Advances in speech recognition, language processing and natural interaction have led to an increased industrial and academic interest. While the robustness and usability of such systems are steadily increasing, speech-based systems are still susceptible to recognition errors. This makes intelligent error handling of utmost importance for the success of those systems. In this work, we integrated anticipatory error handling for a voice-controlled video game where the game would perform a locally optimized action in respect to goal completion and obstacle avoidance, when a command is not recognized. We evaluated the user experience of our approach versus traditional, repetition-based error handling (N = 34). Our results indicate that implementing anticipatory error handling can improve the usability of a system, if it follows the intention of the user. Otherwise, it impairs the user experience, even when deciding for technically optimal decisions.
Competitive first-person shooter games are played over a network, where latency can degrade player performance. To better understand latency's impact, a promising approach is to study how latency affects individual game actions, such as moving and shooting. While target selection (aiming and shooting at an opponent) is fairly well studied, navigation (moving an avatar into position) is not. This paper presents results from a 30-person user study that evaluates the impact of latency on first-person navigation using a custom ``hide and seek'' game that isolates avatar movement in a manner intended to be similar to movement in a first-person shooter game. Analysis of the results shows latency has pronounced effects on player performance (score and seek positioning), with subjective opinions on Quality of Experience following suit.
Learnability is a core aspect of software usability. Video games are not an exception, as game designers need to teach players how to play their creations. We analyzed 40 contemporary video games to identify how video games approach learning experiences. We found that games have advanced far beyond using simple tutorials or demonstration screens and adopt a range of repeatable and reusable design strategies using visual cues to facilitate learning. We provide a detailed descriptive framework of these design strategies, elucidating how and when they can be used, and describing how the visual cues are used to build them. Our research can be useful for both general HCI researchers and practitioners seeking to tap into the rich ideas from video game learnability design looking for practical solutions for their work.
Games have become a popular way of collecting human subject data, based on the premise that they are more engaging than surveys or experiments, but generate equally valid data. However, this premise has not been empirically tested. In response, we designed a game for eliciting linguistic data following Intrinsic Elicitation – a design approach aiming to minimise validity threats in data collection games – and compared it to an equivalent linguistics experiment as control. In a preregistered study and replication (n=96 and n=136), using two different ways of operationalising accuracy, the game generated substantially more enjoyment (d=.70, .73) and substantially less accurate data (d=-.68, -.40) – though still more accurate than random responding. We conclude that for certain data types data collection games may present a serious trade-off between participant enjoyment and data quality, identify possible causes of lower data quality for future research, reflect on our design approach, and urge games HCI researchers to use careful controls where appropriate.
Random Reward Mechanisms (RRMs) in video games are systems in which rewards are issued probabilistically upon certain trigger conditions, such as completing gameplay tasks, exceeding a playtime quota, or making in-game purchases. We investigated the relationship between RRM implementations and user experience. Video analysis of 35 RRM systems allowed for the creation of a classification system based on contrasting observed dimensions. Interviews with 14 video game players provided insights into how factors such as the affordances of non-optimal rewards and the trade-off between random luck and skill impact player perception and interaction with RRMs. We additionally investigated the relationship between auditory, visual, and gameplay design decisions and player expectations for RRM reward presentations, finding that the resources required to obtain the reward and the relative value of the reward impact its expected presentation. Finally, we applied our findings to propose design methodologies for creating engaging and significant RRM systems.