EEG and Eye-Tracking Error-Related Responses During Predictive Text Interactions: A BCI Case Study.

Brain-computer interfaces (BCIs) employ various paradigms which afford intuitive, augmented control for users to navigate digital technologies. In this study we explore the application of these BCI concepts to predictive text systems: commonplace interactive and assistive tools with variable usage contexts and user behaviors. We conducted an experiment to analyze user neurophysiological responses under these different usage scenarios and evaluate the feasibility of a closed-loop, adaptive BCI for use with such technologies. We recorded electroencephalogram (EEG) and eye tracking (ET) data from participants while they completed a self-paced typing task in a simulated predictive text environment. Participants completed the task with different degrees of reliance on the predictive text system (completely dependent, completely independent, or their choice) and encountered both correct and incorrect text generations. Data suggest that erroneous text generations may evoke neurophysiological responses that can be measured with both EEG and pupillometry. Moreover, these responses appear to change according to users' reliance on the predictive text system. Results show promise for use in a passive, hybrid, BCI with a closed-loop, adaptive framework, and support a neurophysiological approach to the challenge of real-time human feedback on system performance.

The "voice" has it: screen reader adoption and switching behavior among vision impaired persons in India.

We present results from a mixed methods study of screen reader use and switching behavior among people with vision impairments in India. We examine loyalty and experimentation with screen readers and find that the main drivers of adoption for early users differ significantly from the factors that drive continued use by advanced users. We discuss the factor that emerges as one of the strongest stated drivers of early adoption, text-to-speech "voice" quality, particularly a "human-sounding voice" as one of the key features differentiating free/open source products from more expensive proprietary products. While the initial preferences are driven by voice quality, application support becomes more important over time as users speed up their sound settings and become more comfortable with the resultant non-human-sounding speech. We discuss these findings from two theoretical perspectives--first, through the application of the economics of behavior switching, and second, vis-à-vis novice and expert approaches toward new product adoption. We argue that these findings further our understanding of initial user comfort related to assistive technology adoption, and the impact of early technology choices on long-term technology switching behavior.