Rating vs. preference : a comparative study of self-reporting

This paper introduces a comparative analysis between rating and pairwise self-reporting via questionnaires in user survey experiments. Two dissimilar game user survey experiments are employed in which the two questionnaire schemes are tested and compared for reliable affect annotation. The statistical analysis followed to test our hypotheses shows that even though the two self-reporting schemes are consistent there are significant order of reporting effects when subjects report via a rating questionnaire. The paper concludes with a discussion of the appropriateness of each self-reporting scheme under conditions drawn from the experimental results obtained.peer-reviewe

Game adaptivity impact on affective physical interaction

Adaptive human computer interaction is necessary for successfully closing the affective loop within intelligent interactive systems. This paper investigates the impact of adaptivity on the physiological state and the expressed emotional preferences of users. A physical interactive game is used as a test-bed system and its real-time adaptation mechanism is evaluated using a survey experiment. Results reveal that entertainment preferences expressed are consistent with the affective model constructed and that adaptation generates dissimilar physiological responses with respect to preferences.peer-reviewe

Preference learning for affective modeling

There is an increasing trend towards personalization of services and interaction. The use of computational models for learning to predict user emotional preferences is of significant importance towards system personalization. Preference learning is a machine learning research area that aids in the process of exploiting a set of specific features of an individual in an attempt to predict her preferences. This paper outlines the use of preference learning for modeling emotional preferences and shows the methodology's promise for constructing accurate computational models of affect.peer-reviewe

Entertainment modeling in physical play through physiology beyond heart-rate

An investigation into capturing the relation of physiology, beyond heart rate recording, to expressed preferences of entertainment in children’s physical gameplay is presented in this paper. An exploratory survey experiment raises the difficulties of isolating elements derived (solely) from heart rate recordings attributed to reported entertainment and a control experiment for surmounting those difficulties is proposed. Then a survey experiment on a larger scale is devised where more physiological signals (Blood Volume Pulse and Skin Conductance) are collected and analyzed. Given effective data collection a set of numerical features is extracted from the child’s physiological state. A preference learning mechanism based on neuro-evolution is used to construct a function of single physiological features that models the players’ notion of ‘fun’ for the games under investigation. Performance of the model is evaluated by the degree to which the preferences predicted by the model match those expressed by the children. Results indicate that there appears to be increased mental/emotional effort in preferred games of children.peer-reviewe

Rating vs. preference : a comparative study of self-reporting

This paper introduces a comparative analysis between rating and pairwise self-reporting via questionnaires in user survey experiments. Two dissimilar game user survey experiments are employed in which the two questionnaire schemes are tested and compared for reliable affect annotation. The statistical analysis followed to test our hypotheses shows that even though the two self-reporting schemes are consistent there are significant order of reporting effects when subjects report via a rating questionnaire. The paper concludes with a discussion of the appropriateness of each self-reporting scheme under conditions drawn from the experimental results obtained.peer-reviewe

Experience-driven procedural content generation (extended abstract)

Procedural content generation is an increasingly important area of technology within modern human-computer interaction with direct applications in digital games, the semantic web, and interface, media and software design. The personalization of experience via the modeling of the user, coupled with the appropriate adjustment of the content according to user needs and preferences are important steps towards effective and meaningful content generation. This paper introduces a framework for procedural content generation driven by computational models of user experience we name Experience-Driven Procedural Content Generation. While the framework is generic and applicable to various subareas of human computer interaction, we employ games as an indicative example of content-intensive software that enables rich forms of interaction.The research was supported, in part, by the FP7 ICT projects C2Learn (318480) and iLearnRW (318803).peer-reviewe

Pairwise Ranking Network for Affect Recognition

In this work we study the problem of emotion recognition under the prism of preference learning. Affective datasets are typically annotated by assigning a single absolute label, i.e. a numerical value that describes the intensity of an emotional attribute, to each sample. Then, the majority of existing works on affect recognition employ sample-wise classification/regression methods to predict affective states, using those annotations. We take a different approach and use a deep network architecture that performs joint training on the tasks of classification/regression of samples and ordinal ranking between pairs of samples. By treating input samples in a pairwise manner, we leverage the auxiliary task of inferring the ordinal relation between their corresponding affective states. Incorporating the ranking objective allows capturing the inherently ordinal structure of emotions and learning the inter-sample relations, resulting in better generalization. Our method is incorporated into existing affect recognition architectures and evaluated on datasets of electroencephalograms (EEG) and images. We show that the approach proposed in this work leads to consistent performance gains when incorporated in classification/regression networks

Towards general models of player affect

While the primary focus of affective computing has been on constructing efficient and reliable models of affect, the vast majority of such models are limited to a specific task and domain. This paper, instead, investigates how computational models of affect can be general across dissimilar tasks; in particular, in modeling the experience of playing very different video games. We use three dissimilar games whose players annotated their arousal levels on video recordings of their own playthroughs. We construct models mapping ranks of arousal to skin conductance and gameplay logs via preference learning and we use a form of cross-game validation to test the generality of the obtained models on unseen games. Our initial results comparing between absolute and relative measures of the arousal annotation values indicate that we can obtain more general models of player affect if we process the model output in an ordinal fashion.peer-reviewe

Generic physiological features as predictors of player experience

This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and self-reported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.peer-reviewe

Grounding truth via ordinal annotation

The question of how to best annotate affect within available content has been a milestone challenge for affective computing. Appropriate methods and tools addressing that question can provide better estimations of the ground truth which, in turn, may lead to more efficient affect detection and more reliable models of affect. This paper introduces a rank-based real-time annotation tool, we name AffectRank, and compares it against the popular rating-based real-time FeelTrace tool through a proofof- concept video annotation experiment. Results obtained suggest that the rank-based (ordinal) annotation approach proposed yields significantly higher inter-rater reliability and, thereby, approximation of the underlying ground truth. The key findings of the paper demonstrate that the current dominant practice in continuous affect annotation via rating-based labeling is detrimental to advancements in the field of affective computing.The authors would like to thank all annotators that participated in the reported experiments. We would also like to thank Gary Hili and Ryan Abela for providing access to the Eryi dataset. The work is supported, in part, by the EU-funded FP7 ICT iLearnRW project (project no: 318803).peer-reviewe