It's all in the past:Temporal-context effects modulate subjective evaluations of emotional visual stimuli, regardless of presentation sequence

The aim of this study was to investigate if and how temporal context influences subjective affective responses to emotional images. To do so, we examined whether the subjective evaluation of a target image is influenced by the valence of its preceding image, and/or its overall position in a sequence of images. Furthermore, we assessed if these potentially confounding contextual effects can be moderated by a common procedural control: randomized stimulus presentation. Four groups of participants evaluated the same set of 120 pictures from the International Affective System (IAPS) presented in four different sequences. Our data reveal strong effects of both aspects of temporal context in all presentation sequences, modified only slightly in their nature and magnitude. Furthermore, this was true for both valence and arousal ratings. Subjective ratings of negative target images were influenced by temporal context most strongly across all sequences. We also observed important gender differences: females expressed greater sensitivity to temporal-context effects and design manipulations relative to males, especially for negative images. Our results have important implications for future emotion research that employs normative picture stimuli, and contributes to our understanding of context effects in general

Is On-Line Data Analysis Safety? Pitfalls Steaming from Automated Processing of Heterogeneous Environmental Data and Possible Solutions

Part 5: Information Tools for Global Environmental AssessmentInternational audienceThe current situation in environmental monitoring is characterized by increasing amount of data from monitoring networks together with increasing requirements on joining of these data from various sources in comprehensive databases and their usage for decision support in environmental protection and management. The automated analysis of such a heterogeneous datasets is a complicated process, rich in statistical pitfalls. There is a number of methods for multivariate classification of objects, e.g. logistic regression, discriminant analysis or neural networks; however, most of commonly used classification techniques have prerequisites about distribution of data, are computationally demanding or their model can be considered as “black box”. Keeping these facts in mind, we attempted to develop a robust multivariate method suitable for classification of unknown cases with minimum sensitivity to data distribution problems; and thus, suitable for routine use in practice