We use the powerful techniques of experimental evolution to directly test hypotheses about how patterns of change and reliability in the environment and of stimuli will influence the evolution of plasticity, in the form of learning, and in fixed behaviors, in the form of innate bias or preference. Reliability of types of stimuli can also predict when prepared learning will evolve.
Experimental evolution is just a start, and we maintain some subsets of populations which we use to test hypotheses about costs of learning, trade-offs of life history and of economics, generalization of learning and preference, mechanisms of memory, and differences in gene expression and genome.
While much of what we do is driven by theory, we are also keenly interested in the cognitive ecology of bees. Here we use observations and knowledge from natural history, combined with theory on how animals deal with changing environments, to make predictions about the cognitive differences we will find in natural populations. We have been doing this work with North American bumble bees, orchid bees, and sweat bees.
We generally think of learning as a means to track change in the environment. How will economics and change favor tracking and which conditions favor constant choice, like the flower constancy we see in bumblebees? How does change in the environment affect how animals weight different types of information? We are using both bumble bees and flies to address some of these questions. With bees we are applying classic tracking models from foraging theory to look at how change, innate bias, and other factors affect when new information will be acquired and applied. We are also looking at decision rules and theories from human economics to predict patterns of behavior in foraging bees.
Animals can use information gained through their own trial and error learning, but they can also take short-cuts and gain information from other individuals. We are working with bumble bees and flies to test how aspects of change in the environment affect social learning versus individual learning, social information use in complex environments, and decision making in the context of social settings.
There is a general consensus that remembering everything forever is not ideal: there are physiological costs as well as costs with potential interference with the recall of memories. We are looking at memory length as an evolutionarily adaptive behavior, specifically asking: when is it optimal to forget? We have published models and previous work with blue jays and pinyon jays. Current work in the lab is using bees and flies, comparing forgetting curves and other aspects of memory for different sources of information, and how this interacts with certainty about the environment.