Different Markov chains modulate visual stimuli processing in a Go-Go experiment in 2D, 3D and augmented reality

Abstract

The introduction of Augmented Reality (AR) has atracted several developments, although the people’s experience of AR has not been clearly studied or contrasted with the human experience to 2D and 3D environments. Here, the directional task was applied in 2D, 3D and AR using simplified stimulus in video games to determine whether there is a difference in human answer reaction time prediction using context stimulus. Testing of the directional task adapted task paradigm is in the Supplementary MaterialResearch question: Are the main differences between 2D, 3D and AR able to be predicted using Markov chains?Methods: A computer was fitted with a digital acquisition card in order to record, test and validate the reaction time (RT) of participants (see Supplementary Material) attached to the arranged RT for the theory of Markov chain probability. A prior survey was conducted and served as a pre-assessment in order to better interpret the data collected from the user throughout the experiment. A Markov chain analysis was performed on the participants’ data. Subsequently, the way certain factors influenced participants RT amongs the three tasks time on the accuracy of the participants was sought in the three tasks (environments) were statististically tested using ANOVA.Results: Markov chains of order 1 and 2 successfully reproduced the average reaction time by participants in 3D and AR tasks, having only 2D task with the variance predicted with the current state. Moreover, a clear explanation of delayed reaction time at every modality was done and quantified.Our results suggest that unconscious preparation of selective choices is not restricted to current motor preparation. Instead, decisions at different environment evolve from the dynamics of preceding cognitive activity.