@article{huang_pugh_kim_nam_2024, title={Brain dynamics of mental workload in a multitasking context: Evidence from dynamic causal modeling}, volume={152}, ISSN={["1873-7692"]}, DOI={10.1016/j.chb.2023.108043}, abstractNote={Multitasking is a common element in complex human-computer interactions and is known to impose deleterious mental workload demands. High mental workload is known to involve bilateral hemisphere activation, but the patterns of effective connectivity (directed causal influence or communication) among brain regions in such a context remain unclear. This study investigated the effect of mental workload on the causal influence brain regions exert over each other under a multitasking scenario. The Dynamic Causal Modeling (DCM) method was implemented to infer the flow of information and allocation of attentional resources. Thirty participants performed four subtasks with varying levels of workload on a computer-based multitasking program, simulating a pilot cockpit. Using eight brain regions commonly identified to be activated in multitasking conditions, nine candidate models were developed. Bayesian model averaging was then used to quantify the connectivity strengths among the brain regions. Linear regression was conducted to study the relationships between connection strengths and subtask performances. The results showed that the causal connections shifted from the left to both sides of the brain with increased workload. Linear regression analysis showed that the subtask performance could be predicted by connectivity strengths. Thus, by studying the brain dynamics of mental workload, we may be able to develop a predictor that supplements subjective self-report measures.}, journal={COMPUTERS IN HUMAN BEHAVIOR}, author={Huang, Jiali and Pugh, Zachary H. and Kim, Sangyeon and Nam, Chang S.}, year={2024}, month={Mar} }
 @article{pugh_huang_leshin_lindquist_nam_2022, title={Culture and gender modulate dlPFC integration in the emotional brain: evidence from dynamic causal modeling}, volume={5}, ISSN={["1871-4099"]}, DOI={10.1007/s11571-022-09805-2}, abstractNote={Past research has recognized culture and gender variation in the experience of emotion, yet this has not been examined on a level of effective connectivity. To determine culture and gender differences in effective connectivity during emotional experiences, we applied dynamic causal modeling (DCM) to electroencephalography (EEG) measures of brain activity obtained from Chinese and American participants while they watched emotion-evoking images. Relative to US participants, Chinese participants favored a model bearing a more integrated dorsolateral prefrontal cortex (dlPFC) during fear v. neutral experiences. Meanwhile, relative to males, females favored a model bearing a less integrated dlPFC during fear v. neutral experiences. A culture-gender interaction for winning models was also observed; only US participants showed an effect of gender, with US females favoring a model bearing a less integrated dlPFC compared to the other groups. These findings suggest that emotion and its neural correlates depend in part on the cultural background and gender of an individual. To our knowledge, this is also the first study to apply both DCM and EEG measures in examining culture-gender interaction and emotion.}, journal={COGNITIVE NEURODYNAMICS}, author={Pugh, Zachary H. and Huang, Jiali and Leshin, Joseph and Lindquist, Kristen A. and Nam, Chang S.}, year={2022}, month={May} }
 @article{pugh_choo_leshin_lindquist_nam_2022, title={Emotion depends on context, culture and their interaction: evidence from effective connectivity}, volume={17}, ISSN={["1749-5024"]}, DOI={10.1093/scan/nsab092}, abstractNote={Abstract}, number={2}, journal={SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE}, author={Pugh, Zachary H. and Choo, Sanghyun and Leshin, Joseph C. and Lindquist, Kristen A. and Nam, Chang S.}, year={2022}, month={Feb}, pages={206–217} }
 @article{huang_choo_pugh_nam_2022, title={Evaluating Effective Connectivity of Trust in Human-Automation Interaction: A Dynamic Causal Modeling (DCM) Study}, volume={64}, ISSN={["1547-8181"]}, url={https://doi.org/10.1177/0018720820987443}, DOI={10.1177/0018720820987443}, abstractNote={Objective Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other—effective connectivity (EC)—in the context of trust in automation. }, number={6}, journal={HUMAN FACTORS}, publisher={SAGE Publications}, author={Huang, Jiali and Choo, Sanghyun and Pugh, Zachary H. and Nam, Chang S.}, year={2022}, month={Sep}, pages={1051–1069} }