Co-actors Exhibit Similarity in Their Structure of Behavioural Variation That Remains Stable Across Range of Naturalistic Activities

Abstract

Human behaviour, along with any natural/biological behaviour, has varying degrees of intrinsic 'noise' or variability. Many studies have shown that the structure or patterning of this variability is sensitive to changes in task and constraint. Furthermore, two or more humans interacting together often begin to exhibit similar structures of behavioural variability (i.e., the patterning of their behavioural fluctuations becomes aligned or matched) independent of any moment-to-moment synchronization (termed complexity matching). However, much of the previous work has focused on a subset of simple or contrived behaviours within the context of highly controlled laboratory tasks. In the current study, individuals and pairs performed five self-paced (unsupervised), semi-structured activities around a university campus. Empatica E4 wristbands and iPhones were used to record the participants' behavioural activity via accelerometers and GpS. the results revealed that the structure of variability in naturalistic human behaviour co-varies with the task-goal constraints, and that the patterning of the behavioural fluctuations exhibited by co-acting individuals does become aligned during the performance of everyday activities. The results also revealed that the degree of complexity matching that occurred between pairs remained invariant across activity type, indicating that this measure could be employed as a robust, task-independent index of interpersonal behaviour. When measuring any human movement or behaviour over time, the resulting measurements always contain various magnitudes of natural variability. This is true whether one considers an individual's reaction times to repeated environmental stimuli 1 , the spatial accuracy of an individual when hitting a nail with a hammer 2,3 , or an individual's stride length when walking or running 4,5. Even an individual's head and postural position while sitting or standing still fluctuates over time 6. Traditionally, such behavioural variation was assumed to be random or non-functional sensorimotor or measurement 'noise' 7-9. However, there is now a growing body of research demonstrating that the behavioural fluctuations that occur during natural human activity are typically correlated over time (i.e., exhibit varying degrees of self-similarity and non-randomness) 1,10-13. Of particular relevance here, is that research exploring the patterning of human behavioural variability has demonstrated how the structure of such variation is sensitive to changes in task and environmental constraint, with some patterns of behavioural variance being more or less random than others 11,12,14. Recent research suggests that the patterning of co-actors' behavioural variability can also become aligned or 'matched' together during task performance, even when the movements or actions of the co-actors are not locally synchronized or coordinated. This latter phenomenon has been termed complexity matching and implies that the behavioural actions and movements of co-acting individuals can become entrained...

Assessing Team Effectiveness by How Players Structure Their Search in a First‐Person Multiplayer Video Game

“…Further, DFA, as employed here, is an individual‐level measure of behavior and thus can only assess SA, as opposed to TSA. A potential extension is to relate TSA to the covariation of DFA α between team members, which has been found to occur during two‐person tasks (Abney et al., 2014 ; Almurad et al., 2017 ; Rigoli, Lorenz, et al., 2020 ). This observation, referred to as “complexity matching” is hypothesized to enable maximal information transmission between individuals (West et al., 2008 ), and thus enhance coordination.…”

“…DFA α can be understood as reflecting a scale that balances external, environment, and task constraints, with internal, individual constraints. Variation in DFA α due to the interplay of task, environment, and internal constraints also extends to social contexts, where a phenomenon known as “complexity matching” can occur (Abney, Paxton, Dale, & Kello, 2014 ; Almurad, Roume, & Delignières, 2017 ; Rigoli, Lorenz, et al., 2020 ), where the DFA α of coactors can be found to correlate with one another, possibly to enable maximal information transmission between individuals (West, Geneston, & Grigolini, 2008 ).…”

Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals

“…The estimated scaling exponent  is the slope of the least-squares fitted line; (d) representative examples of white (random), pink (fractal, long-range correlated, slightly persistent), and blue (anti-persistent) time and power-spectrum series. (Adapted from [44]).…”

Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals

“…As explained in detail in [34,37], applying the DFA methodology yields both a meas ure of persistence and an index of self-similarity in the time series. In short, α ≈ 0.5 indi cates that the signal is uncorrelated or non-similar in variability structure, as in the cas of white noise (Figure 3d).…”

“…(a) Example of time series; (b) integrated series divided into different size windows with their corresponding linear-fit trends; (c) log-log plot of average fluctuations FDFA(n) versus window sizes n. The estimated scaling exponent α is the slope of the least-squares fitted line; (d) representative examples of white (random), pink (fractal, longrange correlated, slightly persistent), and blue (anti-persistent) time and power-spectrum series. (Adapted from ref [37]…”