No abstract

No abstract

Scrub jays are thought to use many tactics to protect their caches. For instance, they predominantly bury food far away from conspecifics, and if they must cache while being watched, they often re-cache their worms later, once they are in private. Two explanations have been offered for such observations, and they are intensely debated. First, the birds may reason about their competitors' mental states, with a ‘theory of mind’; alternatively, they may apply behavioral rules learned in daily life. Although this second hypothesis is cognitively simpler, it does seem to require a different, ad-hoc behavioral rule for every caching and re-caching pattern exhibited by the birds. Our new theory avoids this drawback by explaining a large variety of patterns as side-effects of stress and the resulting memory errors. Inspired by experimental data, we assume that re-caching is not motivated by a deliberate effort to safeguard specific caches from theft, but by a general desire to cache more. This desire is brought on by stress, which is determined by the presence and dominance of onlookers, and by unsuccessful recovery attempts. We study this theory in two experiments similar to those done with real birds with a kind of ‘virtual bird’, whose behavior depends on a set of basic assumptions about corvid cognition, and a well-established model of human memory. Our results show that the ‘virtual bird’ acts as the real birds did; its re-caching reflects whether it has been watched, how dominant its onlooker was, and how close to that onlooker it has cached. This happens even though it cannot attribute mental states, and it has only a single behavioral rule assumed to be previously learned. Thus, our simulations indicate that corvid re-caching can be explained without sophisticated social cognition. Given our specific predictions, our theory can easily be tested empirically.

Caching and recovery of food by corvids is well-studied, but some ambiguous results remain. To help clarify these, we built a computational cognitive model. It is inspired by similar models built for humans, and it assumes that memory strength depends on frequency and recency of use. We compared our model's behavior to that of real birds in previously published experiments. Our model successfully replicated the outcomes of two experiments on recovery behavior and two experiments on cache site choice. Our "virtual birds" reproduced declines in recovery accuracy across sessions, revisits to previously emptied cache sites, a lack of correlation between caching and recovery order, and a preference for caching in safe locations. The model also produced two new explanations. First, that Clark's nutcrackers may become less accurate as recovery progresses not because of differential memory for different cache sites, as was once assumed, but because of chance effects. And second, that Western scrub jays may choose their cache sites not on the basis of negative recovery experiences only, as was previously thought, but on the basis of positive recovery experiences instead. Alternatively, both "punishment" and "reward" may be playing a role. We conclude with a set of new insights, a testable prediction, and directions for future work.

Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the coverage test with which accuracy is assessed. We apply this method, which we call error-calibrated ABC, to a toy example and a realistic 14-parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic coverage test show that our approach improves estimation of parameter values and their credible intervals for both models.