Environmental effects on social learning and its feedback on individual and group level interactions

UoM administered thesis: Phd

  • Authors:
  • Marco Smolla


Through social learning, animals acquire information from others, such as skills and knowledge about the environment. High fidelity transmission of locally adaptive information can lead to population-specific traits, or cultural traits, which are fundamental to the emergence of culture. Despite social learning being widespread in the animal kingdom, culture is rare in nature. This thesis investigates the evolution, ecology, and dynamics of social learning, to increase our understanding why species differ in their ability to generate and accumulate cultural traits, and ultimately how complex human culture emerged. Chapter 2 introduces a novel computational model that explicitly incorporates competition into the social learning context. The model predicts that social learning is most adaptive where resources are unevenly distributed and stable through time, even if individuals compete for limited resources. The model provides an explanation for reports of animals disregarding social information, even if it is available. Testing these predictions Chapter 3 presents a bumblebee foraging experiment. The results support the theoretical predictions, showing that foragers use social information to find rewarding flowers, even if social cues indicate competition. Chapter 4 further examines the trade-off between access to social information and competition. Individuals that are central in a learning network have more opportunities to acquire information from others, but also face an increased likelihood to engage in competition. The results of this model suggest that across different learning contexts centrality is only beneficial for dominant individuals because dominance can mitigate the effect of competition. This also shows that individual phenotypic differences affect the utility of social information. Chapter 5 uses a dynamic network model approach to tests whether these differences modulate the structure of learning networks and by extension of the population. The model shows that this is the case and that where social learning is favoured by the environment networks are more structured. Chapter 6, studies the drivers behind individual differences in social learning. The chapter focusses on reports of sex differences in social information use and finds that they can be explained by differences in risk taking behaviour. The results highlight the importance of the feedback between learning individuals, and how this shapes social learning dynamics on an individual as well as on a population level.


Original languageEnglish
Awarding Institution
Award date1 Aug 2017