Field and experimental data together with computational models reveal how cleaner fish adjust decisions in a biological market

Abstract

While it is generally straightforward to quantify individual performance in cognitive experiments, identifying the underlying cognitive processes remains a major challenge. Often, different mechanistic underpinnings yield similar performances, and Lloyd Morgans cannon warrants acceptance of the simpler explanation. Alternatively, when the different mechanisms interact with environmental conditions, variation in performance across environments might allow to statistically infer the mechanism responsible. We illustrate this point by fitting computational models to experimental data on performance by wild-caught cleaner fish Labroides dimidiatus in an ephemeral reward task, as well as cleaner and client fish densities from the locations of capture. Using Bayesian statistics to fit the model parameters to performance data revealed that cleaner fish most likely estimate future consequences of an action, while it appears unlikely that the removal of the ephemeral reward acts as psychological punishment (negative reinforcement). Incorporating future consequences also yields performances that can be considered the result of locally optimal decision-rules, in contrast to the negative reinforcement mechanism. We argue that the combination of computational models with data is a powerful tool to infer the mechanistic underpinnings of cognitive performance. Author summaryPerformance in behavioural experiments is often used to assess the cognitive abilities of animals. However, animals can get to the same outcome in alternative ways. Thus, the outcome of the experiments does not tell us how animals achieve their performance. In order to overcome this limitation, we used a set of computational models, which provide predictions on how alternative cognitive mechanisms perform in the face of varying environmental conditions, and compare their predictions to performance data from individuals that experienced different conditions throughout their life. Our study system is the cleaner fish Labroides dimidiatus, a coral reef fish that feeds off ectoparasites and dead tissue from the skin of other reef fishes. Cleaner fish often have to choose among alternative clients seeking their cleaning service, and the optimal choice is often hard to achieve. Our performance experiments mimic the natural choices the cleaner fish make. The combination of experiments and computational models points to cleaner fish abilities to account for the future effect of their choices by estimating the long term expected value of those choices. Estimating long term value is a mechanism involved in human foresight.