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Data and Code for: Non-numerical strategies used by bees to solve numerical cognition tasks

posted on 25.12.2020, 23:10 by HaDi MaBouDi, Andrew Barron, James Marshall, Alexander Cope, Eleni Vasilaki, Cwyn Solvi

If we are truly interested in the evolution of cognition in its many forms, it is vital to understand not simply that an animal can solve a task, but how the animal is solving the task. Here, we examined how bees solve a numeric-based task utilizing stimuli common amongst numerical cognition studies. Bees performed well on the task, but additional tests showed that they had learned continuous (non-numerical) cues. In simulations a simple network model containing just nine elements was capable of learning the task, but did so using continuous cues inherent in the training stimuli, with no numerical processing. This model was able to reproduce behaviours that have been considered in other studies indicative of numerical cognition. Our results support the idea that a sense of magnitude may be more primitive and basic than any sense of number. Our findings highlight how problematic inadvertent continuous cues can be for animal studies of numerical cognition. This remains a deep issue within the field of numerical cognition that requires increased vigilance and cleverness on our (the experimenter’s) part. We discuss what we view as currently the best methods for, and suggest new ways by which we can improve assessing, numerical cognition in non-speaking animals.


Brains on Board: Neuromorphic Control of Flying Robots

Engineering and Physical Sciences Research Council

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