Seungji Lee

Choice overload occurs when an ever-growing number of options impairs decision quality, because evaluating options taxes cognitive resources. We investigated whether reducing cognitive demand could mitigate overload by encouraging greater cognitive effort to achieve optimal choice. We conducted two experiments manipulating cognitive demand in complementary ways: Experiment 1 reduced demand by presenting high-attractiveness sets, and Experiment 2 did so by providing a shortlist tool. In both experiments, participants chose from sets of 6–24 options while their eye-gaze and electroencephalographic (EEG) data were recorded. We found that reducing demand made decisions faster, but did not improve choice performance as set-size increased. Under low-demand conditions, eye-gaze measures revealed narrower search and EEG measures showed reduced working memory engagement per option, together indicating less searching and processing efforts. These results suggest that even with reduced cognitive demand, people coast through easier decisions, conserving effort and leaving the choice overload effect largely intact.

Searching familiar faces in the crowd may involve stimulus-driven attention by emotional significance, together with goal-directed attention due to task-relevant needs. The present study investigated the effect of familiarity on attentional processes by exploring eye fixation-related potentials (EFRPs) and eye gazes when humans searched for, among other distracting faces, either an acquaintance’s face or a newly-learned face. Task performance and gaze behavior were indistinguishable for identifying either faces. However, from the EFRP analysis, after a P300 component for successful search of target faces, we found greater deflections of right parietal late positive potentials in response to newly-learned faces than acquaintance’s faces, indicating more involvement of goal-directed attention in processing newly-learned faces. In addition, we found greater occipital negativity elicited by acquaintance’s faces, reflecting emotional responses to significant stimuli. These results may suggest that finding a familiar face in the crowd would involve lower goal-directed attention and elicit more emotional responses.

It has become increasingly important to monitor drivers’ negative emotions during driving to prevent accidents. Despite drivers’ anxiety being critical for safe driving, there is a lack of systematic approaches to detect anxiety in driving situations. This study employed multimodal biosignals, including electroencephalography (EEG), photoplethysmography (PPG), electrodermal activity (EDA) and pupil size to estimate anxiety under various driving situations. Thirty-one drivers, with at least one year of driving experience, watched a set of thirty black box videos including anxiety-invoking events, and another set of thirty videos without them, while their biosignals were measured. Then, they self-reported anxiety-invoked time points in each video, from which features of each biosignal were extracted. The logistic regression (LR) method classified single biosignals to detect anxiety. Furthermore, in the order of PPG, EDA, pupil, and EEG (easiest to hardest accessibility), LR classified accumulated multimodal signals. Classification using EEG alone showed the highest accuracy of 77.01%, while other biosignals led to a classification with accuracy no higher than the chance level. This study exhibited the feasibility of utilizing biosignals to detect anxiety invoked by driving situations, demonstrating benefits of EEG over other biosignals.