Intra-haptic Bayesian Integration between frequency and intensity in humans: Evidence of super-optimal integration in haptic signals

Abstract

Understanding how the Central Nervous System (CNS) integrates multiple forms of information is crucial for the field of neuromechanics, as it can inform the development of optimal feedback systems. Such systems could benefit individuals with various limitations by easing their difficulties and enhancing their capabilities. This study presents an experimental procedure that investigates how the CNS integrates changes in intensity and frequency of haptic feedback, and tests whether the results align with the Bayesian integration theory. Participants were asked to match reference signals using a custom device and software for three conditions: changing intensity, changing frequency, and simultaneous changes in both. Overall, this study investigated the integration of changes in intensity and frequency of haptic feedback within the somatosensory modality, and found that combining different types of haptic information may improve perceptual precision. While the results were supported by Bayesian integration theory, the data also suggests the need for further investigation into potential interactions between inter-sensory modalities, such as audio-haptic feedback. Furthermore, the results showed that the subjects had the least errors in perception when both intensity and frequency conditions are changing, suggesting that the implementation of both conditions is optimal. The study opens up avenues for future research, such as exploring how exactly the variation of both conditions should be from two instantaneous points in time, and whether linearly increasing the levels of both conditions is the best way to communicate information to a user. Additionally, the study suggests that there may be intra-haptic integration ability in humans, which could be explored in future research.