Quantized Interhemispheric Energy Transfer: Learning Motorized Tasks

All human subjects are not equal: don’t ask them same question. In our day-to-day lives, we encounter people who learn very fast, and some learn very slowly. We think that all people learn at different times. If we take statistical data for learning time and a large population, we would find that the time increases linearly from a very fast learner to a very slow learner. However, our result shows this is not a line, but staircase-like jumps are observed. Each stair signifies an upper and a lower time limit. Human subjects who fall in this flat region learn similarly. Calibrating the parameters estimating psychological attributes is debated extensively in psychology. Subjective variations, latent attributes could assign irrelevant data significant. One such problem is the taxonomy of functional lateralization. By comparing the mirror drawing (MD) task with the meta-analysis of brain signals, we demonstrate that lateralization of two hemispheres follows allowed and restricted time gaps. Discrete time-gaps in the energy transfer led to six temporal classifications of learning efficiencies of human subjects. Correlated meta-analysis studies support the finding. Interhemispheric energy transfer in EEG conducted on twenty subjects showed that subjects whose first attempt to complete an MD task fall in a specific time domain, their learning efficiencies change similarly during procedural learning. Six-time domains were confirmed via multiple cross-verification experiments, governing a latent attribute to subjective variances. Our quantized correction to classical psychometric theory would efficiently calibrate the parameters estimating psychological attributes by classifying subjects into at least six categories.