Unsupervised identification of internal perceptual states influencing psychomotor performance

Abstract

When humans perform repetitive tasks over long periods, their performance is not constant. People may drift in and out of states that might be loosely categorised as engagement, disengagement or ‘flow’ and these will be reflected in multiple aspects of their performance (for example, reaction time, accuracy, criteria shifts and potentially longer-term strategy) but until recently it has been challenging to relate these behavioural states to the underlying neural mechanisms that generate them. Here, we took Magnetoencephalograpy recordings of participants performing an engaging task that required rapid, strategic behavioural responses. In this way we acquired both high density neural data and contemporaneous, dense behavioural data. Specifically, participants played a laboratory version of Tetris which collects detailed recordings of player input and game-state throughout performance. We asked whether it was possible to infer the presence of distinct behavioural states from the behavioural data and, if so, whether these states would have distinct neural correlates. We used hidden Markov modelling to segment the behavioural time series into states with unique behavioural signatures, finding that we could identify three distinct and robust behavioural states. We then computed occipital alpha power across each state. These within-participant differences in alpha power were statistically significant, suggesting that individuals shift between behaviourally and neurally distinct states during complex performance, and that visuo-spatial attention change across these states.