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

Area of Science:

• Human-Computer Interaction
• Cognitive Science
• Computational Neuroscience

Background:

• Human experience in Augmented Reality (AR) remains understudied compared to 2D and 3D environments.
• Understanding reaction time (RT) differences across these environments is crucial for AR development.

Purpose of the Study:

• To investigate if Markov chains can predict human reaction time differences in 2D, 3D, and AR environments.
• To explore factors influencing reaction time and accuracy across these distinct visual-spatial contexts.
• To determine if cognitive preparation extends beyond immediate motor actions.

Main Methods:

• Participants' reaction times were recorded and analyzed using Markov chain analysis (orders 1 and 2).
• Statistical testing (ANOVA) was employed to assess the influence of environmental factors and demographics on RT.
• A directional task within simplified video games was used across 2D, 3D, and AR settings.

Main Results:

• Markov chains of order 1 and 2 accurately predicted average reaction times in 3D and AR, while 2D tasks showed variance related to the current state.
• Delayed reaction times were explained across all environments.
• While mood and coffee intake did not significantly alter RTs, gender differences emerged in 3D but not AR, potentially due to AR interface elements.

Conclusions:

• Human decision-making and reaction times in different environments (2D, 3D, AR) are influenced by preceding cognitive activity, not just immediate preparation.
• Findings suggest that neurocomputational models can be refined to incorporate these dynamics.
• AR interfaces may mitigate certain cognitive or demographic differences observed in traditional 3D environments.