The ability to flexibly switch our response to external stimuli based on context is essential for navigating a complex world, highlighting the importance of context-dependent computations in decision-making.
We developed a novel task in rats to investigate context-dependent evidence accumulation, demonstrating that three dynamical solutions can serve as a mathematical framework for this computation.
Our findings indicate that there is significant variability in neural implementations and decision-making strategies among rats, suggesting that diverse approaches can co-exist for successful task performance.
By employing high-throughput methods, we were able to train many rats on the task, revealing a rich heterogeneity in individual performance and neural strategies despite overall success.
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