Abstract
While studies on fish cognition are increasing, consideration of how methodological details influence the ability to detect and measure performance is lagging. Here, in two separate experiments we compared latency to leave the start position, latency to make a decision, levels of participation and success rates (whether fish entered the rewarded chamber as first choice) across different physical designs. Experiments compared fish performance across 1) two sizes of T-mazes, large and standard, and a plus-maze, and 2) open choice arenas with either two or four doors. Fish in T-mazes with longer arms took longer to leave the start chamber and were less likely to participate in a trial than fish in T-mazes with shorter arms. The number of options, or complexity, in a maze significantly impacted success, but did not necessarily impact behavioural measures, and did not impact the number of fish that reached a chamber. Fish in the plus-maze had similar latencies to leave the start box and time to reach any chamber as fish in the same sized T-maze but exhibited lower overall success. Similarly, in an open choice arena, increasing the number of options – doors to potential reward chambers- resulted in lower probability of success. There was an influence of reward position in the choice arena, with rewarded chambers closest to the sides of the arena resulting in lower latencies to enter and higher probability of decision success. Together our results allow us to offer practical suggestions towards optimal maze design for studies of fish cognition.
Original language | English |
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Pages (from-to) | 974-984 |
Journal | Journal of Fish Biology |
Volume | 103 |
Issue number | 5 |
Early online date | 14 Jul 2023 |
DOIs | |
Publication status | Published - 12 Nov 2023 |
Keywords
- Choice arena
- Cognition
- Fish
- Minnows
- Plus-maze
- Spatial cognition
- Sticklebacks
- T-maze
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Maze design: Size and number of choices impact fish performance in cognitive assays(dataset)
Jones, N. A. R. (Creator) & Webster, M. M. (Creator), OSF, 2023
https://osf.io/w82xk/?view_only=176f80a7298e47389552ef568a5a49e0
Dataset