Optimizing Player and Viewer Amusement in Suspense Video Games

Broadcast video games need to provide amusement to both players and audience. To achieve this, one of the most consumed genres is suspense, due to the psychological effects it has on both roles. Suspense is typically achieved in video games by controlling the amount of delivered information about the location of the threat. However, previous research suggests that players need more frequent information to reach similar amusement than viewers, even at the cost of jeopardizing viewers' engagement. In order to obtain models that maximize amusement for both interactive and passive audiences, we conducted an experiment in which a group of subjects played a suspenseful video game while another group watched it remotely. The subjects were asked to report their perceived suspense and amusement, and the data were used to obtain regression models for two common strategies to evoke suspense in video games: by alerting when the threat is approaching and by random circumstantial indications about the location of the threat. The results suggest that the optimal level is reached through randomly providing the minimal amount of information that still allows players to counteract the threat.We reckon that these results can be applied to a broad narrative media, beyond interactive games

Neighborhood Care and Neighborhood Bonds:An Unequal Relationship

Research in environmental psychology has found a positive relationship between place bonds and behaviors related to care and maintenance of place. Although this relationship has been analyzed in natural environments, it has been less frequently studied in urban environments and has yielded contradictory results. The aim of this study is to analyze behavior related to care and conservation of neighborhood and its possible relationship to place bonds, as well as to other variables that we think may be important in explaining this behavior. The participants were 407 residents from eight different neighborhoods with different sociodemographic characteristics in one Spanish city. The results indicate that the relationship between attachment and behavior is significant only in residents with higher socioeconomic levels. These findings may help to explain the contradictory results found in the literature. Other variables which are significant in explaining neighborhood care are social norms, residential satisfaction, and support for protection policies. Place identity was not found to be significantly correlated with neighborhood care

Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

The perception of suspense in stories is affected not only by general literary aspects like narrative structure and linguistic features, but also by anticipation and evocation of feelings like aversion, disgust or empathy. As such, it is possible to alter the feeling of suspense by modifying components of a story that convey these feelings to the audience. Based on a previous straightforward model of suspense adaptation, this paper describes the design, implementation and evaluation of a computational system that adapts narrative scenes for conveying a specific user-defined amount of suspense. The system is designed to address the impact of different types of emotional components on the reader. The relative weighted suspense of these components is computed with a regression model based on a sentiment analysis tool, and used as a fitness function in an evolutionary algorithm. This new function is able to identify the different weights on the prediction of suspense in aspects like outcome, decorative elements, or threat's appearance. The results indicate that this approach represents a significant improvement over the previous existing approach

Diseño y Elaboración de Recursos Docentes para el Desarrollo de Competencias y Habilidades Transversales en Administración de Empresas

Memoria ID13-0066. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2012-2013

Predicting the effects of suspenseful outcome for automatic storytelling

Automatic story generation systems usually deliver suspense by including an adverse outcome in the narrative, in the assumption that the adversity will trigger a certain set of emotions that can be categorized as suspenseful. However, existing systems do not implement solutions relying on predictive models of the impact of the outcome on readers. A formulation of the emotional effects of the outcome would allow storytelling systems to perform a better measure of suspense and discriminate among potential outcomes based on the emotional impact. This paper reports on a computational model of the effect of different outcomes on the perceived suspense. A preliminary analysis to identify and evaluate the affective responses to a set of outcomes commonly used in suspense was carried out. Then, a study was run to quantify and compare suspense and affective responses evoked by the set of outcomes. Next, a predictive model relying on the analyzed data was computed, and an evolutionary algorithm for automatically choosing the best outcome was implemented. The system was tested against human subjects' reported suspense and electromyography responses to the addition of the generated outcomes to narrative passages. The results show a high correlation between the predicted impact of the computed outcome and the reported suspense

Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

The miniaturization and price reduction of sensors have encouraged the proliferation of smart environments, in which multitudinous sensors detect and describe the activities carried out by inhabitants. In this context, the recognition of activities of daily living has represented one of the most developed research areas in recent years. Its objective is to determine what daily activity is developed by the inhabitants of a smart environment. In this field, many proposals have been presented in the literature, many of them being based on ad hoc ontologies to formalize logical rules, which hinders their reuse in other contexts. In this work, we propose the use of class expression learning (CEL), an ontology-based data mining technique, for the recognition of ADL. This technique is based on combining the entities in the ontology, trying to find the expressions that best describe those activities. As far as we know, it is the first time that this technique is applied to this problem. To evaluate the performance of CEL for the automatic recognition of activities, we have first developed a framework that is able to convert many of the available datasets to all the ontology models we have found in the literature for dealing with ADL. Two different CEL algorithms have been employed for the recognition of eighteen activities in two different datasets. Although all the available ontologies in the literature are focused on the description of the context of the activities, the results show that the sequence of the events produced by the sensors is more relevant for their automatic recognition, in general terms

Competencias y habilidades transversales en Administración de Empresas. Propósitos y realidades

Memoria ID-056. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2013-2014

El diagnóstico dinámico en sistemas de producciòn pecuaria

Ganado de leche-Ganadería lech

Evaluación de las aptitudes y actitudes de los alumnos como futuros empleados de la organización

Memoria ID-0064. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015

Elaboración de rúbricas para la evaluación de prácticas en las asignaturas de Organización de Empresas impartidas en el Grado en Relaciones Laborales y Recursos Humanos

Memoria ID-0259. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015