Feature extraction and feature selection are crucial phases in the process of affective modeling. Both, however, incorporate substantial limitations that hinder the development of reliable and accurate models of affect. For the purpose of modeling affect manifested through physiology, this paper builds on recent advances in machine learning with deep learning (DL) approaches. The efficiency of DL algorithms that train artificial neural network models is tested and compared against standard feature extraction and selection approaches followed in the literature. Results on a game data corpus-containing players' physiological signals (i.e. skin conductance and blood volume pulse) and subjective self-reports of affect-reveal that DL outperforms manual ad-hoc feature extraction as it yields significantly more accurate affective models. Moreover, it appears that DL meets and even outperforms affective models that are boosted by automatic feature selection, for several of the scenarios examined. As the DL method is generic and applicable to any affective modeling task, the key findings of the paper suggest that ad-hoc feature extraction and selection-to a lesser degree-could be bypassed.

Information about interactive virtual environments, such as games, is perceived by users through a virtual camera. While most interactive applications let users control the camera, in complex navigation tasks within 3D environments users often get frustrated with the interaction. In this paper, we propose inclusion of camera control as a vital component of affective adaptive interaction in games. We investigate the impact of camera viewpoints on psychophysiology of players through preference surveys collected from a test game. Data is collected from players of a 3D prey/predator game in which player experience is directly linked to camera settings. Computational models of discrete affective states of fun, challenge, boredom, frustration, excitement, anxiety and relaxation are built on biosignal (heart rate, blood volume pulse and skin conductance) features to predict the pairwise selfreported emotional preferences of the players. For this purpose, automatic feature selection and neuro-evolutionary preference learning are combined providing highly accurate affective models. The performance of the artificial neural network models on unseen data reveals accuracies of above 80% for the majority of discrete affective states examined. The generality of the obtained models is tested in different test-bed game environments and the use of the generated models for creating adaptive affect-driven camera control in games is discussed.

How should affect be appropriately annotated and how should machine learning best be employed to map manifestations of affect to affect annotations? What is the use of ratings of affect for the study of affective computing and how should we treat them? These are the key questions this paper attempts to address by investigating the impact of dissimilar representations of annotated affect on the efficacy of affect modelling. In particular, we compare several different binary-class and pairwise preference representations for automatically learning from ratings of affect. The representations are compared and tested on three datasets: one synthetic dataset (testing "in vitro") and two affective datasets (testing "in vivo"). The synthetic dataset couples a number of attributes with generated rating values. The two affective datasets contain physiological and contextual user attributes, and speech attributes, respectively; these attributes are coupled with ratings of various affective and cognitive states. The main results of the paper suggest that ratings (when used) should be naturally transformed to ordinal (ranked) representations for obtaining more reliable and generalisable models of affect. The findings of this paper have a direct impact on affect annotation and modelling research but, most importantly, challenge the traditional state-of-practice in affective computing and psychometrics at large.

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Are ratings of any use in human-computer interaction and user studies at large? If ratings are of limited use, is there a better alternative for quantitative subjective assessment? Beyond the intrinsic shortcomings of human reporting, there are a number of supplementary limitations and fundamental methodological flaws associated with rating-based questionnaires -i.e., questionnaires that ask participants to rate their level of agreement with a given statement, such as a Likert item. While the effect of these pitfalls has been largely downplayed, recent findings from diverse areas of study question the reliability of using ratings. Rank-based questionnaires -i.e., questionnaires that ask participants to rank two or more options -appear as the evident alternative that not only eliminates the core limitations of ratings but also simplifies the use of sound methodologies that yield more reliable models of the underlying reported construct: user emotion, preference, or opinion. This paper solicits recent findings from various disciplines interlinked with psychometrics and offers a quick guide for the use, processing, and analysis of rankbased questionnaires for the unique advantages they offer. The paper challenges the traditional state-of-practice in human-computer interaction and psychometrics directly contributing toward a paradigm shift in subjective reporting.