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Memory Alone Does Not Account for the Way Rats Learn a Simple Spatial Alternation Task

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Dayan,  P
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kastner, D., Gillespie, A., Dayan, P., & Frank, L. (2020). Memory Alone Does Not Account for the Way Rats Learn a Simple Spatial Alternation Task. The Journal of Neuroscience, 40(38), 7311-7317. doi:10.1523/JNEUROSCI.0972-20.2020.


Cite as: http://hdl.handle.net/21.11116/0000-0007-07E6-E
Abstract
Animal behavior provides context for understanding disease models and physiology. However, that behavior is often characterized subjectively, creating opportunity for misinterpretation and misunderstanding. For example, spatial alternation tasks are treated as paradigmatic tools for examining memory; however, that link is actually an assumption. To test this assumption, we simulated a reinforcement learning agent equipped with a perfect memory process. We found that it learns a simple spatial alternation task more slowly and makes different errors than a group of male rats, illustrating that memory alone may not be sufficient to capture the behavior. We demonstrate that incorporating spatial biases permits rapid learning and enables the model to fit rodent behavior accurately. Our results suggest that even simple spatial alternation behaviors reflect multiple cognitive processes that need to be taken into account when studying animal behavior.Significance statementMemory is a critical function for cognition whose impairment has significant clinical consequences. Experimental systems aimed at testing various sorts of memory are therefore also central. However, experimental designs to test memory are typically based on intuition about the underlying processes. We tested this using a popular system: a spatial alternation task. Using behavioral modeling, we show that the most straightforward intuition that these tasks just probe spatial memory, does not account for the speed with which rats learn or the types of errors they make. Only when memory-independent dynamic spatial preferences are added can the model learn like the rats. This highlights the importance of respecting the complexity of animal behavior to interpret neural function and validate disease models.