Ecological correlates of social learning: problems and solutions for the comparative method

A reaction norm framework for the evolution of learning: how cumulative experience shapes phenotypic plasticity

Learning is a familiar process to most people, but it currently lacks a fully developed theoretical position within evolutionary biology. Learning (memory and forgetting) involves adjustments in behaviour in response to cumulative sequences of prior experiences or exposures to environmental cues. We therefore suggest that all forms of learning (and some similar biological phenomena in development, aging, acquired immunity and acclimation) can usefully be viewed as special cases of phenotypic plasticity, and formally modelled by expanding the concept of reaction norms to include additional environmental dimensions quantifying sequences of cumulative experience (learning) and the time delays between events (forgetting). Memory therefore represents just one of a number of different internal neurological, physiological, hormonal and anatomical ‘states’ that mediate the carry‐over effects of cumulative environmental experiences on phenotypes across different time periods. The mathematical and graphical conceptualisation of learning as plasticity within a reaction norm framework can easily accommodate a range of different ecological scenarios, closely linking statistical estimates with biological processes. Learning and non‐learning plasticity interact whenever cumulative prior experience causes a modification in the reaction norm (a) elevation [mean phenotype], (b) slope [responsiveness], (c) environmental estimate error [informational memory] and/or (d) phenotypic precision [skill acquisition]. Innovation and learning new contingencies in novel (laboratory) environments can also be accommodated within this approach. A common reaction norm approach should thus encourage productive cross‐fertilisation of ideas between traditional studies of learning and phenotypic plasticity. As an example, we model the evolution of plasticity with and without learning under different levels of environmental estimation error to show how learning works as a specific adaptation promoting phenotypic plasticity in temporally autocorrelated environments. Our reaction norm framework for learning and analogous biological processes provides a conceptual and mathematical structure aimed at usefully stimulating future theoretical and empirical investigations into the evolution of plasticity across a wider range of ecological contexts, while providing new interdisciplinary connections regarding learning mechanisms.

Components of change in the evolution of learning and unlearned preference

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.

Comprehension of human pointing gestures in young human-reared wolves (Canis lupus) and dogs (Canis familiaris)

Dogs have a remarkable skill to use human-given cues in object-choice tasks, but little is known to what extent their closest wild-living relative, the wolf can achieve this performance. In Study 1, we compared wolf and dog pups hand-reared individually and pet dogs of the same age in their readiness to form eye-contact with a human experimenter in an object-choice task and to follow her pointing gesture. The results showed that dogs already at 4 months of age use momentary distal pointing to find hidden food even without intensive early socialization. Wolf pups, on the contrary, do not attend to this subtle pointing. Accordingly in Studies 2 and 3, these wolves were tested longitudinally with this and four other (easier) human-given cues. This revealed that wolves socialized at a comparable level to dogs are able to use simple human-given cues spontaneously if the human's hand is close to the baited container (e.g. touching, proximal pointing). Study 4 showed that wolves can follow also momentary distal pointing similarly to dogs if they have received extensive formal training. Comparing the wolves to naïve pet dogs of the same age revealed that during several months of formal training wolves can reach the level of dogs in their success of following momentary distal pointing in parallel with improving their readiness to form eye-contact with a human experimenter. We assume that the high variability in the wolves' communicative behaviour might have provided a basis for selection during the course of domestication of the dog.