The world we live in is continuously on the move and contains a wide variety of motion flow. The flow of traffic, the flow of a poem, the flow of ocean currents, or your workflow. With this continuous flow of information comes complexity, complexity that is hard to tease apart and fully understand. We humans seem to be able to effortlessly deal with these complexities. Just by looking at a picture of a swarm of starlings we can practically see the motion in the static image. We somehow can interpret complex biological behaviours of collective flow, flow where agents show both collective and individual behaviour following a coordinated set of rules. In this research project we investigate how the human visual system interprets and predicts these collective behaviours.
Which visual features, cues, or information do we use to interpret these collective patterns and how do we make conclusions out of them? Even more intriguing is how we can predict the future states of such complex patterns. Over eons of evolution, we’ve developed mechanisms that appear to do this very efficiently. We are not perfect, we make mistakes in these estimations, but we do it well and fast enough to pass it on to future generations.
Perception of collective flow has high potential as a field of research because of two main reasons. 1. Very basic (low-level) depictions of collective motion can be generated while very complex (high-level) behaviours are perceived (e.g. agitation, discipline, leadership). This allows for an interesting use case to investigate bottom-up and top-down interactions in the visual cortex. Especially top-down processing (e.g. can cognitive reasoning steer our sensitivities to particular types of motion) is something vision scientists try to better understand and could have large implications for computer vision, machine learning, AI, and applications thereof. 2. Potential of generalization. There are many types of collective flow with inanimate occurrences (e.g. shaken metallic rods, nematic fluids), microscopic occurrences (e.g. macromolecules, cells, bacteria colonies), and richer manifestations with more intelligent organisms (e.g. insect swarms, flocks of birds, humans, traffic). When you look for it, it is all around us. When we understand how we humans process this information efficiently we can mimic this behaviour with models that in turn can be applied in technology to interact with collective patterns more efficiently and robustly.
