Using complexity metrics with R-R intervals and BPM heart rate measures

Lately, growing attention in the health sciences has been paid to the dynamics of heart rate as indicator of impending failures and for prognoses. Likewise, in social and cognitive sciences, heart rate is increasingly employed as a measure of arousal, emotional engagement and as a marker of interpersonal coordination. However, there is no consensus about which measurements and analytical tools are most appropriate in mapping the temporal dynamics of heart rate and quite different metrics are reported in the literature. As complexity metrics of heart rate variability depend critically on variability of the data, different choices regarding the kind of measures can have a substantial impact on the results. In this article we compare linear and non-linear statistics on two prominent types of heart beat data, beat-to-beat intervals (R-R interval) and beats-per-min (BPM). As a proof-of-concept, we employ a simple rest-exercise-rest task and show that non-linear statistics—fractal (DFA) and recurrence (RQA) analyses—reveal information about heart beat activity above and beyond the simple level of heart rate. Non-linear statistics unveil sustained post-exercise effects on heart rate dynamics, but their power to do so critically depends on the type data that is employed: While R-R intervals are very susceptible to non-linear analyses, the success of non-linear methods for BPM data critically depends on their construction. Generally, “oversampled” BPM time-series can be recommended as they retain most of the information about non-linear aspects of heart beat dynamics.

Coming to terms: quantifying the benefits of linguistic coordination

Sharing a public language facilitates particularly efficient forms of joint perception and action by giving interlocutors refined tools for directing attention and aligning conceptual models and action. We hypothesized that interlocutors who flexibly align their linguistic practices and converge on a shared language will improve their cooperative performance on joint tasks. To test this prediction, we employed a novel experimental design, in which pairs of participants cooperated linguistically to solve a perceptual task. We found that dyad members generally showed a high propensity to adapt to each other's linguistic practices. However, although general linguistic alignment did not have a positive effect on performance, the alignment of particular task-relevant vocabularies strongly correlated with collective performance. In other words, the more dyad members selectively aligned linguistic tools fit for the task, the better they performed. Our work thus uncovers the interplay between social dynamics and sensitivity to task affordances in successful cooperation.