Switching costs in stochastic environments drive the emergence of matching behaviour in animal decision-making through the promotion of reward learning strategies

Lyu, Nan; Hu, Yunbiao; Zhang, Jiahua; Lloyd, Huw; Sun, Yipeng; Tao, Yi

doi:10.1038/s41598-021-02979-5

Cited by 1 publication

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is somewhat surprising, because even a simple pure Win-Stay-Lose-Shift strategy could have yielded a better performance (Additional file 1 : Figure S1). One reason not to use a pure WSLS strategy (in which the animal either stays 100% of the time or shifts 100% of the time, based on reward) was suggested by Lyu et al [ 22 ] who noted that shifting resources (in our case the feeders) also comes with a cost and thus might prevent animals from using a WSLS strategy if the environment is highly unstable.…”

Section: Discussionmentioning

confidence: 99%

Fruit bats adjust their decision-making process according to environmental dynamics

Naamani,

Shahar,

Ger

et al. 2023

BMC Biol

View full text Add to dashboard Cite

One of the main functions of behavioral plasticity lies in the ability to contend with dynamic environments. Indeed, while numerous studies have shown that animals adapt their behavior to the environment, how they adapt their latent learning and decision strategies to changes in the environment is less understood. Here, we used a controlled experiment to examine the bats’ ability to adjust their decision strategy according to the environmental dynamics. Twenty-five Egyptian fruit bats were placed individually in either a stable or a volatile environment for four consecutive nights. In the stable environment, two feeders offered food, each with a different reward probability (0.2 vs. 0.8) that remained fixed over two nights and were then switched, while in the volatile environment, the positions of the more and the less rewarding feeders were changed every hour. We then fit two alternative commonly used models namely, reinforcement learning and win-stay-lose-shift strategies to the bats' behavior. We found that while the bats adapted their decision-making strategy to the environmental dynamics, they seemed to be limited in their responses based on natural priors. Namely, when the environment had changed slowly, at a rate that is natural for these bats, they seemed to rely on reinforcement learning and their performance was nearly optimal, but when the experimental environment changed much faster than in the natural environment, the bats stopped learning and switched to a random decision-making strategy. Together, these findings exemplify both the bats’ decision-making plasticity as well as its natural limitations.

show abstract

Section: Discussionmentioning

confidence: 99%