Increased temporal sensitivity for threat: A Bayesian Generalised Linear Mixed Modelling approach

People overestimate the duration of threat-related facial expressions moreover, this effect increases with self-reported fearfulness (Tipples, 2008, 2011). Here, I test whether increased overestimation for threat-related facial expressions in high fearfulness generalizes to pictures of of threatening animals. A further goal was to illustrate the use of Bayesian Generalised Linear Mixed Modelling (BGLMM) to gain more accurate estimates of temporal performance, including estimates of temporal sensitivity. Participants (N = 53) completed a temporal bisection task in which they judged the duration of threatening animals (poised to attack) and non-threatening animals. People overestimated the duration of threatening animals and the effect increased with self-reported fearfulness. In support of rapid accumulation of time due to threat, temporal sensitivity was higher for threat compared to non-threatening images. Analyses indicate that temporal sensitivity effects may have been absent in previous research because of the method used to calculate the index of temporal sensitivity. The benefits of using the Bayesian Generalised Linear Mixed Modelling (BGLMM) are highlighted and researchers are encouraged to use this method as the first option for analysing temporal bisection data.