Engine sounds reflect a racecar driver’s cognition

Singh, Jaskeerat; Shah, Yawer H.; Tonello, Lucio; Tonello, Lucio; Tonello, Lucio; Cappello, Glenda; Cappello, Glenda; Giammaria, Raffaele; Kerick, Scott; Grigolini, Paolo; West, Bruce J. · 2026 · DOAJ

DOI: 10.3389/fphy.2025.1633608

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Summary

This study investigates whether the engine sounds of racecars reflect the cognitive state and adaptability of their drivers. Motivated by the "hard problem" of cognition and the need for data-supported theoretical interpretations of consciousness, the authors draw an analogy between racecar drivers and musicians. Just as human-performed music exhibits higher complexity than computer-generated music due to conscious interpretation, the authors hypothesize that a driven racecar engine produces sound with greater complexity than an idle engine, mirroring the driver’s conscious motor control and decision-making processes. The research analyzes audio data from the International Automobile Federation (FIA) Formula 4 E4 Championship, focusing on the fastest qualifying laps of 12 male drivers aged approximately 16.25 years. Engine noise was recorded using onboard cameras and sampled every 1/16 second to capture pitch frequency changes. The authors employed Modified Diffusion Entropy Analysis (MDEA) to process these time series. This method detects "crucial events"—invisible inputs such as gear shifts—by identifying transitions between defined "stripes" in the signal. These events were converted into a trajectory using a velocity model, allowing the calculation of a scaling parameter ($\delta$). This parameter quantifies system complexity, where $\delta = 1$ represents maximal adaptability (conscious, intelligent control) and $\delta = 0.5$ represents random, minimal adaptability (simple diffusion). The results demonstrate that higher values of the scaling parameter $\delta$, measured during a single qualifying lap, correlate with better overall performance in the championship. The study further examines the learning curve of novice drivers, finding that training facilitates a shift in the scaling parameter from approximately 0.7 to values near 1 as drivers gain experience. Conversely, the authors note that competitive stress can temporarily decrease this parameter, aligning with phenomenology theory regarding ergodicity breaking. The analysis confirms that the engine noise contains significant information about the driver’s cognitive engagement and ability to adapt to the complex demands of racing. The significance of this work lies in its contribution to the emerging field of human-machine interaction and the study of cognition. By establishing that engine noise serves as a proxy for driver cognition, the research offers a non-invasive method to detect crucial cognitive events in real-time. The authors suggest that these findings could inform rehabilitation therapies, drawing parallels to the therapeutic applications of music, and provide a framework for understanding how complex biological systems interact with and transmit information to machines. This approach bridges theoretical concepts of complexity matching and ergodicity breaking with practical applications in motorsports and cognitive science.

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• music audio mood

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• Theoretical Contribution: computational model