Several phenomena in animal learning seem to call for evolutionary explanations, such as patterns of what animals learn and do not learn. While several models consider how evolution should influence learning, we have very little data testing these models. Theorists agree that environmental change is a central factor in the evolution of learning. We describe a mathematical model and an experiment, testing two components of change: reliability of experience and predictability of the best action. Using replicate populations of Drosophila we varied statistical patterns of change across 30 generations. Our results provide the first experimental demonstration that some types of environmental change favour learning while others select against it, giving the first experimental support for a more nuanced interpretation of the selective factors influencing the evolution of learning.

Animals learn some things more easily than others. To explain this so-called prepared learning, investigators commonly appeal to the evolutionary history of stimulus-consequence relationships experienced by a population or species. We offer a simple model that formalizes this long-standing hypothesis. The key variable in our model is the statistical reliability of the association between stimulus, action, and consequence. We use experimental evolution to test this hypothesis in populations of Drosophila. We systematically manipulated the reliability of two types of experience (the pairing of the aversive chemical quinine with color or with odor). Following 40 generations of evolution, data from learning assays support our basic prediction: Changes in learning abilities track the reliability of associations during a population's selective history. In populations where, for example, quinine-color pairings were unreliable but quinine-odor pairings were reliable, we find increased sensitivity to learning the quinine-odor experience and reduced sensitivity to learning quinine-color. To the best of our knowledge this is the first experimental demonstration of the evolution of prepared learning.

Many animals, including insects, make decisions using both personally gathered information and social information derived from the behavior of other, usually conspecific, individuals [1]. Moreover, animals adjust use of social versus personal information appropriately under a variety of experimental conditions [2-5]. An important factor in how information is used is the information's reliability, that is, how consistently the information is correlated with something of relevance in the environment [6]. The reliability of information determines which signals should be attended to during communication [6-9], which types of stimuli animals should learn about, and even whether learning should evolve [10, 11]. Here, we show that bumble bees (Bombus impatiens) account for the reliability of personally acquired information (which flower color was previously associated with reward) and social information (which flowers are chosen by other bees) in making foraging decisions; however, the two types of information are not treated equally. Bees prefer to use social information if it predicts a reward at all, but if social information becomes entirely unreliable, flower color will be used instead. This greater sensitivity to the reliability of social information, and avoidance of conspecifics in some cases, may reflect the specific ecological circumstances of bee foraging. Overall, the bees' ability to make decisions based on both personally acquired and socially derived information, and the relative reliability of both, demonstrates a new level of sophistication and flexibility in animal, particularly insect, decision-making.

Learning is a fundamental mechanism in the behavior of animals. Theorists have long proposed that learning is an adaptation to environmental change, but change itself can present a logical paradox in that change can select both for and against learning. One way to resolve this paradox is through separating change into different components, such as the reliability of stimuli that can be used as cues for behavior, and the certainty with which those cues predict the best behavior to employ. Simple models using these components of change can be successfully applied through experimental evolution to directly test hypotheses of when learning and innate preference will each be favored evolutionarily, as well as when prepared learning will evolve. Costs of learning and, in particular, economic costs such as opportunity costs, should influence when learning will be favored. Although experimental evolution of learning can be difficult in practice, the benefits of the approach far outweigh difficulties. Future studies might approach the role of opportunity costs and how reliability and certainty fully interact to influence the evolution of learning.

Foraging in a variable environment presents a classic problem of decision making with incomplete information. Animals must track the changing environment, remember the best options and make choices accordingly. While several experimental studies have explored the idea that sampling behavior reflects the amount of environmental change, we take the next logical step in asking how change influences memory. We explore the hypothesis that memory length should be tied to the ecological relevance and the value of the information learned, and that environmental change is a key determinant of the value of memory. We use a dynamic programming model to confirm our predictions and then test memory length in a factorial experiment. In our experimental situation we manipulate rates of change in a simple foraging task for blue jays over a 36 hour period. After jays experienced an experimentally determined change regime, we tested them at a range of retention intervals, from 1 to 72 hours. Manipulated rates of change influenced learning and sampling rates: subjects sampled more and learned more quickly in the high change condition. Tests of retention revealed significant interactions between retention interval and the experienced rate of change. We observed a striking and surprising difference between the high and low change treatments at the 24 hour retention interval. In agreement with earlier work we find that a circadian retention interval is special, but we find that the extent of this ‘specialness’ depends on the subject’s prior experience of environmental change. Specifically, experienced rates of change seem to influence how subjects balance recent information against past experience in a way that interacts with the passage of time.

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Memory is a fundamental component of learning, a process by which individuals alter their behavior through experience. Although memory most likely has explicit costs such as synaptic maintenance and metabolic demands, there are also implicit costs to memory, in particular, the use of information that is no longer appropriate or is incorrect. Specifically, the period of retrievability for memories, or ''memory window,'' should be sensitive to the rate of environmental change of information stored in memory. Much empirical data suggest that memory length-this period of retrievability-changes with both the age and state of the individual. Here, we use a dynamic programming approach to examine how optimal memory retrieval might change within the lifetime of the individual learner. We find that optimal memory length varies with both age and state (e.g., energy reserves) of the organism and that features of the environment determine how this change in memory occurs. In our model, retrieval decreases as the environment becomes unreliable but roughly increases with the cost of living. Cost of living interacts with the state of the organism: with high cost of living, an organism in a very poor state should have a long memory length, but an organism in a very good state with low costs of living should have a short memory length. Finally, we find there are circumstances where it is optimal for memory retrieval to decline toward the end of the lifetime. Because this framework does not incorporate inevitable degradation of neural mechanisms, this result implies that memory loss with age might actually be adaptive.

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