Predicting Resilience Losses in Dyadic Team Performance.

Area of Science:

• Psychology
• Human-Computer Interaction
• Dynamical Systems Theory

Background:

• Resilience is crucial for stable performance in dynamic environments.
• Understanding the early warning signals of resilience loss is vital for team performance.
• Dynamical systems approaches offer novel insights into complex system behaviors.

Purpose of the Study:

• To investigate resilience losses in dyadic performance using a dynamical systems approach.
• To identify temporal patterns signaling transitions and resilience losses in human performance.
• To assess the utility of cross-recurrence quantification analysis and critical fluctuation measures.

Main Methods:

• Dyads (n=42) performed a lateral rhythmical pointing task under ascending and descending scoring scenarios.
• Cross-recurrence quantification analysis was used to measure performance complexity and resilience.
• Lag 1 autocorrelations and mean squared successive differences (MSSD) were applied to detect critical slowing down and critical fluctuations.

Main Results:

• Performance transitions were observed, though not consistently induced by scoring scenarios across individuals.
• Contrary to critical slowing down indicators, increased MSSD reliably signaled performance transitions.
• Negative performance transitions were associated with increased fluctuations in both performance and complexity, indicating resilience loss.

Conclusions:

• Mean squared successive differences (MSSD) serve as a critical indicator of human performance transitions.
• Increased complexity fluctuations specifically signal negative performance transitions and resilience losses.
• Combining performance fluctuation and complexity data can predict positive and negative dyadic team performance transitions.