Dynamical Measures of Control

I am interested in how we can leverage time-varying aspects of interaction behaviour to understand user experience and help systems adapt to user needs.

There is a large and growing body of evidence that temporal patterning in skilled behaviour carries information about cognition, perception and experience. One strand of my research investigates how this patterning can be used to identify factors which drive positive and negative experiences with technology: Breakdowns in user-technology coordination, due to factors like fatigue, and tight sensorimotor couplings that lead to enjoyable feelings of control. One exciting feature of these methods is that they often only require the capture and analysis of existing control inputs: from keyboards, mice, eye-trackers, or game controllers.

Below are my existing publications on this topic. In ongoing work I am currently applying these analytical techniques to control and experience in videogames.

Papers

Multifractal Mice: Operationalising Dimensions of Readiness-to-hand via a Feature of Hand Movement

Multifractality in Typing as a Marker of Fatigue

Multifractality as a lens on embodied human computer interaction