Statistical modelling of gaze behaviour as categorical time series: what you should watch to save soccer penalties
Previous research on gaze behaviour in sport has typically reported summary fixation statistics thereby largely ignoring the temporal sequencing of gaze. In the present study on penalty kicking in soccer, our aim was to apply a Markov chain modelling method to eye movement data obtained from goalkeepers. Building on the discrete analysis of gaze employed by Dicks et al. (Atten Percept Psychophys 72(3):706720, 2010b), we wanted to statistically model the relative probabilities of the goalkeepers gaze being directed to different locations throughout the penalty takers approach (Dicks et al. in Atten Percept Psychophys 72(3):706720, 2010b). Examination of gaze behaviours under in situ and video-simulation task constraints reveals differences in information pickup for perception and action (Attention, Perception and Psychophysics 72(3), 706720). The probabilities of fixating anatomical locations of the penalty taker were high under simulated movement response conditions. In contrast, when actually required to intercept kicks, the goalkeepers initially favoured watching the penalty takers head but then rapidly shifted focus directly to the ball for approximately the final second prior to foot-ball contact. The increased spatio-temporal demands of in situ interceptive actions over laboratory-based simulated actions lead to different visual search strategies being used. When eye movement data are modelled as time series, it is possible to discern subtle but important behavioural characteristics that are less apparent with discrete summary statistics alone.
gaze behaviour time series markov chain modelling representative design