EEG, psychophysiology, cognitive and sensorimotor characteristics.
Emerging research · Human Performance & Training
Neuroadaptive Training
Predicting individual VR / simulator training efficiency and skill transfer from neurophysiological, psychophysiological and early learning-response data.
Core hypothesis
Individual functional profile shapes the learning trajectory
Different people can show different immediate reactions to the same immersive environment, different rates of learning and different levels of transfer to a physical or simulator task. Neuroadaptive Training treats these differences as measurable structure rather than noise.
The working hypothesis is that baseline neurophysiological, psychophysiological and cognitive characteristics, combined with the first-session response, can be used to forecast subsequent training effectiveness and transfer.
Research logic
From measurement to adaptation
Performance, errors, time, stability and subjective state in early sessions.
Rate and shape of improvement across repeated exposure.
Performance in a comparable task outside the original VR condition.
Personalized content, difficulty and duration for the next training block.
Why CoHMI can study this now
Existing components already exist inside the programme
EEG foundation
Published comparison of activity in real and virtual environments.
Psychophysiological testing
Pre-flight operator tests already linked to performance experiments.
VR / simulator tasks
Immersive and simulator platforms support repeated standardized sessions.
Learning-curve data
Repeated attempts allow analysis of early learning and variability.
Transfer conditions
Current training groups already separate route familiarity and training duration.
Personalization concept
Earlier international VR+AI concept work included digital profiles and adaptive training logic.
Pilot design
A compact study can test the central mechanism
Baseline profile
EEG + agreed cognitive, sensorimotor and psychophysiological measures.
Standardized VR training
Task dynamics, errors, time, stability and subjective state.
Transfer test
A comparable task outside the original VR condition.
Prediction model
Identify baseline and early-response predictors of learning and transfer.
Potential scientific output
From pilot to adaptive VR system
The first result is a predictive model linking individual profile, immersive response, learning dynamics and transfer. A later stage can turn that model into an adaptive VR training system.
predictive modelpersonalized trainingVR adaptationEEG / psychophysiologyskill transfer