Humans can perform complex actions even when based on degraded visual information, as when walking along a path at dusk or reaching for a water bottle that is partly hidden by other objects. A generally accepted explanation is that the brain creates the “best guess” given the available visual information and this best guess is usually good enough. For example, when you reach for a water bottle multiple times, your hand might fall sometimes short and sometimes overreach in an unpredictable way. On average, however, your hand will land in an appropriate location. Theoretically, when feedback is available —like touching the bottle—this situation is easier to correct than when errors are random and unpredictable. Understanding the computational strategies the brain uses to allow humans to interact with their environment despite degraded visual information could directly inform the design of more robust AI-robotic systems. This has vital implications for national security and public safety, where autonomous systems must operate reliably under poor visibility to detect potentially dangerous objects or agents, or find survivors in enclosed spaces after disasters such as flood or earthquakes. In addition, this research can translate into development of bio-inspired sensory technologies, such as advanced vision-based prosthetics and contribute to designing living environments for individuals with low vision. This project aims to challenge long-held theoretical assumptions about th