Top-down effects on early visual processing in humans: a predictive coding framework

An increasing number of human electroencephalography (EEG) studies examining the earliest component of the visual evoked potential, the so-called C1, have cast doubts on the previously prevalent notion that this component is impermeable to top-down effects. This article reviews the original studies that (i) described the C1, (ii) linked it to primary visual cortex (V1) activity, and (iii) suggested that its electrophysiological characteristics are exclusively determined by low-level stimulus attributes, particularly the spatial position of the stimulus within the visual field. We then describe conflicting evidence from animal studies and human neuroimaging experiments and provide an overview of recent EEG and magnetoencephalography (MEG) work showing that initial V1 activity in humans may be strongly modulated by higher-level cognitive factors. Finally, we formulate a theoretical framework for understanding top-down effects on early visual processing in terms of predictive coding

Who is the director of this movie? Automatic style recognition based on shot features

We show how low-level formal features, such as shot duration, meant as length of camera takes, and shot scale, i.e. the distance between the camera and the subject, are distinctive of a director's style in art movies. So far such features were thought of not having enough varieties to become distinctive of an author. However our investigation on the full filmographies of six different authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total number of 120 movies analysed second by second, confirms that these shot-related features do not appear as random patterns in movies from the same director. For feature extraction we adopt methods based on both conventional and deep learning techniques. Our findings suggest that feature sequential patterns, i.e. how features evolve in time, are at least as important as the related feature distributions. To the best of our knowledge this is the first study dealing with automatic attribution of movie authorship, which opens up interesting lines of cross-disciplinary research on the impact of style on the aesthetic and emotional effects on the viewers

Republican Monsters: The Cultural Construction of American Positivist Criminology, 1767-1920

This dissertation examines the history of and cultural influences on positivist criminology in the United States. From Benjamin Rush to the present day, the U.S. has produced an extensive corpus of empirical and theoretical studies that seeks to discern an objective, scientifically-grounded basis for criminal behavior. American positivist criminology has drawn on numerous subfields and theories, including rational choice / economic theory, biology, and psychology, but in all cases, maintains that a purely scientific explanation of offending is possible. This study proceeds from the perspective that divisions between scientific and non-scientific thought are untenable. Drawing on scholarship in literary criticism and sociology, I argue that positivist criminology confronts an inherent contradiction in purporting to develop a purely scientific account of phenomena that are defined by the moral and cultural sentiments of a society. I thus hypothesize that positivist criminology is in fact reliant on the irrational and fictive cultural tropes and images of crime that it claims to exorcize. The dissertation proceeds by reviewing the literature on the history of criminology, developing a set of functional types or tropes for character analysis, and then examining four separate periods in the development of scientific criminology: eighteenth century studies of rational action, nineteenth century studies of defective reasoning, early twentieth century studies of race and crime, and the development of scientifically informed criminalistics programs. Each of these cases captures a different period and focus in the development of scientific criminology. In threading continuity between these cases, I show how criminological positivism is consistently reliant on culturally informed tropes and characters to render itself sensible and coherent

An investigation of the possibility of differential effects of color upon human emotions

"Color affects our emotional attitudes and our behavior even when we are not aware of it."1 This statement from the pamphlet, Color Planning for School Interiors, exemplifies the viewpoint of the popular books and articles which have been written about the uses of color in such areas as advertising, merchandising, education, hospital administration, and home decoration. In the popular literature, certain emotional values are consistently attributed to each color: orange and red are supposed to be stimulating and exciting; yellow is warm, vibrant, and cheerful; blue is subduing, depressing, and soothing; green, neither stimulating nor sedative, is tranquil and peaceful; violet and purple are cold, exotic, subduing, and depressing; blue-green is cool and soothing; and magenta is exotic and pleasing. Red, yellow, and orange are supposed to make one energetic and active; blue and green to make one meditative and listless. 2,3,

From forensic psychophisiology to forensic neurophysiology. New trends in examinations in the detection of deception

From introduction: "Developed and perfected for years, polygraph examination techniques have probably reached the limits of their capabilities. Their diagnostic value is comparable to that of other techniques routinely used in investigations (Widacki 1977, Widacki & Horvath 1978). Neither new examination techniques nor new kinds of tests are likely substantially to affect this conclusion. Granted, whereas whether it is possible to improve the diagnostic value by another 1 % and increase the number of conclusive results may be of significance for practice, this remains more an issue of perfecting practice rather than a scientific problem."(...

Palmprint Gender Classification Using Deep Learning Methods

Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and data augmentation were performed, various convolutional and deep learning-based classification approaches were empirically designed, optimized, and tested. Results of gender classification as high as 94.87% were achieved on the PolyU palmprint database and 90.70% accuracy on the CASIA palmprint database. Optimal performance was achieved by combining two different pre-trained and fine-tuned deep CNNs (VGGNet and DenseNet) through score level average fusion. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was also implemented to ascertain which specific regions of the palmprint are most discriminative for gender classification