Wild animals suppress the spread of socially transmitted misinformation.

Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviors to misinformation epidemics. Transmission of information through groups depends on the rules that individuals use to transform the perceived actions of others into their own behaviors. Because it is often not possible to directly infer decision-making strategies in situ, most studies of behavioral spread assume that individuals make decisions by pooling or averaging the actions or behavioral states of neighbors. However, whether individuals may instead adopt more sophisticated strategies that exploit socially transmitted information, while remaining robust to misinformation, is unknown. Here, we study the relationship between individual decision-making and misinformation spread in groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously through groups. Using automated visual field reconstruction of wild animals, we infer the precise sequences of socially transmitted visual stimuli perceived by individuals during decision-making. Our analysis reveals a feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially transmitted cues. This form of dynamic gain control can be achieved by a simple and biologically widespread decision-making circuit, and it renders individual behavior robust to natural fluctuations in misinformation exposure.

An Inductance-Based Sensing System for Bellows-Driven Continuum Joints in Soft Robots.

In this work we present a novel, inductance-based system to measure and control the motion of bellows-driven continuum joints in soft robots. The sensing system relies on coils of wire wrapped around the minor diameters of each bellows on the joint. As the bellows extend, these coils of wire become more distant, decreasing their mutual inductance. Measuring this change in mutual inductance allows us to measure the motion of the joint. By dividing the sensing of the joint into two sections and measuring the motion of each section independently, we are able to measure the overall deformation of the joint with a piece-wise constant-curvature approximation. This technique allows us to measure lateral displacements that would be otherwise unobservable. When measuring bending, the inductance sensors measured the joint orientation with an RMS error of 1.1 °. The inductance sensors were also successfully used as feedback to control the orientation of the joint. The sensors proposed and tested in this work provided accurate motion feedback that would be difficult to achieve robustly with other sensors. This sensing system enables the creation of robust, self-sensing soft robots based on bellows-driven continuum joints.