Electrodermal activity as an index of food neophobia outside the lab.

Introduction: Understanding how food neophobia affects food experience may help to shift toward sustainable diets. Previous research suggests that individuals with higher food neophobia are more aroused and attentive when observing food-related stimuli. The present study examined whether electrodermal activity (EDA), as index of arousal, relates to food neophobia outside the lab when exposed to a single piece of food. Methods: The EDA of 153 participants was analyzed as part of a larger experiment conducted at a festival. Participants completed the 10-item Food Neophobia Scale. Subsequently, they saw three lids covering three foods: a hotdog labeled as "meat", a hotdog labeled as "100% plant-based", and tofu labeled as "100% plant-based". Participants lifted the lids consecutively and the area-under-the-curve (AUC) of the skin conductance response (SCR) was captured between 20 s before and 20 s after each food reveal. Results: We found a significant positive correlation between food neophobia and AUC of SCR during presentation of the first and second hotdog and a trend for tofu. These correlations remained significant even when only including the SCR data prior to the food reveal (i.e., an anticipatory response). Discussion: The association between food neophobia and EDA indicates that food neophobic individuals are more aroused upon the presentation of food. We show for the first time that the anticipation of being presented with food already increased arousal for food neophobic individuals. These findings also indicate that EDA can be meaningfully determined using wearables outside the lab, in a relatively uncontrolled setting for single-trial analysis.

Dual Passive Reactive Brain-Computer Interface: A Novel Approach to Human-Machine Symbiosis.

The present study proposes a novel concept of neuroadaptive technology, namely a dual passive-reactive Brain-Computer Interface (BCI), that enables bi-directional interaction between humans and machines. We have implemented such a system in a realistic flight simulator using the NextMind classification algorithms and framework to decode pilots' intention (reactive BCI) and to infer their level of attention (passive BCI). Twelve pilots used the reactive BCI to perform checklists along with an anti-collision radar monitoring task that was supervised by the passive BCI. The latter simulated an automatic avoidance maneuver when it detected that pilots missed an incoming collision. The reactive BCI reached 100% classification accuracy with a mean reaction time of 1.6 s when exclusively performing the checklist task. Accuracy was up to 98.5% with a mean reaction time of 2.5 s when pilots also had to fly the aircraft and monitor the anti-collision radar. The passive BCI achieved a F1-score of 0.94. This first demonstration shows the potential of a dual BCI to improve human-machine teaming which could be applied to a variety of applications.