Brain Interaction during Cooperation: Evaluating Local Properties of Multiple-Brain Network

Abstract

Subjects' interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, mental workload, to define an objective measure based on how and if team members are interacting is not so straightforward. In this study, behavioral, subjective and synchronized electroencephalographic data were collected from couples involved in a cooperative task to describe the relationship between task difficulty and team coordination, in the sense of interaction aimed at cooperatively performing the assignment. Multiple-brain connectivity analysis provided information about the whole interacting system. The results showed that averaged local properties of a brain network were affected by task difficulty. In particular, strength changed significantly with task difficulty and clustering coefficients strongly correlated with the workload itself. In particular, a higher workload corresponded to lower clustering values over the central and parietal brain areas. Such results has been interpreted as less efficient organization of the network when the subjects' activities, due to high workload tendencies, were less coordinated.

Keywords: EEG; cooperation; human interaction; hyperscanning; mental workload; multiple-brain connectivity.

Figure 1

NASA-Multi Attribute Task Battery (MATB). (I) Interface: (a) Emergency lights task; (b) Tracking task; (c) Auditory monitoring task, and; (d) Fuel managing task. (II) Participants shared the MATB task. In particular, the Pilot (on the right) performed the tracking and emergency lights tasks, while the Co-Pilot (on the left) dealt with the fuel managing and the auditory monitoring tasks.

Figure 2

Bar diagram reporting the performance (a), NASA-Task Load Index (NASA-TLX) (b) and workload index (c) value obtained in Easy (blue bar) and Hard (red bar) conditions. The symbol * points out a significant difference between the considered conditions (p < 0.05).

Figure 3

Grand Average Connectivity Maps obtained comparing Easy (first column) and Hard (second column) conditions with baseline condition in Theta (first row), Alpha (second row) and Beta (third row) conditions. The patterns are reported on a 2-D scalp model seen from above with the nose pointing to the upper part of the page. Only significant connections were reported (paired t-test, p < 0.05). The colour and diameter of the arrows code for the averaged strength were obtained within the experimental group. The value D is the inter-brain density.

Figure 4

Bar diagram reporting the strength value obtained in Easy and Hard conditions over frontal, central, parietal and occipital brain areas, in theta (a) and alpha (b) electroencephalogram (EEG) bands. The symbol * points out statistical difference between the considered conditions (paired t-test, p < 0.05).

Figure 5

Scatterplot reporting the trend of workload index over clustering index for central brain area in the alpha (R = −0.772, p = 0.008) and beta (R = −0.784, p = 0.007) EEG bands (a,b), and for parietal brain area in the alpha (R = −0.058, p = 0.87) and beta (R = −0.690, p = 0.026) EEG bands (c,d). Plots refer to the Hard condition.