally taken faunal analysis into their own hands, they have debated how best to use animal remains to study everything from early hominin hunting or scavenging to animal production in ancient market economies. Animal remains from archaeological sites have been used to infer three kinds of information: the age of deposits (chronology); paleoenvironment and paleoecological relations among humans and other species; human choices and actions related to use of animals as food and raw materials. Methods for reconstructing human diet and behavior have undergone the greatest growth over the last four decades, and most of this book addresses the second and third areas. This book deals with what I know best: vertebrate zooarchaeology, and within that, analysis of mammalian bones and teeth

Digital Manipulation of Human Faces: Effects on Emotional Perception and Brain Activity

The study of human face-processing has granted insight into key adaptions across various social and biological functions. However, there is an overall lack of consistency regarding digital alteration styles of human-face stimuli. In order to investigate this, two independent studies were conducted examining unique effects of image construction and presentation. In the first study, three primary forms of stimuli presentation styles (color, black and white, cutout) were used across iterations of non-thatcherized/thatcherized and non-inverted/inverted presentations. Outcome measures included subjective reactions measured via ratings of perceived “grotesqueness,” and objective outcomes of N170 event-related potentials (ERPs) measured via encephalography. Results of subjective measures indicated that thatcherized images were associated with an increased level of grotesque perception, regardless of overall condition variant and inversion status. A significantly larger N170 component was found in response to cutout-style images of human faces, thatcherized images, and inverted images. Results suggest that cutout image morphology may be considered a well-suited image presentation style when examining ERPs and facial processing of otherwise unaltered human faces. Moreover, less emphasis can be placed on decision making regarding main condition morphology of human face stimuli as it relates to negatively valent reactions. The second study explored commonalities between thatcherized and uncanny images. The purpose of the study was to explore commonalities between these two styles of digital manipulation and establish a link between previously disparate areas of human-face processing research. Subjective reactions to stimuli were measured via participant ratings of “off-putting.” ERP data were gathered in order to explore if any unique effects emerged via N170 and N400 presentations. Two main “morph continuums” of stimuli, provided by Eduard Zell (see Zell et al., 2015), with uncanny features were utilized. A novel approach of thatcherizing images along these continuums was used. thatcherized images across both continuums were regarded as more off-putting than non-thatcherized images, indicating a robust subjective effect of thatcherization that was relatively unimpacted by additional manipulation of key featural components. Conversely, results from brain activity indicated no significant differences of N170 between level of shape stylization and their thatcherized counterparts. Unique effects between continuums and exploratory N400 results are discussed

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Model integration. Integrated socio-economic model on food waste

The model architecture described in this deliverable provides the framework through which data and simulations from the data on food waste at a consumer level and at a retail level can be integrated into simulation models. This report highlights the technical approaches followed to achieve model integration. This report highlights the technical approaches followed to achieve model integration. An integrated whole-of-system modelling approach will be developed as a part of the REFRESH project to allow the development of a decision-relevant, and dynamic policy support tool, by which a road map to the reduction of European food waste by 50% by 2030 can be developed. The vital first step (highlighted in this report) is to develop the structures to allow model integration between different model types: Agent-Based Models and Bayesian Networks. These structures were developed and tested to ensure that the model types can be integrated. The architecture described in this deliverable provides the framework through which data and simulations from the data on food waste at a consumer level and at a retail level can be integrated into simulation models. Since a sizable share of the food waste is generated either at the consumer level or at the interaction between consumers and retailers, we address the modelling effort with two integrated ABM-BN models. The first model reproduces the dynamic evolution of food waste choices of consumers as consequence of social interactions. The second focuses instead on the conditions for the successful diffusion and adoption of innovations to reduce food waste at the retailer level. The systemic modelling approach proposed will allow the development of selected simulation scenarios at the consumer and retail level, facilitating decision making in the face of uncertainty. These integrated setups are first iterations of working integrated models, aimed at validating technically the setups as well as the integration process itself. As they are, there are certainly factors that are likely to be important in determining food waste, which are not yet included in the models. However, the latter are flexible and can accommodate further details, and variables. Their construction is purposefully flexible in terms of components of decisions. The integration with Bayesian Networks ensure that Agent-Based models will learn from data originated from the other refresh WPs and will evolve, allowing the introduction of new variables and factors that will lead to the improvement of the different simulation scenarios. The REFRESH project implements a behavioural economics approach in order to identify and measure the most important socio-economic conditions and potential policy interventions driving businesses’ and consumers’ choices in the generation of food waste. More specifically, this work aims to provide new information on consumer and business behaviour by measuring the effects of major tangible factors of food waste, by identifying hidden and emerging profiles of consumer’ and business’ behaviours affecting food waste, and by allowing the detection of intangible food waste drivers. Such an objective is achieved through the development and the testing of Agent-Based Models (ABMs) and Bayesian networks (BNs)

Machine Learning Approaches to Human Body Shape Analysis

Soft biometrics, biomedical sciences, and many other fields of study pay particular attention to the study of the geometric description of the human body, and its variations. Although multiple contributions, the interest is particularly high given the non-rigid nature of the human body, capable of assuming different poses, and numerous shapes due to variable body composition. Unfortunately, a well-known costly requirement in data-driven machine learning, and particularly in the human-based analysis, is the availability of data, in the form of geometric information (body measurements) with related vision information (natural images, 3D mesh, etc.). We introduce a computer graphics framework able to generate thousands of synthetic human body meshes, representing a population of individuals with stratified information: gender, Body Fat Percentage (BFP), anthropometric measurements, and pose. This contribution permits an extensive analysis of different bodies in different poses, avoiding the demanding, and expensive acquisition process. We design a virtual environment able to take advantage of the generated bodies, to infer the body surface area (BSA) from a single view. The framework permits to simulate the acquisition process of newly introduced RGB-D devices disentangling different noise components (sensor noise, optical distortion, body part occlusions). Common geometric descriptors in soft biometric, as well as in biomedical sciences, are based on body measurements. Unfortunately, as we prove, these descriptors are not pose invariant, constraining the usability in controlled scenarios. We introduce a differential geometry approach assuming body pose variations as isometric transformations of the body surface, and body composition changes covariant to the body surface area. This setting permits the use of the Laplace-Beltrami operator on the 2D body manifold, describing the body with a compact, efficient, and pose invariant representation. We design a neural network architecture able to infer important body semantics from spectral descriptors, closing the gap between abstract spectral features, and traditional measurement-based indices. Studying the manifold of body shapes, we propose an innovative generative adversarial model able to learn the body shapes. The method permits to generate new bodies with unseen geometries as a walk on the latent space, constituting a significant advantage over traditional generative methods