Engine sounds reflect a racecar driver’s cognition - Singh, Jaskeerat; Shah, Yawer H.; Tonello, Lucio; Cappello, Glenda; Giammaria, Raffaele; Kerick, Scott; Grigolini, Paolo; West, Bruce J. (Frontiers in Physics, 2026)
We analyze the engine noise of racecars to shed light on the interaction between the brains of the drivers and their racecars and also the interaction between the brains of different drivers for the International Automobile Federation (FIA) Formula 4, E4 Championship. Statistical analysis is performed using the same theoretical tools as those adopted in the recent past to study the brain of an orchestra director through the resulting music. The result of this statistical analysis is the evaluation of a scaling parameter that we compare between drivers. We interpret this scaling parameter as a measure of the driver’s ability, with 1 representing maximal adaptability and 0.5 representing random or minimal adaptability (less than 0.5 does not exist for the trajectory model we have). The results obtained show that higher values of the scaling parameter, measured in a single qualifying lap, correspond to better performance in their championship. We also study the training process that allows novice drivers to move from values of the scaling parameter around 0.7 to values very close to 1 as they gain experience. We find that more experienced drivers have a larger scaling parameter and we also explore the effects of competition that can lead to a decrease of the said scaling parameter. This is in line with phenomenology theory, despite being temporary. This work suggests that the study of racecar noise can shed light on the difficult issue of cognition. Having in mind the therapeutic applications of music, we conjecture that this discovery may provide an important contribution to rehabilitation therapy. We also contribute to the emerging field of human-machine interaction by showing how to transmit crucial events to a machine and detect them.