How cognitive heuristics can explain social interactions in spatial movement

The movement of pedestrian crowds is a paradigmatic example of collective motion. The precise nature of individual-level behaviours underlying crowd movements has been subject to a lively debate. Here, we propose that pedestrians follow simple heuristics rooted in cognitive psychology, such as ‘stop if another step would lead to a collision’ or ‘follow the person in front’. In other words, our paradigm explicitly models individual-level behaviour as a series of discrete decisions. We show that our cognitive heuristics produce realistic emergent crowd phenomena, such as lane formation and queuing behaviour. Based on our results, we suggest that pedestrians follow different cognitive heuristics that are selected depending on the context. This differs from the widely used approach of capturing changes in behaviour via model parameters and leads to testable hypotheses on changes in crowd behaviour for different motivation levels. For example, we expect that rushed individuals more often evade to the side and thus display distinct emergent queue formations in front of a bottleneck. Our heuristics can be ranked according to the cognitive effort that is required to follow them. Therefore, our model establishes a direct link between behavioural responses and cognitive effort and thus facilitates a novel perspective on collective behaviour.

[1]  A. Schadschneider,et al.  Simulation of pedestrian dynamics using a two dimensional cellular automaton , 2001 .

[2]  A. Schadschneider,et al.  Ordering in bidirectional pedestrian flows and its influence on the fundamental diagram , 2012 .

[3]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[4]  Dirk Helbing,et al.  How simple rules determine pedestrian behavior and crowd disasters , 2011, Proceedings of the National Academy of Sciences.

[5]  Andreas Schadschneider,et al.  An Experimental Study of Pedestrian Congestions: Influence of Bottleneck Width and Length , 2009, 0911.4350.

[6]  Felix Dietrich,et al.  The effect of stepping on pedestrian trajectories , 2015 .

[7]  Brett R Fajen,et al.  Behavioral dynamics of steering, obstacle avoidance, and route selection. , 2003, Journal of experimental psychology. Human perception and performance.

[8]  Yoshihiro Ishibashi,et al.  Self-Organized Phase Transitions in Cellular Automaton Models for Pedestrians , 1999 .

[9]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[10]  W Schiff,et al.  Information Used in Judging Impending Collision , 1979, Perception.

[11]  P. McLeod,et al.  Do Fielders Know Where to Go to Catch the Ball or Only How to Get There , 1996 .

[12]  P. Todd,et al.  Simple Heuristics That Make Us Smart , 1999 .

[13]  Christine J. Ziemer,et al.  Estimating distance in real and virtual environments: Does order make a difference? , 2009, Attention, perception & psychophysics.

[14]  Jonathan D. Nelson,et al.  Simple Heuristics and the Modelling of Crowd Behaviours , 2014 .

[15]  Gerd Gigerenzer,et al.  Models of ecological rationality: the recognition heuristic. , 2002, Psychological review.

[16]  A. Czirók,et al.  Collective Motion , 1999, physics/9902023.

[17]  Cécile Appert-Rolland,et al.  Traffic Instabilities in Self-Organized Pedestrian Crowds , 2012, PLoS Comput. Biol..

[18]  Paul A. Braren,et al.  How We Avoid Collisions With Stationary and Moving Obstacles , 2004 .

[19]  A. Johansson,et al.  Constant-net-time headway as a key mechanism behind pedestrian flow dynamics. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  G. Morone,et al.  Walking there: Environmental influence on walking-distance estimation , 2012, Behavioural Brain Research.

[21]  Andreas Schadschneider,et al.  Evacuation Dynamics: Empirical Results, Modeling and Applications , 2008, Encyclopedia of Complexity and Systems Science.

[22]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[23]  Ellen Garbarino,et al.  Cognitive Effort, Affect, and Choice , 1997 .

[24]  Eli Brenner,et al.  Time course of the effect of the Muller-Lyer illusion on saccades and perceptual judgments. , 2014, Journal of vision.

[25]  Gerd Gigerenzer,et al.  Why Heuristics Work , 2008, Perspectives on psychological science : a journal of the Association for Psychological Science.

[26]  Dirk Helbing,et al.  Experimental study of the behavioural mechanisms underlying self-organization in human crowds , 2009, Proceedings of the Royal Society B: Biological Sciences.

[27]  S. Rushton,et al.  Optic Flow Processing for the Assessment of Object Movement during Ego Movement , 2009, Current Biology.

[28]  F. Matusek,et al.  Efficient secure storage of privacy enhanced video surveillance data in intelligent video surveillance systems , 2008, 2008 23rd International Symposium on Computer and Information Sciences.

[29]  P G Gipps,et al.  A micro simulation model for pedestrian flows , 1985 .

[30]  Rodney A. Brooks,et al.  A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network , 1989, Neural Computation.

[31]  Rodney A. Brooks,et al.  A robot that walks; emergent behaviors from a carefully evolved network , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[32]  Hubert Klüpfel,et al.  Evacuation Dynamics: Empirical Results, Modeling and Applications , 2009, Encyclopedia of Complexity and Systems Science.

[33]  Jeremy F. Burn,et al.  How visual perceptual grouping influences foot placement , 2015, Royal Society Open Science.

[34]  Gerta Köster,et al.  Natural discretization of pedestrian movement in continuous space. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Melissa S Parade,et al.  Humans perceive object motion in world coordinates during obstacle avoidance. , 2013, Journal of vision.

[36]  Bruce L. McNaughton,et al.  Path integration and the neural basis of the 'cognitive map' , 2006, Nature Reviews Neuroscience.

[37]  M. Hollands,et al.  Visually guided stepping under conditions of step cycle-related denial of visual information , 1996, Experimental Brain Research.

[38]  Tandra Ghose,et al.  Generalization between canonical and non-canonical views in object recognition. , 2013, Journal of vision.

[39]  M. Schreckenberg,et al.  Experimental study of pedestrian counterflow in a corridor , 2006, cond-mat/0609691.

[40]  William H Warren,et al.  Follow the leader: visual control of speed in pedestrian following. , 2014, Journal of vision.

[41]  Gerta Köster,et al.  How update schemes influence crowd simulations , 2014 .

[42]  Angel Garcimartín,et al.  Experimental proof of faster-is-slower in systems of frictional particles flowing through constrictions. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  Stefan Holl,et al.  Disentangling the Impact of Social Groups on Response Times and Movement Dynamics in Evacuations , 2015, PloS one.