A dynamical systems view of neuroethology: Uncovering stateful computation in natural behaviors

PreyTouch: a touchscreen-based closed-loop system for studying predator-prey interactions

The ability to catch prey is crucial for survival and reproduction and is subject to strong natural selection across predators. Prey capture demands the orchestrated activation of multiple brain regions and the interplay between sensory processing, decision-making, and motor execution. These factors, together with the ubiquity of prey capture across species makes it appealing for comparative studies across neuroscience and ecology. However, despite recent technological advances, experimental approaches for studying natural behaviors such as prey catch are lagging behind. To bridge this gap, we created PreyTouch-a novel approach for performing prey capture experiments that incorporate flexible prey control, accurate monitoring of predator touchscreen strikes and automated rewarding. Further, its real-time processing enables coupling predator movement and prey dynamics for studying predator-prey interactions. Finally, PreyTouch is optimized for automated long-term experiments featuring a web UI for remote control and monitoring. We successfully validated PreyTouch by conducting long-term prey capture experiments on the lizard Pogona vitticeps. This revealed the existence of prey preferences, complex prey attack patterns, and fast learning of prey dynamics. PreyTouch's unique features and the importance of studying prey capture behavior make it a valuable platform for connecting natural behavior with cognitive studies across various species and disciplines.

The Nested States Model: A Phenomenologically-Grounded Model of the Mind

BACKGROUND

Subjective experience is central to the nature of mental illness, yet it has not played a central role in most empirical approaches to psychopathology. While phenomenological perspectives in psychiatry have seen a recent resurgence, there remains a need for more detailed models of psychopathological processes based on explicit phenomenological and enactive foundations.

SUMMARY

We present a framework derived from the Nested States Model (NSM) through which such phenomenologically-grounded models might be constructed. The NSM describes the dynamic structure of subjective experience as a system of nested states that reciprocally influence one another across hierarchical layers. Here, we show how the NSM provides a scheme for characterizing patterns of experience that comprise various psychopathological processes. We demonstrate the utility of this scheme both for clinical practice and for building our knowledge of psychopathological processes more broadly.

KEY MESSAGES

The NSM can advance three aims that we see as critical for the lasting integration of phenomenological approaches to psychopathology within psychiatry. First, we show that the NSM provides a means for constructing clinical formulations and treatment considerations that center squarely on an individual's subjective experiences. Second, the NSM supplies a framework for organizing findings from clinical-phenomenological research that can guide the construction of broader phenomenologically-grounded models of psychopathological processes. Lastly, the NSM aligns our perspective on subjective experience with emerging perspectives on brain dynamics, helping to bridge phenomenological work with ongoing neurophysiological research.

ReptiLearn: An automated home cage system for behavioral experiments in reptiles without human intervention

Understanding behavior and its evolutionary underpinnings is crucial for unraveling the complexities of brain function. Traditional approaches strive to reduce behavioral complexity by designing short-term, highly constrained behavioral tasks with dichotomous choices in which animals respond to defined external perturbation. In contrast, natural behaviors evolve over multiple time scales during which actions are selected through bidirectional interactions with the environment and without human intervention. Recent technological advancements have opened up new possibilities for experimental designs that more closely mirror natural behaviors by replacing stringent experimental control with accurate multidimensional behavioral analysis. However, these approaches have been tailored to fit only a small number of species. This specificity limits the experimental opportunities offered by species diversity. Further, it hampers comparative analyses that are essential for extracting overarching behavioral principles and for examining behavior from an evolutionary perspective. To address this limitation, we developed ReptiLearn-a versatile, low-cost, Python-based solution, optimized for conducting automated long-term experiments in the home cage of reptiles, without human intervention. In addition, this system offers unique features such as precise temperature measurement and control, live prey reward dispensers, engagement with touch screens, and remote control through a user-friendly web interface. Finally, ReptiLearn incorporates low-latency closed-loop feedback allowing bidirectional interactions between animals and their environments. Thus, ReptiLearn provides a comprehensive solution for researchers studying behavior in ectotherms and beyond, bridging the gap between constrained laboratory settings and natural behavior in nonconventional model systems. We demonstrate the capabilities of ReptiLearn by automatically training the lizard Pogona vitticeps on a complex spatial learning task requiring association learning, displaced reward learning, and reversal learning.