Mechanisms of collective learning: how can animal groups improve collective performance when repeating a task?

Abstract

Learning is ubiquitous in animals: individuals can use their experience to fine-tune behaviour and thus to better adapt to the environment during their lifetime. Observations have accumulated that, at the collective level, groups can also use their experience to improve collective performance. Yet, despite apparent simplicity, the links between individual learning capacities and a collective's performance can be extremely complex. Here we propose a centralized and broadly applicable framework to begin classifying this complexity. Focusing principally on groups with stable composition, we first identify three distinct ways through which groups can improve their collective performance when repeating a task: each member learning to better solve the task on its own, members learning about each other to better respond to one another and members learning to improve their complementarity. We show through selected empirical examples, simulations and theoretical treatments that these three categories identify distinct mechanisms with distinct consequences and predictions. These mechanisms extend well beyond current social learning and collective decision-making theories in explaining collective learning. Finally, our approach, definitions and categories help generate new empirical and theoretical research avenues, including charting the expected distribution of collective learning capacities across taxa and its links to social stability and evolution. This article is part of a discussion meeting issue 'Collective behaviour through time'.

Keywords: collective decision-making; collective learning; evolutionarily stable strategies; individual learning; social learning; social stability.

Figure 1.

‘Collective learning’, as we define it here, occurs when collective performance at a task (i.e. a measure integrating the behaviour of multiple individuals) changes consistently (usually improving members' fitness) when the group repeats a task (i.e. increases its experience of it). Clear experimental examples include quicker colony emigration to a new nest site after repeated nest destructions in Temnothorax ants [9], the development of straighter collective homing routes in pigeons released multiple times from the same site [10] and more accurate collective choice of a predator-free arm in a y-maze in guppies [11]. (Online version in colour.)

Figure 2.

Schematic representation of three distinct but non-mutually exclusive mechanisms through which groups could increase collective performance through experience, illustrated with the repeated collective task of not losing a member to a predator encounter. (a) Individuals may each progressively learn to recognize/detect predators and thus quickly get to safety; (b) individuals may learn to detect and reduce risky behaviours from other members to ensure they all get to safety, and/or they may learn to attend to and correctly interpret warning calls; (c) individuals may progressively specialize into subtasks (e.g. predator versus social vigilance) and thus avoid interference and/or increase complementarity between individuals with different specializations. (Online version in colour.)

Figure 3.

Schematic representations of non-mutually exclusive constraints on collective learning capacities which might help predict its distribution in animal groups: (a) the individual cognitive processing of information may be more challenging when several individuals, rather than only a single individual, contribute to a task; (b) what individual members learn and/or know does not necessarily influence the collective decision; (c) conditions for the emergence of collective learning need to be evolutionarily stable strategies, successfully traded off against other biological functions, to persist across generations. (Online version in colour.)