No abstract

The introduction of Augmented Reality (AR) has attracted several developments, although the people’s experience of AR has not been clearly studied or contrasted with the human experience in 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 was also done.Research 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 attached to the arranged RT for the theory of Markov chain probability. A Markov chain analysis was performed on the participants’ data. Subsequently, the way certain factors influenced participants RT amongst the three tasks time on the accuracy of the participants was sought in the three tasks (environments) were statistically 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 tasks with the variance predicted with the current state. Moreover, a clear explanation of delayed RT in every environment was done. Mood and coffee did not show significant differences in RTs on a simplified videogame. Gender differences were found in 3D, where endogenous directional goals are in 3D, but no gender differences appeared in AR where exogenous AR buttons can explain the larger RT that compensate for the gender difference. Our results suggest that unconscious preparation of selective choices is not restricted to current motor preparation. Instead, decisions in different environments and gender evolve from the dynamics of preceding cognitive activity can fit and improve neurocomputational models.

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.

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.