Improving the Human–Computer Dialogue With Increased Temporal Predictability

Abstract

Objective: An experiment was conducted to investigate the impacts of length and variability of system response time (SRT) on user behavior and user experience (UX) in sequential computing tasks.Background: Length is widely considered to be the most important aspect of SRTs in human-computer interaction. Research on temporal attention shows that humans adjust to temporal structures and that performance substantially improves with temporal predictability.Method: Participants performed a sequential task with simulated office software. Duration and variability, that is, the number of different SRTs, was manipulated. Lower variability came at the expense of on average higher durations. User response times, task execution times, and failure rates were measured to assess user performance. UX was measured with a questionnaire.Results: A reduction in variability improved user performance significantly. Whereas task load and failure rates remained constant, responses were significantly faster. Although a reduction in variability came along with, on average, increased SRTs, no difference in UX was found.Conclusion: Considering SRT variability when designing software can yield considerable performance benefits for the users. Although reduced variability comes at the expense of overall longer SRTs, the interface is not subjectively evaluated to be less satisfactory or demanding. Time design should aim not only at reducing average SRT length but also at finding the optimum balance of length and variability.Application: Our findings can easily be applied in any user interface for sequential tasks. User performance can be improved without loss of satisfaction by selectively prolonging particular SRTs to reduce variability.

Time-based task expectancy: perceptual task indicator expectancy or expectancy of post-perceptual task components?

“…The resulting scheduling of the processes leads to system delays of different length (Blazewicz et al, 2007 ). Several previous findings showed that such variable delays reduce user satisfaction and performance (Fischer et al, 2005 ; Weber et al, 2013 ). On the contrary, a study conducted by Thomaschke and Haering ( 2014 ) showed that performance improved if delays were variable but highly predictive for the upcoming event.…”

Incidental covariation learning leading to strategy change

“…Delays of action effects often covary with outcomes and demands for corrections in everyday human-computer interaction (cf. Thomaschke and colleagues, [65, 66]). For instance, an unexpected long waiting time for the sound of the paper tray suggests the printer has a problem.…”

Timing affect: Dimension-specific time-based expectancy for affect.

“…Our interactions with the environment become increasingly mediated by technology (Livingstone, 2009), and are hence affected by technological delays (Dabrowski & Munson, 2011). As most everyday life computing resources rely on heavily parallel processing, system delay must actively be scheduled across users and processes (Weber, Haering, & Thomaschke, 2013). A recent study by Thomaschke and Haering (2014) demonstrated that time-based expectancy can be successfully employed to inform delay scheduling algorithms.…”