A neural marker for social bias toward in-group accents

Accents provide information about the speaker's geographical, socio-economic, and ethnic background. Research in applied psychology and sociolinguistics suggests that we generally prefer our own accent to other varieties of our native language and attribute more positive traits to it. Despite the widespread influence of accents on social interactions, educational and work settings the neural underpinnings of this social bias toward our own accent and, what may drive this bias, are unexplored. We measured brain activity while participants from two different geographical backgrounds listened passively to 3 English accent types embedded in an adaptation design. Cerebral activity in several regions, including bilateral amygdalae, revealed a significant interaction between the participants' own accent and the accent they listened to: while repetition of own accents elicited an enhanced neural response, repetition of the other group's accent resulted in reduced responses classically associated with adaptation. Our findings suggest that increased social relevance of, or greater emotional sensitivity to in-group accents, may underlie the own-accent bias. Our results provide a neural marker for the bias associated with accents, and show, for the first time, that the neural response to speech is partly shaped by the geographical background of the listener

In search of predictive endophenotypes in addiction: insights from preclinical research.

Drug addiction is widely recognized to afflict some but not all individuals by virtue of underlying risk markers and traits involving multifaceted interactions between polygenic and external factors. Remarkably, only a small proportion of individuals exposed to licit and illicit drugs develop compulsive drug-seeking behavior, maintained in the face of adverse consequences and associated detrimental patterns of drug intake involving extended and repeated bouts of binge intoxication, withdrawal and relapse. As a consequence, research has increasingly endeavored to identify distinctive neurobehavioral mechanisms and endophenotypes that predispose individuals to compulsive drug use. However, research in active drug users is hampered by the difficulty in categorizing putatively causal behavioral traits prior to the initiation of drug use. By contrast, research in experimental animals is often hindered by the validity of approaches used to investigate the neural and psychological mechanisms of compulsive drug-seeking habits in humans. Herein, we survey and discuss the principal findings emanating from preclinical animal research on addiction and highlight how specific behavioral endophenotypes of presumed genetic origin (e.g. trait anxiety, novelty preference and impulsivity) differentially contribute to compulsive forms of drug seeking and taking and, in particular, how these differentiate between different classes of stimulant and non-stimulant drugs of abuse.The authors acknowledge funding support from the UK Medical Research Council (grants G9536855; G0701500; G0802729), the Newton-Cambridge Trust and the Wellcome Trust (grant WT109738MA). The Behavioural and Clinical Neuroscience Institute at Cambridge University is supported by a core award from the Medical Research Council (G1000183) and Wellcome Trust (093875/Z/10/Z).This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1111/gbb.1226

Collaboration in neuroscience: the young PI perspective.

Wellcome Trust, University of Cambridge, CIG, Adelis FoundationThis is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1111/ejn.1322

Revisiting SIFT for plant foliage in RGB images acquired on a turntable

In this work, SIFT features are revisited for their use in two applications of computer vision for plant analysis. The first application is the reconstruction of 3D models of plants through tracking homologue points in successive intensity images. The second application is to provide a new global descriptor that gives a measure of the level of self-similariy of foliage for plants of different architectures and foliar appearance. In order to properly exploit SIFT descriptors in relation to these applications, we discuss two aspects of the classical SIFT keypoint matching practice. On the one hand we propose to match detected keypoints based on a scale criterion. On the other hand, we drop the ratio rule while matching keypoints in two images and propose the use of a spatial proximity filter instead

Modèle stochastique et représentation par graphe pour le suivi spatio-temporel de pathogènes à la surface de feuilles par imagerie

Modèle stochastique et représentation par graphe pour le suivi spatio-temporel de pathogènes à la surface de feuilles par imagerie

Progress towards omnidirectional transformation optics with lenses

We study, theoretically, omni-directional Euclidean transformation-optics (TO) devices comprising planar, light-ray-direction changing, imaging, interfaces. We initially studied such devices in the case when the interfaces are homogeneous, showing that very general transformations between physical and electromagnetic space are possible. We are now studying the case of inhomogeneous interfaces. This case is more complex to analyse, but the inhomogeneous interfaces include ideal thin lenses, which gives rise to the hope that it might be possible to construct practical omni-directional TO devices from lenses alone. Here we report on our progress in this direction

OPE statistics from higher-point crossing

We present new asymptotic formulas for the distribution of OPE coefficients in conformal field theories. These formulas involve products of four or more coefficients and include light-light-heavy as well as heavy-heavy-heavy contributions. They are derived from crossing symmetry of the six and higher point functions on the plane and should be interpreted as non-Gaussianities in the statistical distribution of the OPE coefficients. We begin with a formula for arbitrary operator exchanges (not necessarily primary) valid in any dimension. This is the first asymptotic formula constraining heavy-heavy-heavy OPE coefficients in d > 2. For two-dimensional CFTs, we present refined asymptotic formulas stemming from exchanges of quasi-primaries as well as Virasoro primaries.We present new asymptotic formulas for the distribution of OPE coefficients in conformal field theories. These formulas involve products of four or more coefficients and include light-light-heavy as well as heavy-heavy-heavy contributions. They are derived from crossing symmetry of the six and higher point functions on the plane and should be interpreted as non-Gaussianities in the statistical distribution of the OPE coefficients. We begin with a formula for arbitrary operator exchanges (not necessarily primary) valid in any dimension. This is the first asymptotic formula constraining heavy-heavy-heavy OPE coefficients in $d>2$. For two-dimensional CFTs, we present refined asymptotic formulas stemming from exchanges of quasi-primaries as well as Virasoro primaries

From impulses to maladaptive actions: the insula is a neurobiological gate for the development of compulsive behavior.

Impulsivity is an endophenotype of vulnerability for compulsive behaviors. However, the neural mechanisms whereby impulsivity facilitates the development of compulsive disorders, such as addiction or obsessive compulsive disorder, remain unknown. We first investigated, in rats, anatomical and functional correlates of impulsivity in the anterior insular (AI) cortex by measuring both the thickness of, and cellular plasticity markers in, the AI with magnetic resonance imaging and in situ hybridization of the immediate early gene zif268, respectively. We then investigated the influence of bilateral AI cortex lesions on the high impulsivity trait, as measured in the five-choice serial reaction time task (5-CSRTT), and the associated propensity to develop compulsivity as measured by high drinking levels in a schedule-induced polydipsia procedure (SIP). We demonstrate that the AI cortex causally contributes to individual vulnerability to impulsive-compulsive behavior in rats. Motor impulsivity, as measured by premature responses in the 5-CSRTT, was shown to correlate with the thinness of the anterior region of the insular cortex, in which highly impulsive (HI) rats expressed lower zif268 mRNA levels. Lesions of AI reduced impulsive behavior in HI rats, which were also highly susceptible to develop compulsive behavior as measured in a SIP procedure. AI lesions also attenuated both the development and the expression of SIP. This study thus identifies the AI as a novel neural substrate of maladaptive impulse control mechanisms that may facilitate the development of compulsive disorders.This research was carried-out within the Department of Psychology and the Department of Pharmacology of the University of Cambridge as well as the INSERM AVENIR team Psychobiology of Compulsive Disorders of the University of Poitiers. It was supported by an INSERM AVENIR grant and a FYSSEN foundation grant to DB. MLD was supported by a PhD fellowship from the Fondation pour la Recherche Médicale (FRM) and ABR was supported by a post-doctoral fellowship from the INSERM. BJE was supported by the United Kingdom Medical Research Council (MRC) Grant 9536855.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/mp.2015.14

Low-cost image annotation for supervised machine learning. Application to the detection of weeds in dense culture

An open problem in robotized agriculture is to detect weeds in dense culture. This problem can be addressed with computer vision and machine learning. The bottleneck of supervised approaches lay in the manual annotation of training images. We propose two different approaches for detecting weeds position to speed up this process. The first approach is using synthetic images and eye-tracking to annotated images [4] which is at least 30 times faster than manual annotation by an expert, the second approach is based on real RGB and depth images collected via Kinect v2 sensor. We generated a data set of 150 synthetic images which weeds were randomly positioned on it. Images were gazed by two observers. Eye tracker sampled eye position during the execution of this task [5, 6]. Area of interest was recorded as rectangular patches. A patch is considered as including weeds if the average fixation time in this patch exceeds 1.04 seconds. The quality of visual annotation by eye-tracking is assessed by two ways. First, direct comparison of visual annotation with ground-truth which is shown an average 94.7% of all fixations on an image which fell within ground-truth bounding-boxes. Second, as shown in fig.1 eye-tracked annotated data is used as a training data set in four machine learning approaches and compare the recognition rate with the ground-truth. These four machine learning methods are tested in order to assess the quality of the visual annotation. These methods correspond to handcrafted features adapted to texture characterization. They are followed by a linear support vector machine binary classifier. The table 1 gives the average accuracy and standard deviation. Experimental results prove that visual eye-tracked annotated data are almost the same as in-silico ground-truth and performances of supervised machine learning on eye-tracked annotated data are very close to the one obtained with ground-truth