Understanding canine olfaction has traditionally focused on performance outcomes rather than the cognitive processes emerging through interaction. This emerging study reframes scent detection through the lens of enactivist cognitive science, positioning the dog as an embodied agent whose cognition arises through continuous engagement with the environment.
The authors present an animal–computer interaction (ACI) framework that integrates theory and technology to investigate olfactory cognition as an enacted process. Central to this approach is a sensor-rich olfactory workstation paired with a wearable suit designed specifically for detection dogs.
The prototype system integrates infrared proximity sensors, inertial measurement units, electroencephalography (EEG), and high-definition video, enabling synchronized multimodal data capture. This configuration allows researchers to observe how bodily movement, sensory input, and neural activity co-evolve during real-world scent detection.
Rather than treating olfaction as a passive sensory input, the system supports analysis of how sniffing patterns, locomotion, posture, and decision-making dynamically shape perception. This aligns with enactivist principles, which emphasize that cognition is not computed internally but emerges through action.
The study outlines early analytical capabilities for multimodal data fusion and highlights the potential role of machine learning in identifying meaningful patterns across behavioral, physiological, and neural signals. These tools may enable deeper insights into detection strategies, learning trajectories, and adaptive behavior.
Importantly, the authors emphasize an ethically conscious design philosophy, framing the dog as an active user rather than a passive instrument. By centering canine agency, the system supports more respectful and effective ACI designs for working dogs.
Although still in its early stages, this work establishes a theoretical and empirical foundation for future research at the intersection of enactive cognition, canine science, and animal–computer interaction. The approach offers promising avenues for advancing both scientific understanding and practical applications of detection dogs across security, conservation, and medical fields.
Source: Aswin, R., Sadhu, S. K. T., Bitan, I., & Kulgod, A. (2024). Towards Enactivist ACI – Sensor-Rich Olfactory Workstation and Suit for Detection Dogs. International Conference on Animal-Computer Interaction, published December 2, 2024.
