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) have atracted several developments, although the human answer to AR have not been clearly studied or contrasted with 2D and 3D experience. Here, Posner paradigm was applied in 2D, 3D and AR to find whether are the differences in prediction using context stimulus. Testing of the Posner adapted task paradigm is in Supplementary MaterialResearch question: Are 2D, 3D and AR main differences processed able to predict with Markov chains?Methods: A digital acquisition card attached to the computer was arranged for testing and validating reaction time recording of participants (see Supplementary Material) and the theory of Markov chain probability. On the other hand, was a survey as a pre-assessment, to better interpret the data collected from the user throughout the experiment. Once obtained the participant data, a Markov chain analysis was performed bearing in mind accuracy True-False sequence. Subsequent to this, the influence of certain factors on the Reaction Time on the accuracy of the participants was sought in the three environments provided by video games with ANOVA.Results: Markov chains of order 1 and 2 successfully reproduced the average reaction time by overall participants in 3D and AR tasks, having only 2D task predicting with the current state and different possible regressors for 3D and AR. Moreover, a clear explanation of delayed reaction time at every modality was done and quantified.