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Nevin Manimala Statistics

Finding food in the dark: how trajectories of a gymnotiform fish change with spatial learning

J Exp Biol. 2022 Nov 11:jeb.244590. doi: 10.1242/jeb.244590. Online ahead of print.

ABSTRACT

We analysed the trajectories of freely foraging Gymnotus sp., a pulse type gymnotiform weakly electric fish, swimming in a dark arena. We compared the fish’s initial behaviour as it learned the relative location of landmarks and food, to its behaviour after learning was complete, i.e., after time/distance to locate food had reached a minimal asymptotic level. During initial exploration when the fish did not know the arena layout, trajectories included many sharp angle head turns that occurred at nearly completely random intervals. After spatial learning was complete, head turns became far smoother. Interestingly, the fish still did not take a stereotyped direct route to the food but instead took smooth but variable curved trajectories. We measured the fish’s heading angle error (heading angle – heading angle towards food). After spatial learning, the fish’s initial heading angle errors were strongly biased to zero, i.e., the fish mostly turned towards the food. As the fish approached closer to the food, they switched to a random search strategy with a more uniform distribution of heading angle errors.

PMID:36366924 | DOI:10.1242/jeb.244590

By Nevin Manimala

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