The modularity of aesthetic processing and perception in the human brain. Functional neuroimaging studies of neuroaesthetics.

By taking advantage of the advent of functional Magnetic Resonance Imaging (fMRI) this thesis argues that aesthetics belongs in the domain of neurobiology by investigating the different brain processes that are implicated in aesthetic perception from two perspectives. The first experiment explores a specific artistic style that has stressed the problem in the relationship between objects and context. This study investigates the neural responses associated with changes in visual perception, as when objects are placed in their normal context versus when the object-context relationship is violated. Indeed, an aim of this study was to cast a new light on this specific artistic style from a neuroscientific perspective. In contrast to basic rewards, which relate to the reproduction of the species, the evolution of abstract, cognitive representations facilitates the use of a different class of rewards related to hedonics. The second part investigates the hedonic processes involved in aesthetic judgments in order to explore if such higher order cognitive rewards use the same neural reward mechanism as basic rewards. In the first of these experiments we modulate the extent to which the neural correlates of aesthetic preference vary as a function of expertise in architecture. In the second experiment we aim to measure the more general effects of labelling works of art with cognitive semantic information in order to explore the neural modulation of aesthetic preference relative to this information. The main finding of this thesis is that stimulus affective value is represented separately in OFC, with positive reward (increasing aesthetic judgments) being represented in medial OFC and negative reward value is being represented in lateral OFC. Furthermore ventral striatum encode reward expectancy and the predictive value of a stimulus. These findings suggest a dissociation of reward processing with separate neural substrates in reward expectancy and stimulus affective value

Combining Deep Facial and Ambient Features for First Impression Estimation

14th European Conference on Computer Vision (ECCV) -- OCT 08-16, 2016 -- Amsterdam, NETHERLANDSFirst impressions influence the behavior of people towards a newly encountered person or a human-like agent. Apart from the physical characteristics of the encountered face, the emotional expressions displayed on it, as well as ambient information affect these impressions. In this work, we propose an approach to predict the first impressions people will have for a given video depicting a face within a context. We employ pre-trained Deep Convolutional Neural Networks to extract facial expressions, as well as ambient information. After video modeling, visual features that represent facial expression and scene are combined and fed to a Kernel Extreme Learning Machine regressor. The proposed system is evaluated on the ChaLearn Challenge Dataset on First Impression Recognition, where the classification target is the Big Five personality trait labels for each video. Our system achieved an accuracy of 90.94% on the sequestered test set, 0.36% points below the top system in the competition

Understanding Demographics and Experience of Tourists in Yellowstone National Park through Social Media

This study compared tourists’ demographic variables between survey data and Twitter data in Yellowstone National Park and explored tourists’ experience through Twitter data. First, there were significant differences in age groups of tourists between social media data and survey data. Compared to survey data, tourists who identified by Twitter data concentrated on middle age groups. Secondly, the spatial distribution of geotagged tweets reflected the road network and main attractions in Yellowstone National Park. The peak visitation season is from June to September in survey data, while, in social media data, the peak visitation season is slightly shorter. Finally, the sentiment analysis was conducted and only 6.7% of tweets were negative, indicating that most tourists in Yellowstone National Park had good experience. Therefore, analyzing Twitter data will be helpful for understanding tourists’ demographics, attitudes and experience in the national parks and improving customer service in the further