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Where does the organization in behavior come from? Ecological psychology seeks to explain adaptive behavior as self-organized, emerging from the dynamics of agent-environment interactions, without assuming such organization a priori. This chapter offers an accessible introduction to the behavioral dynamics approach to modeling perception and action, applied to the case of human locomotion and crowd behavior. Most models of such behavior are ‘omniscient’, presuming accurate knowledge of the environmental state, but we find that information-based ‘visual’ models more closely capture human data. At the individual level, a locomotor trajectory emerges from an agent’s interactions with goals and obstacles, as attractors and repellers appear, shift, and bifurcate. At the dyad level, pedestrian interactions governed by visual control laws yield coordinated movements such as interception, collision avoidance, and following. At the collective level, these local interactions generate human ‘flocking’, crowd bifurcations, and patterns of lanes and stripes in crossing flows of pedestrians. By adopting an empirical, bottom-up, information-based approach, behavioral dynamics helps explain individual and collective behavior as resulting from a process of self-organized pattern formation, without appealing to internal plans or external commands. 

Previous simulations of crossing flows using a vision-based collision-avoidance model reproduced lanes and stripes but showed larger heading adjustments during crossing than the human data. Here we investigate two possible explanations. First, we tested participants walking through a virtual crowd under two density conditions, refit the collision avoidance model, and re-simulated the crossing flows data. Our findings reveal little influence of moderate densities on human collision avoidance behavior. Second, we are testing mutual collision avoidance between two participants to determine whether a revised model better approximates the crossing flows data. 

We previously reported an experiment in which covert or explicit leaders (confederates) were placed in a group of walking pedestrians in order to test leader influence on human crowd motion. Here we simulate the participant trajectories with variants of an empirical pedestrian model, treating the covert leaders’ motion as input, and test model agreement with the experimental data. We are currently using reconstructed influence networks [2] to modify the model weights in order to simulate the influence of explicit leaders. The results help us to understand how leader influence propagates via local interactions in real human crowds. 

Conference Abstract