(85) The Effect of Game Elements and Learning Mechanics on Neural Signatures of Cognitive Control and Performance
Date:
Contributors: Cloude, E. B. , Huber, S. E., Ninaus, M., & Kober, S. E.
Venue: 1st Cognitive Imaging Research Symposium, East Lansing, Michigan, USA, August 21, 2025
Abstract: Game-based learning (GBL) environments are increasingly used to enhance learning outcomes by embedding game mechanics that foster emotional and cognitive engagement. While GBL is often effective, the cognitive-neural mechanisms that underlie its success remain poorly understood. It is unclear how specific design features, such as game elements (e.g., visuals, music, rewards) and learning mechanics (e.g., rule-based tasks grounded in learning theory), interact to influence cognitive control, a core function of the prefrontal cortex (PFC) essential for goal-directed learning. Our study addresses this gap by examining whether GBL design features shape dynamic patterns of PFC activity during learning. Using functional near-infrared spectroscopy (fNIRS), we recorded hemodynamic responses (ΔHbO, ΔHbR) across 9 PFC regions-of-interest (ROIs) while 41 healthy adults (n = 41) completed four task conditions that systematically varied in the presence of game elements (no game elements = nGE vs. GE) and learning mechanics (+LM vs. -LM). To capture the complexity of neural dynamics, we applied multiplex recurrence network analysis, which enables the quantification of local (= within each ROI) measures (recurrence, determinism, entropy), inter-regional (= between ROIs) measures (coupling), and global (= among all ROIs) measures (network efficiency) to describe properties of hemodynamic brain activity. Results showed that GE+LM tasks elicited significantly higher recurrence and determinism, along with elevated entropy, suggesting a balance between stability and flexibility in local-level hemodynamics. Predictive modeling revealed that only local-level hemodynamics significantly predicted task accuracy, with structured yet flexible patterns (e.g., high recurrence and moderate entropy) supporting better performance outcomes. Surprisingly, inter-regional coupling between ROIs was strongest in GE-LM tasks, indicating that game elements alone may drive stimulus-bound synchrony, while learning mechanics promote more differentiated processing. GE+LM tasks showed modestly higher global efficiency than other conditions, although this effect was contingent upon the presence of both GE and LM. These findings suggest that effective learning depends more on localized, adaptive control processes than on global coordination, offering new insights into how design features shape optimal brain functioning during game-based learning.