Implicit gender bias has costly and complex consequences for women in the workplace, with many women reporting gender microaggressions which result in them being overlooked or disrespected. We present an online desktop virtual environment that follows the story of a male or female self-avatar from the first-person perspective, who either experiences a positive or negative workplace scenario. The negative scenario included many examples from the taxonomy of gender microaggressions. Participants who experienced negative workplace experiences with a female self-avatar had significantly decreased levels of implicit gender bias compared to those who had a male self-avatar. There was evidence of empathy and perspective taking in the negative condition for the female self-avatar. Experiences of a positive workplace scenario showed no significant decreases in implicit gender bias regardless of self-avatar gender. We discuss the implications of these findings and make recommendations for virtual environment technologies and scenarios with respect to the reduction of implicit biases.

Video accessibility is crucial for blind and visually impaired individuals for education, employment, and entertainment purposes. However, professional video descriptions are costly and time-consuming. Volunteer-created video descriptions could be a promising alternative, however, they can vary in quality and can be intimidating for novice describers. We developed a Human-in-the-Loop Machine Learning (HILML) approach to video description by automating video text generation and scene segmentation and allowing humans to edit the output. The HILML approach facilitates human-machine collaboration to produce high quality video descriptions while keeping a low barrier to entry for volunteer describers. Our HILML system was significantly faster and easier to use for first-time video describers compared to a human-only control condition with no machine learning assistance. The quality of the video descriptions and understanding of the topic created by the HILML system compared to the human-only condition were rated as being significantly higher by blind and visually impaired users.

Previous research has shown that when White people embody a black avatar in virtual reality (VR) with full body visuomotor synchrony, this can reduce their implicit racial bias. In this paper, we put men in female and male avatars in VR with full visuomotor synchrony using wearable trackers and investigated implicit gender bias and embodiment. We found that participants embodied in female avatars displayed significantly higher levels of implicit gender bias than those embodied in male avatars. The implicit gender bias actually increased after exposure to female embodiment in contrast to male embodiment. Results also showed that participants felt embodied in their avatars regardless of gender matching, demonstrating that wearable trackers can be used for a realistic sense of avatar embodiment in VR. We discuss the future implications of these findings for both VR scenarios and embodiment technologies.

The scatterplot matrix (SPLOM) is a commonly used technique for visualizing multiclass multivariate data. However, multiclass SPLOMs have issues with overdraw (overlapping points), and most existing techniques for alleviating overdraw focus on individual scatterplots with a single class. This paper explores whether animation using flickering points is an effective way to alleviate overdraw in these multiclass SPLOMs. In a user study with 69 participants, we found that users not only performed better at identifying dense regions using animated SPLOMs, but also found them easier to interpret and preferred them to static SPLOMs. These results open up new directions for future work on alleviating overdraw for multiclass SPLOMs, and provide insights for applying animation to alleviate overdraw in other settings.

Software-based agents are becoming increasingly ubiquitous and automated. However, current technology and algorithms are still fallible, which considerably affects users’ trust and interaction with such agents. In this article, we investigate two factors that can engender user trust in agents: reliability and attractiveness of agents. We show that agent reliability is not more important than agent attractiveness. Subjective user ratings of agent trust and perceived accuracy suggest that attractiveness may be even more important than reliability.

We present Brain Automated Chorales (BACh), an adaptive brain-computer system that dynamically increases the levels of difficulty in a musical learning task based on pianists' cognitive workload measured by functional near-infrared spectroscopy. As users' cognitive workload fell below a certain threshold, suggesting that they had mastered the material and could handle more cognitive information, BACh automatically increased the difficulty of the learning task. We found that learners played with significantly increased accuracy and speed in the brain-based adaptive task compared to our control condition. Participant feedback indicated that they felt they learned better with BACh and they liked the timings of the level changes. The underlying premise of BACh can be applied to learning situations where a task can be broken down into increasing levels of difficulty.

Emerging ultra-small wearables like smartwatches pose a design challenge for touch-based text entry. This is due to the "fat-finger problem," wherein users struggle to select elements much smaller than their fingers. To address this challenge, we developed DriftBoard, a panning-based text entry technique where the user types by positioning a movable qwerty keyboard on an interactive area with respect to a fixed cursor point. In this paper, we describe the design and implementation of DriftBoard and report results of a user study on a watch-size touchscreen. The study compared DriftBoard to two ultra-small keyboards, ZoomBoard (tapping-based) and Swipeboard (swiping-based). DriftBoard performed comparably (no significant difference) to ZoomBoard in the major metrics of text entry speed and error rate, and outperformed Swipeboard, which suggests that panning-based typing is a promising input method for text entry on ultra-small touchscreens.

The bubble cursor is a promising cursor expansion technique, improving a user's movement time and accuracy in pointing tasks. We introduce a brain-based target expansion system, which improves the efficacy of bubble cursor by increasing the expansion of high importance targets at the optimal time based on brain measurements correlated to a particular type of multitasking. We demonstrate through controlled experiments that brain-based target expansion can deliver a graded and continuous level of assistance to a user according to their cognitive state, thereby improving task and speed-accuracy metrics, even without explicit visual changes to the system. Such an adaptation is ideal for use in complex systems to steer users toward higher priority goals during times of increased demand.

We show how brain sensing can lend insight to the evaluation of visual interfaces and establish a role for fNIRS in visualization. Research suggests that the evaluation of visual design benefits by going beyond performance measures or questionnaires to measurements of the user's cognitive state. Unfortunately, objectively and unobtrusively monitoring the brain is difficult. While functional near-infrared spectroscopy (fNIRS) has emerged as a practical brain sensing technology in HCI, visual tasks often rely on the brain's quick, massively parallel visual system, which may be inaccessible to this measurement. It is unknown whether fNIRS can distinguish differences in cognitive state that derive from visual design alone. In this paper, we use the classic comparison of bar graphs and pie charts to test the viability of fNIRS for measuring the impact of a visual design on the brain. Our results demonstrate that we can indeed measure this impact, and furthermore measurements indicate that there are not universal differences in bar graphs and pie charts.

We present a novel integration of a brain-computer interface (BCI) with a multi-touch surface. BCIs based on the P300 paradigm often use a visual stimulus of a flashing character to elicit an event related potential in the brain's EEG signal. Traditionally, P300-based BCI paradigms use a grid layout of visual targets, commonly an alphabet, and allow users to select targets using their thoughts. In our new system a multi-touch table senses objects placed upon its surface and the system can highlight the objects on the table by flashing an area of light around them. This allows us to construct a P300-based BCI that uses a user-assembled collection of objects as targets, rather than a pre-determined grid layout. An experiment shows that our new paradigm works just as well as the traditional paradigms, thus highlighting the potential for BCIs to be integrated in a broader range of situations.