As the public use of virtual reality (VR) scales, understanding how users engage across various sources of VR content is critical. 360-video is popular due to its ease of both creation and access. There are, however, few studies of 360-videos, and they suffer from three limitations. First, most studies rely on small and homogeneous samples of participants. Second, they tend to examine only a single 360-video, or a handful of them in a few exceptional cases. Third, very few studies trace participants’ VR use over multiple experiences. The current study examined a large sample of participants (511) and a large set of 360-videos (80). Each participant experienced 5 of the videos, and we tracked head movement in addition to self-report data on presence, arousal, simulator sickness, and future use intention for each video. This design allowed us to answer novel questions relating to individual differences of participants and changes in experience over time, and in general to present results of VR use at a scale not seen before in the literature. Moreover, the results suggest that looking at patterns across stimuli provide unique insights which are missed when looking only within a single piece of content.

Digital experiences capture an increasingly large part of life, making them a preferred, if not required, method to describe and theorize about human behavior. Digital media also shape behavior by enabling people to switch between different content easily, and create unique threads of experiences that pass quickly through numerous information categories. Current methods of recording digital experiences provide only partial reconstructions of digital lives that weave - often within seconds - among multiple applications, locations, functions and media. We describe an end-to-end system for capturing and analyzing the "screenome" of life in media, i.e., the record of individual experiences represented as a sequence of screens that people view and interact with over time. The system includes software that collects screenshots, extracts text and images, and allows searching of a screenshot database. We discuss how the system can be used to elaborate current theories about psychological processing of technology, and suggest new theoretical questions that are enabled by multiple time scale analyses. Capabilities of the system are highlighted with eight research examples that analyze screens from adults who have generated data within the system. We end with a discussion of future uses, limitations, theory and privacy.

This paper examines relationships between perceptions of warmth and competence, emotional responses, and behavioral tendencies in the context of social robots. Participants answered questions about these three aspects of impression formation after viewing an image of one of 342 social robots in the Stanford Social Robots Database. Results suggest that people have similar emotional and behavioral reactions to robots as they have to humans; impressions of the robots' warmth and competence predicted specific emotional responses (admiration, envy, contempt, pity) and those emotional responses predicted distinct behavioral tendencies (active facilitation, active harm, passive facilitation, passive harm). However, the predicted relationships between impressions and harmful behavioral tendencies were absent. This novel asymmetry for perceptions and intentions towards robots is deliberated in the context of the computers as social actors framework and opportunities for further research are discussed.

Dominance is a key aspect of interpersonal relationships. To what extent do nonverbal indicators related to dominance status translate to a nonanthropomorphic robot? An experiment ( N = 25) addressed whether a mobile robot's motion style can influence people's perceptions of its status. Using concepts from improv theater literature, we developed two motion styles across three scenarios (robot makes lateral motions, approaches, and departs) to communicate a robot's dominance status through nonverbal expression. In agreement with the literature, participants described a motion style that was fast, in the foreground, and more animated as higher status than a motion style that was slow, in the periphery, and less animated. Participants used fewer negative emotion words to describe the robot with the purportedly high-status movements versus the purportedly low-status movements, but used more negative emotion words to describe the robot when it made departing motions that occurred in the same style. This result provides evidence that guidelines from improvisational theater for using nonverbal expression to perform interpersonal status can be applied to influence perception of a nonanthropomorphic robot's status, thus suggesting that useful models for more complicated behaviors might similarly be derived from performance literature and theory.

Life experiences are increasingly intertwined with digital devices, suggesting screens as a preferred, if not required, data source for behavioral studies and health interventions. Text Information Extraction from digital screenshots is then a key prerequisite to the overall accuracy of analyses regarding media behaviors. This unique image data set offers the opportunity i) to test existing Image Processing and Text Recognition methods, and ii) to identify and discuss the computational challenges specific to the considered case. Our aim is to assess whether and how state-of-the-art methodologies can be applied to this novel data set. We show how combining OpenCV-based pre-processing with a Long short-term memory (LSTM) based release of Tesseract OCR, without ad hoc training, ensured a 74% text accuracy at the character level. The implications and incidence of different error factors on the resulting quality of text are discussed, prompting the discussion of future research trajectories.

A large literature describes the use of robots' physical bodies to support communication with people. Touch is a natural channel for physical interaction, yet it is not understood how principles of interpersonal touch might carry over to a robot. Exploring how interpersonal rules surrounding body accessibility and touch apply to a robot is critical toward understanding the extent to which people treat the act of touching body regions as a sign of closeness---even if the body belongs to a robot---and is important to the field of humanoid social robotics. Thirty-one students participated in an interactive anatomy lesson with a small, humanoid robot. Participants either touched or pointed to an anatomical region of the robot in each of 26 trials while their skin conductance response was measured. Touching less accessible regions of a robot's body (e.g., its buttocks and genitals) was more physiologically arousing than touching more accessible regions (e.g., its hands and feet). No differences in physiological arousal were found when just pointing to those same anatomical regions. A social robot elicited tactile responses in human physiology, a result that signals people treat touching body parts as an act of closeness in itself that does not require a human recipient. The power of touching a humanoid body with identifiable body parts should caution mechanical and interaction designers about the positive and negative effects of human-robot interaction.

Animated characters are common in user interfaces, but important questions remain about whether characters work in all situations and for all users. This experiment tested the effects of different character presentations on user anxiety, task performance, and subjective evaluations of two commerce websites. There were three character conditions (no character, a character that ignored the user, and a character that closely monitored work on the website). Users were separated into two groups that had different attitudes about accepting help from others: people with control orientations that were external (users thought that other people controlled their success) and those with internal orientations (users thought they were in control). Results showed that the effects of monitoring and individual differences in thoughts about control worked as they do in real life. Users felt more anxious when characters monitored their website work and this effect was strongest for users with an external control orientation. Monitoring characters also decreased task performance, but increased trust in website content. Results are discussed in terms of design considerations that maximize the positive influence of animated agents.