Toxic behavior in Multiplayer Online Battle Arena (MOBA) games has become a major issue. While previous studies have examined factors influencing toxic behavior, few have captured the cognitive and emotional states of the aggressors at the point of emergence of toxic behavior, or traced its evolution across an entire match. To fill the gap, we conducted replay-based semi-structured interviews with 18 players who recently initiated toxic behavior during matches. With adapted retrospective think-aloud protocols and players’ emotional journey maps, we collected their subjective perceptions and dynamic changes of emotion. Through thematic analysis, we identified a multi-dimensional criterion for evaluating toxicity severity and a three-layer cognition–emotion association structure, and described recurring persistent and single-instance patterns of toxic behavior observed in our matches. Based on our findings, we contribute to understanding the internal evolution of player toxicity and discuss implications for preventive intervention strategies and designs aiming at mitigating toxic behavior.

As social service robots become commonplace, it is essential for them to effectively interpret human signals, such as verbal, gesture, and eye gaze, when people need to focus on their primary tasks to minimize interruptions and distractions. Toward such a socially acceptable Human-Robot Interaction, we conducted a study ($N=24$) in an AR-simulated context of a coffee chat. Participants elicited social cues to signal intentions to an anthropomorphic, zoomorphic, grounded technical, or aerial technical robot waiter when they were speakers or listeners. Our findings reveal common patterns of social cues over intentions, the effects of robot morphology on social cue position and conversational role on social cue complexity, and users' rationale in choosing social cues. We offer insights into understanding social cues concerning perceptions of robots, cognitive load, and social context. Additionally, we discuss design considerations on approaching, social cue recognition, and response strategies for future service robots.

Preference-based learning aims to align robot task objectives with human values. One of the most common methods to infer human preferences is by pairwise comparisons of robot task trajectories. Traditional comparison-based preference labeling systems seldom support labelers to digest and identify critical differences between complex trajectories recorded in videos. Our formative study (N = 12) suggests that individuals may overlook non-salient task features and establish biased preference criteria during their preference elicitation process because of partial observations. In addition, they may experience mental fatigue when given many pairs to compare, causing their label quality to deteriorate. To mitigate these issues, we propose FARPLS, a Feature-Augmented Robot trajectory Preference Labeling System. FARPLS highlights potential outliers in a wide variety of task features that matter to humans and extracts the corresponding video keyframes for easy review and comparison. It also dynamically adjusts the labeling order according to users’ familiarities, difficulties of the trajectory pair, and level of disagreements. At the same time, the system monitors labelers’ consistency and provides feedback on labeling progress to keep labelers engaged. A between-subjects study (N = 42, 105 pairs of robot pick-and-place trajectories per person) shows that FARPLS can help users establish preference criteria more easily and notice more relevant details in the presented trajectories than the conventional interface. FARPLS also improves labeling consistency and engagement, mitigating challenges in preference elicitation without raising cognitive loads significantly.

Online critique communities (OCCs) provide a convenient space for creators to solicit feedback on their artifacts and improve skills. Creators’ behavioral, emotional, and cognitive engagement with comments on their works contribute to their skill development. However, what kinds of critique creators feel engaging may change with the creation stage of their shared artifacts. In this paper, we first model three dimensions of engagement expressed in creators’ replies to peer comments. Then we quantitatively examine how their engagement is affected by artifacts’ stage and feedback characteristics via regression analysis. Results show that creators sharing works-in-progress tend to exhibit lower behavioral and emotional engagement, but higher cognitive engagement than those sharing complete works. The increase in the valence of the feedback is associated with a stronger increase in behavior engagement for seekers sharing complete works than works-in-progress. Finally, we discuss how our insights could benefit OCCs and other online help-seeking platforms.