This Research Topic aims to connect quantitative behavioral analysis, individual cognition and decision-making, and neural-inspired models, with ecologically-relevant behavior such as foraging, nest and mate choice, predator avoidance, social interactions, and migration. Behavior is influenced by many factors in a dynamic environment. In contrast, laboratory tasks are often designed to eliminate parameters that could influence behavior, to generate data that fit existing models. Challenges in observing and quantifying behavior in natural situations have made it difficult to develop associated theoretical models, and to relate decision mechanisms to ecological and evolutionary mechanisms. However, recent advances in data acquisition technology, computer vision, behavioral modeling, and machine learning are changing this, by facilitating the collection and efficient processing of large amounts of data to extract relevant behavior and environmental details. New theoretical tools and methods are needed to analyze and model such data and to understand animal behavior in complex, ecologically relevant settings. This topic invites submissions that use theory and experiment to investigate behavioral choices relevant to an animal’s natural environment, and to link levels of organization of behavioral processes, from the neurobiological to individual to group and system-level.
To understand how natural selection has shaped animal behavior and decision-making, it is crucial to link behavior in ecologically-relevant tasks with both the underlying neurobiological mechanisms, and the environmental context. Decision making has been a major focus in the systems and cognitive neuroscience community because it bridges sensory, motor, and executive functions. Important advances have been made in understanding the neurobiological underpinnings of decision-making, using neuronal recordings of animals as they engage in laboratory tasks such as the two-alternative forced choice task, and stochastic modeling of the decision processes. However, the day-to-day decisions made by an animal can be quite different from those made in idealized lab conditions. Choices are often made sequentially ("stay-or-go") instead of between simultaneous alternatives. Behavioral responses tend to be specific to a particular environmental context. Because the outcome of behavior influences an animal's survival and reproduction, evolution shapes behavior to perform in an animal's natural environment. Thus many decision strategies can only be understood in a particular ecological context. For example, context- or state-dependent decisions may be an appropriate strategy in a changing and uncertain environment, but are sub-optimal in a static context. Moreover, since decisions are constrained by an individual's underlying neurobiological circuitry and cognitive abilities, some decision-making mechanisms are the result of evolutionary history, conserved across different species.
This interdisciplinary Research Topic aims to connect theorists and experimentalists studying decision-making in ecologically-relevant contexts. This work spans the fields of ecology, neuroscience, psychology, animal behavior, behavioral economics, computer science, mathematics and physics. Topics include, but are not limited to, foraging, habitat and mate choice, social relationships, perception-based decision modeling, quantitative behavioral analysis, and evolutionary studies. Both theoretical and experimental studies are welcome, and studies that combine both are especially encouraged.
This Research Topic aims to connect quantitative behavioral analysis, individual cognition and decision-making, and neural-inspired models, with ecologically-relevant behavior such as foraging, nest and mate choice, predator avoidance, social interactions, and migration. Behavior is influenced by many factors in a dynamic environment. In contrast, laboratory tasks are often designed to eliminate parameters that could influence behavior, to generate data that fit existing models. Challenges in observing and quantifying behavior in natural situations have made it difficult to develop associated theoretical models, and to relate decision mechanisms to ecological and evolutionary mechanisms. However, recent advances in data acquisition technology, computer vision, behavioral modeling, and machine learning are changing this, by facilitating the collection and efficient processing of large amounts of data to extract relevant behavior and environmental details. New theoretical tools and methods are needed to analyze and model such data and to understand animal behavior in complex, ecologically relevant settings. This topic invites submissions that use theory and experiment to investigate behavioral choices relevant to an animal’s natural environment, and to link levels of organization of behavioral processes, from the neurobiological to individual to group and system-level.
To understand how natural selection has shaped animal behavior and decision-making, it is crucial to link behavior in ecologically-relevant tasks with both the underlying neurobiological mechanisms, and the environmental context. Decision making has been a major focus in the systems and cognitive neuroscience community because it bridges sensory, motor, and executive functions. Important advances have been made in understanding the neurobiological underpinnings of decision-making, using neuronal recordings of animals as they engage in laboratory tasks such as the two-alternative forced choice task, and stochastic modeling of the decision processes. However, the day-to-day decisions made by an animal can be quite different from those made in idealized lab conditions. Choices are often made sequentially ("stay-or-go") instead of between simultaneous alternatives. Behavioral responses tend to be specific to a particular environmental context. Because the outcome of behavior influences an animal's survival and reproduction, evolution shapes behavior to perform in an animal's natural environment. Thus many decision strategies can only be understood in a particular ecological context. For example, context- or state-dependent decisions may be an appropriate strategy in a changing and uncertain environment, but are sub-optimal in a static context. Moreover, since decisions are constrained by an individual's underlying neurobiological circuitry and cognitive abilities, some decision-making mechanisms are the result of evolutionary history, conserved across different species.
This interdisciplinary Research Topic aims to connect theorists and experimentalists studying decision-making in ecologically-relevant contexts. This work spans the fields of ecology, neuroscience, psychology, animal behavior, behavioral economics, computer science, mathematics and physics. Topics include, but are not limited to, foraging, habitat and mate choice, social relationships, perception-based decision modeling, quantitative behavioral analysis, and evolutionary studies. Both theoretical and experimental studies are welcome, and studies that combine both are especially encouraged.
