Cultural transmission in an ever-changing world: trial-and-error copying may be more robust than precise imitation

Abstract

Cultural transmission facilitates the spread of behaviours within social groups and may lead to the establishment of stable traditions in both human and non-human animals. The fidelity of transmission is frequently emphasized as a core component of cultural evolution and as a prerequisite for cumulative culture. Fidelity is often considered a synonym of precise copying of observed behaviours. However, while precise copying guarantees reliable transmission in an ideal static world, it may be vulnerable to realistic variability in the actual environment. Here, we argue that fidelity may be more naturally achieved when the social learning mechanisms incorporate trial-and-error; and that the robustness of social transmission is thereby increased. We employed a simple model to demonstrate how culture that is produced through exact copying is fragile in an (even slightly) noisy world. When incorporating a certain degree of trial-and-error, however, cultures are more readily formed in a stochastic environment and are less vulnerable to rare ecological changes. We suggest that considering trial-and-error learning as a stabilizing component of social transmission may provide insights into cultural evolution in a realistic, variable, world.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'.

Keywords: copying errors; cultural evolution; imitation; social learning; transmission fidelity; trial-and-error learning.

Figure 1.

Model value function and illustrated template-matching process. (a) The value function in Models 1 and 2 is a bell-curve encompassing an optimal behaviour and defined as (giving a value Z for a behaviour x). The yellow area depicts a range within which the performance of the behaviour is considered a successful skill performance. Vertical lines illustrate a process of social learning: red: demonstrator's actual performance; green: observer's copied template; grey (dotted): observer's trial-and-error attempts; grey (dashed): observer's successful attempt. (b) Illustration of the value function for Model 3. The value is a function of both the behaviour and the encountered environment and defined as (giving a value Z for a behaviour x and environment E).

Figure 2.

The effect of copying inaccuracy and trial-and-error on assimilation rates of the seeded innovation. Assimilation time (number of iterations until 95% of the population acquired the skill) for different combinations of σ_I (trial-and-error range) and σ_c (copy inaccuracy) in simulations of (a) Model 1: stochastic trial-and-error; (b) Model 2: trial-and-error with template updating and (c) Model 3: trial-and-error learning in a variable environment. Colours indicate the assimilation time. The white area depicts simulations in which the innovation did not reach the assimilation threshold after 500 iterations. The small circles and their respective regression lines present the fastest assimilation for each value of σ_c. (d) Assimilation time for different values of σ_I when σ_c = 0 in Model 3. Note that, in this model, some degree of trial-and-error produces the most efficient cultural transmission, even without any copying error.

Figure 3.

Simulations of Model 3 under different conditions. (a) Examples of four simulations of Model 3, with different combinations of σ_I and σ_c. (b) The effect of changes in environmental variance: examples of three simulations of Model 3, with different combinations of σ_I and σ_c, where the variance of the environmental factor increased sharply in iteration no. 250. Note how the top line shows a recovering population, the middle one a partially recovering population, and the lowest line a collapse of the cultural trait. (c) Simulations of Model 3 with different types of learners. Blue: learners that consider environmental variability (as defined in Model 3); red: learners that update their template as in Model 2 (and ignore environmental variability); yellow: a stochastic trial-and-error observer (as in Model 1).