The influence of prior intention on joint action: an fNIRS-based hyperscanning study

Friend or foe: classifying collaborative interactions using fNIRS

To succeed, effective teams depend on both cooperative and competitive interactions between individual teammates. Depending on the context, cooperation and competition can amplify or neutralize a team's problem solving ability. Therefore, to assess successful collaborative problem solving, it is first crucial to distinguish competitive from cooperative interactions. We investigate the feasibility of using lightweight brain sensors to distinguish cooperative from competitive interactions in pairs of participants (N=84) playing a decision-making game involving uncertain outcomes. We measured brain activity using functional near-infrared spectroscopy (fNIRS) from social, motor, and executive areas during game play alone and in competition or cooperation with another participant. To distinguish competitive, cooperative, and alone conditions, we then trained support vector classifiers using combinations of features extracted from fNIRS data. We find that features from social areas of the brain outperform other features for discriminating competitive, cooperative, and alone conditions in cross-validation. Comparing the competitive and alone conditions, social features yield a 5% improvement over motor and executive features. Social features show promise as means of distinguishing competitive and cooperative environments in problem solving settings. Using fNIRS data provides a real-time measure of subjective experience in an ecologically valid environment. These results have the potential to inform intelligent team monitoring to provide better real-time feedback and improve team outcomes in naturalistic settings.

Distinct neural couplings to shared goal and action coordination in joint action: evidence based on fNIRS hyperscanning

Joint action is central to human nature, enabling individuals to coordinate in time and space to achieve a joint outcome. Such interaction typically involves two key elements: shared goal and action coordination. Yet, the substrates entrained to these two components in joint action remained unclear. In the current study, dyads performed two tasks involving both sharing goal and action coordination, i.e. complementary joint action and imitative joint action, a task only involving shared goal and a task only involving action coordination, while their brain activities were recorded by the functional near-infrared spectroscopy hyperscanning technique. The results showed that both complementary and imitative joint action (i.e. involving shared goal and action coordination) elicited better behavioral performance than the task only involving shared goal/action coordination. We observed that the interbrain synchronization (IBS) at the right inferior frontal cortex (IFC) entrained more to shared goal, while left-IFC IBS entrained more to action coordination. We also observed that the right-IFC IBS was greater during completing a complementary action than an imitative action. Our results suggest that IFC plays an important role in joint action, with distinct lateralization for the sub-components of joint action.

Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy

Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen's d, the power analysis indicated that a high sample size ([Formula: see text]) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.