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  1. Pattern Recognition and Machine Learning.Christopher M. Bishop - 2006 - Springer: New York.
    This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would (...)
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  • Neuroscientific Evidence for Simulation and Shared Substrates in Emotion Recognition: Beyond Faces.Andrea S. Heberlein & Anthony P. Atkinson - 2009 - Emotion Review 1 (2):162-177.
    According to simulation or shared-substrates models of emotion recognition, our ability to recognize the emotions expressed by other individuals relies, at least in part, on processes that internally simulate the same emotional state in ourselves. The term “emotional expressions” is nearly synonymous, in many people's minds, with facial expressions of emotion. However, vocal prosody and whole-body cues also convey emotional information. What is the relationship between these various channels of emotional communication? We first briefly review simulation models of emotion recognition, (...)
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  • Representational geometry: integrating cognition, computation, and the brain.Nikolaus Kriegeskorte & Rogier A. Kievit - 2013 - Trends in Cognitive Sciences 17 (8):401-412.
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  • Beyond mind-reading: multi-voxel pattern analysis of fMRI data.Kenneth A. Norman, Sean M. Polyn, Greg J. Detre & James V. Haxby - 2006 - Trends in Cognitive Sciences 10 (9):424-430.
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  • Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.Yin Liang, Baolin Liu, Xianglin Li & Peiyuan Wang - 2018 - Frontiers in Human Neuroscience 12.
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  • Implicit and Explicit Attention to Pictures and Words: An fMRI-Study of Concurrent Emotional Stimulus Processing.Tobias Flaisch, Martin Imhof, Ralf Schmälzle, Klaus-Ulrich Wentz, Bernd Ibach & Harald T. Schupp - 2015 - Frontiers in Psychology 6.
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  • Linear Representation of Emotions in Whole Persons by Combining Facial and Bodily Expressions in the Extrastriate Body Area.Xiaoli Yang, Junhai Xu, Linjing Cao, Xianglin Li, Peiyuan Wang, Bin Wang & Baolin Liu - 2018 - Frontiers in Human Neuroscience 11.
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  • Brightness differences influence the evaluation of affective pictures.Daniël Lakens, Daniel A. Fockenberg, Karin P. H. Lemmens, Jaap Ham & Cees J. H. Midden - 2013 - Cognition and Emotion 27 (7):1225-1246.
    We explored the possibility of a general brightness bias: brighter pictures are evaluated more positively, while darker pictures are evaluated more negatively. In Study 1 we found that positive pictures are brighter than negative pictures in two affective picture databases (the IAPS and the GAPED). Study 2 revealed that because researchers select affective pictures on the extremity of their affective rating without controlling for brightness differences, pictures used in positive conditions of experiments were on average brighter than those used in (...)
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