Biology subjects

Quinones, A. E.

Publications and source records attributed to Quinones, A. E..

3 recordsLinked to original sources

bioRxiv2023

The role of learning in the evolution of status signalling: a modeling approach

Adaptive behavioural responses often depend on qualities of the interacting partner of an individual. For example, when competing for resources, an individual might be better off escalating fights with individuals of lower quality, while restraining from fighting individuals of higher quality. Communication systems involving signals of quality allow individuals to reduce uncertainty regarding the fighting ability of their partners and make more adaptive behavioral decisions. However, dishonest individuals can destabilize such communications systems. An open question is whether cognitive mechanisms, such as learning, can maintain the honesty of signals, thus favoring their evolutionary stability. We present evolutionary simulations where individuals can produce a signal proportional to their quality and learn along their lifetime the best response to the signals emitted by their peers. Our simulations replicate previous results where the handicap principle mediates the evolution of signals. In our simulations learning on the receiver side can mediate the evolution of signals of quality on the sender side. When the cost of the signal is proportional to the quality of the sender, all individuals in populations are honest signalers. In contrast to traditional models which predict the absence of signals when the cost is not proportional to the quality of the signaler, our model revealed that learning facilitates the evolution of a polymorphism in which populations comprise both honest and dishonest signalers. We argue that learning may have a role in the evolution and dynamics of a wide range of communication systems and more generally in behavioral responses.

evolutionary biology↗

bioRxiv2023

Flexibility of learning in complex worlds

Learning to adjust to changing environments is an important aspect of behavioral flexibility. Here we investigate the possible advantages of flexible learning rates in volatile environments, using learning simulations. We compare two established learning mechanisms, one with fixed learning rates and one with flexible rates that adjust to volatility. We study three types of ecological and experimental volatility: transitions from a simpler to a more complex foraging environment, reversal learning, and learning set formation. For transitions to a complex world, we use developing cleaner fish as an example, having more types of client fish to choose between as they become adult. There are other similar transitions in nature, such as migrating to a new and different habitat. Performance in reversal learning and in learning set formation are commonly used experimental measures of behavioral flexibility. Concerning transitions to a complex world, we show that both fixed and flexible learning rates perform well, losing only a small proportion of available rewards in the period after a transition, but flexible rates perform better than fixed. For reversal learning, flexible rates improve the performance with each successive reversal, because of increasing learning rates, but this does not happen for fixed rates. For learning set formation, we find no improvement in performance with successive shifts to new stimuli to discriminate for either flexible or fixed learning rates. Flexible learning rates might thus explain increasing performance in reversal learning, but not in learning set formation. We discuss our results in relation to current ideas about behavioral flexibility.

animal behavior and cognition↗

bioRxiv2022

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

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.

animal behavior and cognition↗