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BEHAVIOR

From Rats to Humans: how novel behavioral paradigms and reinforcement learning can bridge the gap in translation

Lab Animal volume 51, pages 289–290 (2022)Cite this article

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A challenge in translational research is comparing behaviors across species. A new study combines a novel behavioral paradigm in rats and humans with reinforcement learning to infer shared computations for goal-directed navigation.

A key motivation for the use of varied animal models in science is the prospect of translation of findings via evolutionary conserved systems and functions1. Successful examples of this approach are the fields of memory and navigation, which have used rodent models to provide a foundational understanding regarding the function of the hippocampal formation in humans2,3,4. However, a critical challenge to the promise of translation is the multiple ways a given organism’s brain can orchestrate a class of behaviors, such as navigation. Indeed, humans and rodents intrinsically differ in the senses used and behaviors executed during navigation. For example, during navigation, humans may rely on visual observation and memory-guided planning whereas rodents, with lower visual acuity and depth perception, may rely more on olfactory and whisker sensory input. Navigational studies are usually optimized for the single species being studied, making it challenging to compare behaviors and underlying computations across species. To address this challenge, de Cothi and colleagues5 constrained the sensory input and navigational experience between humans and rats in a homologous spatial navigation task. By then using reinforcement learning (RL) to model and quantify the task-related behavior of rats and humans, they were able to narrow down the underlying computations used to solve the task across species.

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Fig. 1: Inference of underlying computations as a function of the identification of overlapping behaviors between species and an artificial RL agent.

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  1. Stanford University School of Medicine, Stanford, CA, USA

    Alexander Gonzalez & Lisa M. Giocomo

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  1. Alexander Gonzalez
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  2. Lisa M. Giocomo
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Correspondence to Lisa M. Giocomo.

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The authors declare no competing interests.

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Gonzalez, A., Giocomo, L.M. From Rats to Humans: how novel behavioral paradigms and reinforcement learning can bridge the gap in translation. Lab Anim 51, 289–290 (2022). https://doi.org/10.1038/s41684-022-01077-x

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  • DOI: https://doi.org/10.1038/s41684-022-01077-x

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