Max Planck UCL Centre for Computational Psychiatry and Ageing Research and Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
Jack Brookes
Max Planck UCL Centre for Computational Psychiatry and Ageing Research and Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
Samson Hall
Max Planck UCL Centre for Computational Psychiatry and Ageing Research and Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
Sajjad Zabbah
Max Planck UCL Centre for Computational Psychiatry and Ageing Research and Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
Ulises Daniel Serratos Hernandez
Max Planck UCL Centre for Computational Psychiatry and Ageing Research and Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
Dominik R. Bach
Max Planck UCL Centre for Computational Psychiatry and Ageing Research and Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK; University of Bonn, Transdisciplinary Research Area “Life and Health”, Hertz Chair for Artificial Intelligence and Neuroscience, 53121 Bonn, Germany; Corresponding author
Summary: Animals including humans must cope with immediate threat and make rapid decisions to survive. Without much leeway for cognitive or motor errors, this poses a formidable computational problem. Utilizing fully immersive virtual reality with 13 natural threats, we examined escape decisions in N = 59 humans. We show that escape goals are dynamically updated according to environmental changes. The decision whether and when to escape depends on time-to-impact, threat identity and predicted trajectory, and stable personal characteristics. Its implementation appears to integrate secondary goals such as behavioral affordances. Perturbance experiments show that the underlying decision algorithm exhibits planning properties and can integrate novel actions. In contrast, rapid information-seeking and foraging-suppression are only partly devaluation-sensitive. Instead of being instinctive or hardwired stimulus-response patterns, human escape decisions integrate multiple variables in a flexible computational architecture. Taken together, we provide steps toward a computational model of how the human brain rapidly solves survival challenges.
