Don't sit so close to me: Unconsciously elicited affect automatically provokes social avoidance

Behavior may be automatically prompted by cues in our social environment. Previous research has focused on cognitive explanations for such effects. Here we hypothesize that affective processes are susceptible to similar automatic influences. We propose that exposure to groups stereotyped as dangerous or violent may provoke an anxiety response and, thus, a tendency to move away. In the present experiment, we subliminally exposed participants to images of such a group, and found that they displayed greater avoidance in a subsequent interaction. Critically, this effect was explained by their increased sensitivity to threat-related information. These findings demonstrate an affective mechanism responsible for nonconscious priming effects on interpersonal behavior

Priming in interpersonal contexts: Implications for affect and behavior

Priming stereotypes can lead to a variety of behavioral outcomes, including assimilation, contrast, and response behaviors. However, the conditions that give rise to each of these outcomes are unspecified. Furthermore, theoretical accounts posit that prime-to-behavior effects are either direct (i.e., unmediated) or mediated by cognitive processes, whereas the role of affective processes has been largely unexplored. The present research directly investigated both of these issues. Three experiments demonstrated that priming a threatening social group ("hoodies") influences both affect and behavior in an interpersonal context. Hoodie priming produced both behavioral avoidance and several affective changes (including social apprehension, threat sensitivity, and self-reported anxiety and hostility). Importantly, avoidance following hoodie priming was mediated by anxiety and occurred only under conditions of other-(but not self-) focus. These results highlight multiple routes through which primes influence affect and behavior, and suggest that attention to self or others determine the nature of priming effects

When not thinking leads to being and doing: Stereotype suppression and the self

Suppressing stereotypes often results in more stereotype use, an effect attributed to heightened stereotype activation. The authors report two experiments examining the consequences of suppression on two self-relevant outcomes: the active self-concept and overt behavior. Participants who suppressed stereotypes incorporated stereotypic traits into their self-concepts and demonstrated stereotype-congruent behavior compared to those who were exposed to the same stereotypes but did not suppress them. These findings address issues emerging from current theories of suppression, priming, and the active self

" Women! Your Country Needs You! " Fleeing Feminism or Gendering Citizenship in Great War Britain?

doi erroné : 10.3172/MIN.4.2.26International audienceWhen war broke out in August 1914, the National Union of Women's Suffrage Societies suspended its political work on behalf of women's suffrage and plunged into relief work for women and children. Because it appeared to conform to the reigning ideology of separate spheres, this response has been presented as conclusive evidence of British feminism's ideological collapse in the face of war. This article argues a contrario that the National Union's response is further evidence of feminism's ideological resilience in this period. Relief work, it shows, was one aspect of a broader project aimed at " gendering " the concept and language of citizenship in order to appropriate them for women. The result was an insistence on women's identity as " citizens, " an identity that in turn had important consequences for the kind of feminism that could be articulated in its name

Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging

In the present work sparse-based methods are applied to the analysis of hyperspectral images with the aim at studying their capability of being adequate methods for variable selection in a classification framework. The key aspect of sparse methods is the possibility of performing variable selection by forcing the model coefficients related to irrelevant variables to zero. In particular, two different sparse classification approaches, i.e. sPCA+kNN and sPLS-DA, were compared with the corresponding classical methods (PCA + kNN and PLS-DA) to classify Arabica and Robusta coffee species. Green coffee samples were analyzed using near infrared hyperspectral imaging and the average spectra from each hyperspectral image were used to build training and test sets; furthermore a test image was used to evaluate the performances of the considered methods at pixel-level. In our case, sparse methods led to similar results as classical methods, with the advantage of obtaining more interpretable and parsimonious models. An important result to highlight is that variable selection performed with two different sparse classification approaches converged to the selection of same spectral regions, which implies the chemical relevance of those regions in the discrimination of Arabica and Robusta coffee species

Colourgrams GUI: A graphical user-friendly interface for the analysis of large datasets of RGB images

Colourgrams GUI is a graphical user-friendly interface developed in order to facilitate the analysis of large datasets of RGB images through the colourgrams approach. Briefly, the colourgrams approach consists in converting a dataset of RGB images into a matrix of one-dimensional signals, the colourgrams, each one codifying the colour content of the corresponding original image. This matrix of signals can be in turn analysed by means of common multivariate statistical methods, such as Principal Component Analysis (PCA) for exploratory analysis of the image dataset, or Partial Least Squares (PLS) regression for the quantification of colour-related properties of interest. Colourgrams GUI allows to easily convert the dataset of RGB images into the colourgrams matrix, to interactively visualize the signals coloured according to qualitative and/or quantitative properties of the corresponding samples and to visualize the colour features corresponding to selected colourgram regions into the image domain. In addition, the software also allows to analyse the colourgrams matrix by means of PCA and PLS

Mixture design and multivariate image analysis to monitor the colour of strawberry yoghurt purée

Food colour is a commercial added value, since it represents the first appealing factor for consumers. In this context, this study was aimed at evaluating the effect of strawberry yoghurt purée (SYP) formulation on the corresponding colour and on its variation over time, which is mainly due to degradation and browning phenomena. To this aim, a combined approach was used that included mixture design and multivariate analysis of RGB images. Strawberry purée, sugar, lemon juice and two types of thickener were mixed in different proportions by I-optimal mixture design to obtain 44 SYP formulations. The samples were subjected to light and temperature stress conditions for five weeks; during this time the RGB images of the samples were acquired using a flatbed scanner, along with the images of the corresponding control samples. The dimensionality of the acquired images was reduced by two different approaches: i) the conversion of images into signals, namely colourgrams, which can be seen as the colour fingerprint of the imaged samples, and ii) the calculation of the median values of various colour-related parameters. The colourgrams dataset was then subjected to exploratory data analysis using Principal Component Analysis, while the median values of colour-related parameters were analysed using Response Surface Methodology and Partial Least Squares-Discriminant Analysis. The aim of data analysis was both to find the best colour parameters to describe colour variability over time, and to investigate the cause-effect relationship between mixture proportions and colour response. The results highlighted that, among the considered colour parameters, relative green (i.e., the ratio of green to lightness) and red could be used to monitor colour changes. Colour variation due to stress conditions was more pronounced for samples with a high percentage of strawberry purée, and the type of thickener also affected the colour degradation kinetics

Data Fusion Approach to Simultaneously Evaluate the Degradation Process Caused by Ozone and Humidity on Modern Paint Materials

The knowledge of the atmospheric degradation reactions affecting the stability of modern materials is still of current interest. In fact, environmental parameters, such as relative humidity (RH), temperature, and pollutant agents, often fluctuate due to natural or anthropogenic climatic changes. This study focuses on evaluating analytical and statistical strategies to investigate the degradation processes of acrylic and styrene-acrylic paints after exposure to ozone (O3) and RH. A first comparison of FTIR and Py-GC/MS results allowed to obtain qualitative information on the degradation products and the influence of the pigments on the paints’ stability. The combination of these results represents a significant potential for the use of data fusion methods. Specifically, the datasets obtained by FTIR and Py-GC/MS were combined using a low-level data fusion approach and subsequently processed by principal component analysis (PCA). It allowed to evaluate the different chemical impact of the variables for the characterization of unaged and aged samples, understanding which paint is more prone to ozone degradation, and which aging variables most compromise their stability. The advantage of this method consists in simultaneously evaluating all the FTIR and Py-GC/MS variables and describing common degradation patterns. From these combined results, specific information was obtained for further suitable conservation practices for modern and contemporary painted films