Experimental Approach

The three complementary modes in our investigation : brain and behavior experiments (left) provide basic information about human’s intrinsic dynamics and their interaction; theoretical/computational models (right) incorporate discovered principles and mechanisms into equations that generate data for comparison with experiments; and (top middle) the powerful dynamic clamp called virtual partner interaction (VPI) allows a real partner to interact with an avatar driven by a dynamical model of human social behavior thereby enabling a full exploration of the parameter space of basic social interactions.

 

Neural choreography

Neural circuits originate spatiotemporal signatures in the EEG called neuromarkers. These neuromarkers are transiently recruited to serve specific functions of the human brain, for instance spatial and selective attention, somatosensation or motor coordination. This neuromarker approach has already lead to the discovery of a new set of neural oscillations related to social coordination: the two neuromarkers phi1 and phi2  (See Tognoli et al., 2007 for more detail). This finding was made possible by virtue of using a continuous social coordination task and a high spectral resolution. The method was then applied to other tasks such as intentional social coordination, action observation and delayed imitation (Tognoli et al., 2010; Tognoli & Kelso, submitted; Suutari at al., in prep). This pointed out the diversity of neural mechanisms contributing to different phases and facets of social behavior. This raises fundamental questions about their functional organization and quantitative properties. “How?” and “when?” they work together needs to be understood. Theory and observation suggests that neuromarkers work together in two (likely related) ways: one through phase-, frequency-locking and metastability (Bressler & Kelso, 2001; Bressler & Tognoli et al., 2006; Jensen & Colgin , 2007; Kelso & Tognoli, 2009; Tognoli & Kelso, 2009; Kelso, 2012); the other via deterministic rules of pattern sequence. Together, they form a “neuromarker choreography”.

Within~Between

When elements of a system come to interact, their activity ceases to be solely determined by their selves: their behavior also depends on the system’s other elements and their dynamics. How are neural, behavioral and social factors coordinated in real time so as to make possible the emergence of social cognition? Our team has concentrated for years on the elaboration of the ”coordination dynamics” framework in the perspective of understanding these dynamical similarities across neurobiological, behavioral and social levels (Kelso, 1995; Fuchs and Jirsa, 2008; Kelso, 2009; Kelso et al. 2012). Through the use of hyperscanning techniques, it is now possible of recording simultaneously two brains. This allows the study of the relationships between neuromarkers at the individual brain level (Tognoli et al. 2007) and inter-brain measurement (Dumas et al. 2010). Such investigation of the links between intra- and inter-brain neuromarkers could not only help in the understanding of social cognition but also of the key complementary pair of integration∼segregation (Kelso and Engstrøm, 2006).

Memory & Learning

The EEG hyperscanning experimental setting.

We demonstrated in a finger flexion study (Oullier et al., 2008) that the individual behavior of people, who have socially interacted, continues to exhibit a pattern even when visual contact is removed. This persistence of behavior indicates social memory. The goals of our new study are to provide evidence of social memory in a neurobehavioral study of social coordination, to show its neural correlates and those of its breakdown, and to identify the factors that modulate it. We instructed participants to flex their fingers continuously and at a spontaneous, comfortable pace. For twenty seconds, the participants moved alone, then for twenty seconds, they moved while watching their partner, and then moved without view of their partner for twenty seconds more. In more than half of the cases, participants spontaneously coordinated. Interestingly, we see that of those coordinating trials, their relative phase destabilized once visual contact ended, but their mutually adopted frequency remained approximately the same for some of the time. Looking at interrater agreed judgments, we see a bimodal pattern: one pattern indicating a quickly decaying memory, while the other suggests a longer than twenty second persistence. The instant that a participant loses the social frequency is the instant where we look in the EEG recording. We are pursuing two approaches to calculating the frequency spectra of the neural oscillations. One method uses the entire post-interaction period, while the other takes small windows before, during, and after the moment of social memory loss. This allows us to compare local and global pictures of brain activity in time. Preliminary results indicate that frontocentral activity in the theta band is relevant to social memory.

Learning of a new coordination pattern between left and right limbs: (left) early in practice , (middle) delayed retention test (24h) and (right) similar test without practice.

We also investigate skill learning by developping strategies, conceptual tools and operational measures that enable investigations of the role of initially present patterns of behavior in individual learners. We specifically discovered that the paths that different people take to learning a new skill—whether the skill is acquired suddenly or gradually, how much attention is devoted to learning a new skill and people’s ability to recall that skill—are a function not only of improvements in accuracy of the behavioral patterns produced over the course of practice, but also, and above all, of changes in pattern stability (Kostrubiec et al. 2012).

Team coordination

Behavioral manifold representing team behavior. A similar manifold can also be found for brain dynamics.

Performing a task as a team requires that team members mutually coordinate their actions. It is this coordination that distinguishes the performance of a team from the same actions performed independently. Our goal is to find behavioral and neural correlates of team coordination and team performance.
Tasks performed by teams are often complex with a high number of degrees of freedom. Such tasks are notoriously difficult to analyze because of the high variability of the associated behavioral and brain dynamics. This variability is in part due to the degeneracy of behavioral and brain dynamics. Degeneracy is ubiquitous in the brain, but rarely explicitly acknowledged. Importantly, it demands a departure from the currently prevalent quest for a single neural mechanism. We take advantage of degeneracy and pursue a conceptual and empirical framework which explains variability in geometrical terms. In our framework behavioral and brain signals are interpreted as evolving along a manifold in phase space, which reflects task constraints and team coordination.
Using this approach revealed that team coordination depends on experience, decaying quickly in novices but being maintained longer in intermediate and experienced teams (Dodel et al., 2010). Furthermore, in a study of dual EEG data from a two-member team performing a simulated combat scenario we found that dimensionality increases in the joint brain dynamics of the team members is a signature of increased task demand (Dodel et al., 2011). In another dual EEG study in which a two-member team performed an ecologically valid task, our approach revealed that team coordination is associated with increased inter-brain coherence of beta and gamma rhythms in trial segments associated with information flow between the subjects (Dodel et al., 2012).