TY - GEN
T1 - Integrating non-invasive neuroimaging and computer log data to improve understanding of cognitive processes
AU - Friedman, Leah
AU - Walker, Erin
AU - Liu, Ruixue
AU - Solovey, Erin T.
N1 - Funding Information:
We would like to thank Aria Kim, Anush Lingamoorthy, Patricia Rahmlow, Gloria Houseman, Damian Baraty, Kyle Ellis, Juan Garcia Lopez, Denisa Qori and Reza Moradinezhad for their collaboration. This work was partially funded by Grants DGE-1835307 and CNS-1711773 from the National Science Foundation as well as the CRA-W CREU project sponsored by the National Science Foundation.
PY - 2018/10/16
Y1 - 2018/10/16
N2 - 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.
AB - 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.
KW - FNIRS
KW - Functional near-infrared spectroscopy
KW - SART
UR - http://www.scopus.com/inward/record.url?scp=85058267883&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058267883&partnerID=8YFLogxK
U2 - 10.1145/3279810.3279854
DO - 10.1145/3279810.3279854
M3 - Conference contribution
AN - SCOPUS:85058267883
T3 - Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal Data, MCPMD 2018
BT - Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal Data, MCPMD 2018
PB - Association for Computing Machinery, Inc
T2 - 2018 Workshop on Modeling Cognitive Processes from Multimodal Data, MCPMD 2018
Y2 - 16 October 2018
ER -