Toward an Antiphony Framework for Dividing Tasks into Subtasks

Abstract

Summary: Task analysis is a staple of ergonomics, neuroergonomics, human factors, and experimental psychology inquiry, and often benefits from granularity beyond the task level to the subtask level. The concept and challenge of identifying the subcomponents of tasks are neither new, nor solved. Practitioners routinely identify individually internally consistent and yet conflicting subdivisions. The challenge of producing reliable, valid subtask data across efforts recommends a unified framework for identifying consistent subtask divisions within tasks. A framework is here forwarded, based upon universal "antiphony" turn-taking behavior in human-human interaction, but adapted to address the highly scripted vocabulary of human-machine interaction. Practical application to a real-world vehicle interface is demonstrated, an example discussed in the light of research design, applied use, and future improvement.

Toward a Theory of Visual Information Acquisition in Driving

“…Collapsing large areas of space (e.g., windshield, side mirrors, instrument cluster, center stack) means data are not coregistered with what the driver could see. Coarse temporal classification of eye movements has similar challenges, with classifications such as “driving,” “urban,” or “highway” subtending long, complex periods of information acquisition, often interleaved between road and in-vehicle information systems (see Sawyer, Mehler, 2017). While questions about situation awareness can be asked to acquire understanding about what information the driver has acquired, questions about how they acquired that information are lost to averaging over space and time.…”

Trust Impacts Driver Glance Strategy in Multitasking

“…Using the antiphony framework (Sawyer, Reimer & Mehler, 2017), both DVs were calculated separately for epochs in which driver or system was actively engaged and communication occurred (active), and the silent waiting that could proceed communication (latency). Pre-experiment reported level of trust in technology, on a 1-10 Likert scale, were strongly positively skewed.…”