Inferring behavioral rules of movement from trajectory data
Animals need to search efficiently for resources like food and shelter. However, most work on animal search for novel resources assumes simple forms of a random walk, despite evidence of sophisticated cognitive capabilities of even simple organisms. I study the search behavior of ants to find the non-random behaviors which allow ant colonies to find resources efficiently. I apply modern trajectory analysis tools and modeling to large data sets I gather from lab experiments.
In this presentation, I will focus on four main points: 1) Efficiency of area coverage strategies in the math world vs the real world, where noise has a big influence. 2) Regular left-right meandering, which is nested over multiple scales by analyzing the turn-autocorrelation of trajectories. This non-random behavior may contribute to higher efficiency. 3) Possible other behaviors like pheromone trail avoidance and modulation of behavior according to nest-mate presence. 4) How we may use modern techniques to detect structure in the tracks, like short repeating elements and hierarchy of movement patterns.