Humans gradually integrate sudden gain or loss of visual information into spatial orientation perception.

Introduction: Vestibular and visual information is used in determining spatial orientation. Existing computational models of orientation perception focus on the integration of visual and vestibular orientation information when both are available. It is well-known, and computational models capture, differences in spatial orientation perception with visual information or without (i.e., in the dark). For example, during earth vertical yaw rotation at constant angular velocity without visual information, humans perceive their rate of rotation to decay. However, during the same sustained rotation with visual information, humans can continue to more accurately perceive self-rotation. Prior to this study, there was no existing literature on human motion perception where visual information suddenly become available or unavailable during self-motion. Methods: Via a well verified psychophysical task, we obtained perceptual reports of self-rotation during various profiles of Earth-vertical yaw rotation. The task involved transitions in the availability of visual information (and control conditions with visual information available throughout the motion or unavailable throughout). Results: We found that when visual orientation information suddenly became available, subjects gradually integrated the new visual information over ~10 seconds. In the opposite scenario (visual information suddenly removed), past visual information continued to impact subject perception of self-rotation for ~30 seconds. We present a novel computational model of orientation perception that is consistent with the experimental results presented in this study. Discussion: The gradual integration of sudden loss or gain of visual information is achieved via low pass filtering in the visual angular velocity sensory conflict pathway. In conclusion, humans gradually integrate sudden gain or loss of visual information into their existing perception of self-motion.

A machine learning approach to identify stochastic resonance in human perceptual thresholds.

BACKGROUND: Stochastic resonance (SR) is achieved when a faint signal is improved with the addition of the appropriate amount of white noise. Perceptual thresholds are expected to follow a characteristic performance improvement curve as a function of the white noise level added (i.e., thresholds are reduced with an optimal amount of added white noise, beyond which excessive white noise is no longer beneficial). Since SR exhibition in perceptual thresholds is defined by a shape rather than a statistical difference, the presence of SR is typically identified qualitatively. The current state-of-the-art is for blinded human judges to categorize the presence of SR by visually examining data. While categorizations are made with subject data intermixed within a balanced, simulated dataset, which accounts for false positives, this method is still subjective and prone to human error. NEW METHOD: We use a logistic regression (LR) trained on engineered features in order to quantitatively classify exhibition of SR. The LR was trained on datasets simulated from a model for SR performance enhancement. RESULTS: We implemented the algorithmic classification process in 6 perceptual threshold test cases, informed by the literature and parameters were defined by experimental subject data. Comparison to Existing Method(s): We report algorithmic classifications of SR exhibition, considering the 6 test cases, that outperform existing subjective methods in accuracy (p < 0.05). CONCLUSIONS: We demonstrate that algorithmic classification can effectively identify SR in perceptual thresholds, providing a rigorous, objective, and quantitative approach to identifying the presence of SR.

Galvanic Vestibular Stimulation Produces Cross-Modal Improvements in Visual Thresholds.

BACKGROUND: Stochastic resonance (SR) refers to a faint signal being enhanced with the addition of white noise. Previous studies have found that vestibular perceptual thresholds are lowered with noisy galvanic vestibular stimulation (i.e., "in-channel" SR). Auditory white noise has been shown to improve tactile and visual thresholds, suggesting "cross-modal" SR. OBJECTIVE: We investigated galvanic vestibular white noise (nGVS) (n = 9 subjects) to determine the cross-modal effects on visual and auditory thresholds. METHODS: We measured auditory and visual perceptual thresholds of human subjects across a swath of different nGVS levels in order to determine if some individual-subject determined best nGVS level elicited a reduction in thresholds as compared the no noise condition (sham). RESULTS: We found improvement in visual thresholds (by an average of 18%, p = 0.014). Subjects with higher (worse) visual thresholds with no stimulation (sham) improved more than those with lower thresholds (p = 0.04). Auditory thresholds were unchanged by vestibular stimulation. CONCLUSION: These results are the first demonstration of cross-modal improvement with galvanic vestibular stimulation, indicating galvanic vestibular white noise can produce cross-modal improvements in some sensory channels, but not all.

Three-Dimensional-Printing of Bio-Inspired Composites.

Optimized for millions of years, natural materials often outperform synthetic materials due to their hierarchical structures and multifunctional abilities. They usually feature a complex architecture that consists of simple building blocks. Indeed, many natural materials such as bone, nacre, hair, and spider silk, have outstanding material properties, making them applicable to engineering applications that may require both mechanical resilience and environmental compatibility. However, such natural materials are very difficult to harvest in bulk, and may be toxic in the way they occur naturally, and therefore, it is critical to use alternative methods to fabricate materials that have material functions similar to material function as their natural counterparts for large-scale applications. Recent progress in additive manufacturing, especially the ability to print multiple materials at upper micrometer resolution, has given researchers an excellent instrument to design and reconstruct natural-inspired materials. The most advanced 3D-printer can now be used to manufacture samples to emulate their geometry and material composition with high fidelity. Its capabilities, in combination with computational modeling, have provided us even more opportunities for designing, optimizing, and testing the function of composite materials, in order to achieve composites of high mechanical resilience and reliability. In this review article, we focus on the advanced material properties of several multifunctional biological materials and discuss how the advanced 3D-printing techniques can be used to mimic their architectures and functions. Lastly, we discuss the limitations of 3D-printing, suggest possible future developments, and discuss applications using bio-inspired materials as a tool in bioengineering and other fields.