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As non-invasive neuroimaging techniques become less expensive and more portable, we have the capability to monitor brain activity during various computer activities. This provides an opportunity to integrate brain data with computer log data to develop models of cognitive processes. These models can be used to continually assess an individual's changing cognitive state and develop adaptive human-computer interfaces. As a step in this direction, we have conducted a study using functional near-infrared spectroscopy (fNIRS) during the Sustained Attention to Response Task (SART) paradigm, which has been used in prior work to elicit mind wandering and to explore response inhibition. The goal with this is to determine whether fNIRS data can be used as a predictor of errors on the task. This would have implications for detecting similar cognitive processes in more realistic tasks, such as using a personal learning environment. Additionally, this study aims to test individual differences by correlating objective behavioral data and subjective self reports with activity in the medial prefrontal cortex (mPFC), associated with the brain's default mode network (DMN). We observed significant differences in the mPFC between periods prior to task error and periods prior to a correct response. These differences were particularly apparent amongst those individuals who performed poorly on the SART task and those who reported drowsiness. In line with previous work, these findings indicate an opportunity to detect and correct attentional shifts in individuals who need it most.

Integrating non-invasive neuroimaging and computer log data to improve understanding of cognitive processes