Context and trade-offs characterize real-world threat detection systems: A review and comprehensive framework to improve research practice and resolve the translational crisis. (August 2020) - Fendt, Markus; Parsons, Michael H.; Apfelbach, Raimund; Carthey, Alexandra J.R.; Dickman, Chris R.; Endres, Thomas; Frank, Anke S.K.; Heinz, Daniel E.; Jones, Menna E.; Kiyokawa, Yasushi; Kreutzmann, Judith C.; Roelofs, Karin; et al.
Highlights: In nature, antipredator decision-making is shaped by context and characterized by trade-offs. In the laboratory, neurobiological models of fear and anxiety control context and limit trade-offs. Translational science is failing because models fail to address real-world conditions. We develop a mechanistic model to show why contextual factors should be experimentally manipulated. Abstract: A better understanding of context in decision-making—that is, the internal and external conditions that modulate decisions—is required to help bridge the gap between natural behaviors that evolved by natural selection and more arbitrary laboratory models of anxiety and fear. Because anxiety and fear are mechanisms evolved to manage threats from predators and other exigencies, the large behavioral, ecological and evolutionary literature on predation risk is useful for re-framing experimental research on human anxiety-related disorders. We review the trade-offs that are commonly made during antipredator decision-making in wild animals along with the context under which the behavior is performed and measured, and highlight their relevance for focused laboratory models of fear and anxiety. We then develop an integrative mechanistic model of decision-making under risk which, when applied to laboratory and field settings, should improve studies of the biological basis of normal and pathological anxiety and may therefore improve translational outcomes.