The virtual hand paradigm: A new method for studying prediction and language-vision interactions

Abstract

We introduce a new method for measuring prediction and language-vision interactions: tracking the trajectories of hand-reaching movements in Virtual Reality (VR) environments. Spatiotemporal trajectory tracking of hand-reaching movements in VR offers an ecologically valid yet controlled medium for conducting experiments in an environment that mirrors characteristics of real-world behaviors. Importantly, it enables tracking the continuous dynamics of processing on a single-trial level. In an exploratory experiment, L2 speakers heard predictive or non-predictive sentences (e.g., “The barber cuts the hair” vs. “The coach remembers the hair”). Participants’ task was to move their hands as quickly and as accurately as possible towards the object most relevant to the sentence. We measured reaction times (RTs) and hand-reaching trajectories as indicators of predictive behavior. There was a main effect of predictability: Predictable items were touched faster than unpredictable ones. Importantly, uncertainty was captured using spatiotemporal survival analysis by prolonged fluctuations in upward and downward vertical hand movements before making a final move to target or distractor. Self-correction of prediction errors was revealed by participants switching the direction of hand-reaching movements mid-trial. We conclude that the virtual hand paradigm enables measuring the onset and dynamics of predictive behavior in real time in single and averaged trial data and captures (un)certainty about target objects and the self-correction of prediction error online in ‘close to real-world’ experimental settings. The new method has great potential to provide additional insights about time-course and intermediate states of processing, provisional interpretations and partial target commitments that go beyond other state-of-the-art methods.