Abstract
The computational principles underlying predictive capabilities in animals are poorly understood. Here, we wondered whether predictive models mediating prey capture could be reduced to a simple set of sensorimotor rules performed by a primitive organism. For this task, we chose the larval zebrafish, a tractable vertebrate that pursues and captures swimming microbes. Using a novel naturalistic 3D setup, we show that the zebrafish combines position and velocity perception to construct a future positional estimate of its prey, indicating an ability to project trajectories forward in time. Importantly, the stochasticity in the fish's sensorimotor transformations provides a considerable advantage over equivalent noise-free strategies. This surprising result coalesces with recent findings that illustrate the benefits of biological stochasticity to adaptive behavior. In sum, our study reveals that zebrafish are equipped with a recursive prey capture algorithm, built up from simple stochastic rules, that embodies an implicit predictive model of the world.
Highlights
It is becoming clear from recent ethological and neuroscience studies that remarkable capabilities in animals are often built by combining sets of more basic behaviors
Characterizing the goals, algorithms, and advantages of animals possessing implicit models should provide insight into the evolution of more advanced forms of predictive knowledge (Spelke and Hespos, 2018). We examine these questions through characterization and computational modeling of 3D prey capture sequences executed by the larval zebrafish
We uncovered three basic rules that larval zebrafish implement while hunting their fastmoving prey: 1) prey position linearly governs the aspects of five degrees of freedom in which fish can rotate or translate through the water: rotation in yaw and pitch, as well as lateral, vertical and radial displacement; 2) prey velocity modulates all of these aspects of 3D motion and allows the fish to project prey position forward in time; and 3) prey coordinate transformation operates via graded variance based on prey proximity to the strike zone
Summary
It is becoming clear from recent ethological and neuroscience studies that remarkable capabilities in animals are often built by combining sets of more basic behaviors. Characterizing the goals, algorithms, and advantages of animals possessing implicit models should provide insight into the evolution of more advanced forms of predictive knowledge (Spelke and Hespos, 2018) We examine these questions through characterization and computational modeling of 3D prey capture sequences executed by the larval zebrafish. All aspects of the fish’s 3D movement choices are strongly and proportionally modulated by the angular and radial velocity of its prey Combining these two rules yields an emergent strategy whereby the fish predicts future prey locations and recursively halves the angle of attack. This work reveals that even the most complex behavior in larval zebrafish can be reduced to a set of simple rules These rules coalesce to generate a stochastic recursive algorithm embodied by zebrafish during hunting, which reflects an implicit predictive model of the world
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