Imagine searching a collection of coins for quarters (0.25), dimes (0.10), nickels (0.05), and pennies (0.01)—a hybrid foraging task where observers look for multiple instances of multiple target types. In such tasks, how do target values and their prevalence influence foraging and eye movement behaviors (e.g., should you prioritize rare quarters or common nickels)? To explore this, we conducted human psychophysics experiments, revealing that humans are proficient reward foragers. Their eye fixations are drawn to regions with higher average rewards, fixation durations are longer on more valuable targets, and their cumulative rewards exceed chance, approaching the upper bound of optimal foragers. To probe these decision-making processes of humans, we developed a transformer-based Visual Forager (VF) model trained via reinforcement learning. Our VF model takes a series of targets, their corresponding values, and the search image as inputs, processes the images using foveated vision, and produces a sequence of eye movements along with decisions on whether to collect each fixated item. Our model outperforms all baselines, achieves cumulative rewards comparable to those of humans, and approximates human foraging behavior in eye movements and foraging biases within time-limited environments. Furthermore, stress tests on out-of-distribution tasks with novel targets, unseen values, and varying set sizes demonstrate the VF model’s effective generalization. Our work offers valuable insights into the relationship between eye movements and decision-making, with our model serving as a powerful tool for further exploration of this connection. All data, code, and models are available at https://github.com/ZhangLab-DeepNeuroCogLab/visual-forager.

In this paper, we consider the attitude tracking control problem for rigid spacecraft with external disturbances and model uncertainties. An attitude tracking predefined-time controller with predefined bound is proposed based on back-stepping. The controller can make the attitude error of spacecraft converge into a predefined bound within the predefined time with continuous and non-singular torque. Numerical simulations are carried out to illustrate the effectiveness of the controller proposed.

This paper studies the finite time attitude control of a satellite with flexible appendages when the velocity measurements are not available in the condition of external disturbances and model uncertainties. First a dynamic model which considers the coupling effect between the motion of the rigid hub and the deformation of the flexible appendages is build. Then a terminal sliding mode observer is present to rebuild the velocity information of the satellite in finite time. With this estimation of velocity, a finite time attitude controller is designed based on nonsingular fast terminal sliding mode theory. In the end, simulations are presented to shown the effectiveness of the proposed control scheme with high accuracy and fast convergence.