Back

Decision-making strategies of two related fish species diverge under increased perceptual load

Tomasek, M.; Dunster, B.; Goverts, Z.; Naceur, D.; Jordan, A. L.; Dufour, V.

2025-03-07 animal behavior and cognition
10.1101/2025.03.07.641999 bioRxiv
Show abstract

Three main mechanisms have been proposed to explain divergent decisions between species faced with the same options: differences in sensory abilities, attentional capacities, or cognitive evaluations. While these mechanisms have been well-established in controlled settings, there is limited empirical evidence regarding the ways in which different species make decisions under varying perceptual loads. Here we investigated the decision-making processes of two closely related cichlid species, Aulonocranus dewindti and Cyathopharynx furcifer, in Lake Tanganyika. Males of these species construct sand bowers from which they remove foreign objects, and we examined their preferences when presented with objects varying in three attributes: colour, size, and shape. We show that both species exhibited similar preferences under low perceptual load. Similarly, when the perceptual load was increased by increasing the number of options, both species choices were driven by attentional capture, and an oddity effect was observed. However, when the perceptual load was increased by adding more attributes (feature conjunctions), the species diverged in their decision-making. One species, A. dewindti, evaluated the options in an absolute manner, considering all available attributes, as evidenced by its insensitivity to decoy effects. The other species, C. furcifer either showed attentional capture or evaluated the options in a comparative manner. We discuss the potential reasons behind these differences and their implications for the evolution of decision-making mechanisms.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.