Norm reinforcement, not conformity or environmental factors, is predicted to sustain cultural variation

Abstract

The maintenance of cross-cultural variation and arbitrary traditions in human populations is a key question in cultural evolution. Conformist transmission, the tendency to follow the majority, was previously considered central to this phenomenon. However, recent theory indicates that cognitive biases can greatly reduce its ability to maintain traditions. Therefore, we expanded prior models to investigate two other ways that cultural variation can be sustained: payoff-biased transmission and norm reinforcement. Our findings predict that both payoff-biased transmission and reinforcement can enhance conformist transmission's ability to maintain traditions. However, payoff-biased transmission can only sustain cultural variation if it is functionally related to environmental factors. In contrast, norm reinforcement readily generates and maintains arbitrary cultural variation. Furthermore, reinforcement results in path-dependent cultural dynamics, meaning that historical traditions influence current practices, even though group behaviours have changed. We conclude that environmental variation probably plays a role in functional cultural traditions, but arbitrary cultural variation is more plausibly due to the reinforcement of norm compliance.

Keywords: Cultural evolution; conformist transmission; norm reinforcement; punishment; tradition stability.

Graphical abstract

Figure 1.

Schematic representation of the payoff-biased copying and environmental variation model. Note that group behavioural distributions are updated based on the mean of the posterior with an assumed variance of 1. See the main text for the exact formulation of each step.

Figure 2.

The figure illustrates the equilibrium mean value or behaviour (u) reached based on the strength of the fitness landscape (N/v_f) and the influence of the prior (1/v_p), where N is the number of observations, v_f is the variance in fitness and v_p represents the variance in the prior. The equilibrium value (u) consistently lies between u_p and u_f. The colour coding in the figure indicates the closeness of the equilibrium value (u) to u_p and u_f, with u_p shown in dark red and u_f in white. When the fitness landscape is weak, i.e. n/v_f is low, its ability to influence decision-making is limited, leading to equilibria that are largely determined by the prior, thus moving u closer to u_p. This situation is akin to a world with only conformist transmission and biased priors, and as outlined by Morgan and Thompson (2020), priors dominate in this situation. On the other hand, when the prior is very weak, i.e. v_p is high, the fitness landscape significantly affects the equilibrium value in the population, resulting in u approximating u_f.

Figure 3.

Schematic representation of the norm reinforcement model. See the main text for the exact formulation of each step.

Figure 4.

Visualisations of the model's updating process. All panels show the prior in black. (a) The behavioural distribution (solid line), posterior distribution (dashed line) and noisy posterior distribution (dot–dash–dotted line) for one of two groups (blue and green) across one timestep (the fitness function is excluded to reduce clutter). It is noteworthy that the blue population begins with a bimodal distribution, which, over time, will diminish, resulting in a convergence towards a normal distribution. However, in this particular timestep, both the posterior and the smoothed posterior retain this bimodal characteristic, which the group will adopt as their behavioural distribution in the next timestep. (b) The behaviour distributions (solid lines) and fitness functions (dotted lines) for three groups (blue, green and yellow). With more than two groups, some groups end up ‘sandwiched’ between others. (c) The behaviour distributions (solid lines) and noisy posterior distributions (dot–dash–dotted lines) for three groups. Here the prior is strong enough to pull all groups towards each other, for this timestep at least.

Figure 5.

(a) Cultural evolution of two groups, assuming u_p = 0, v_p = 5 and v = 1. Note that at equilibrium the two groups are symmetrically distributed around a mean value of 0 which is the most likely prior value (u_p = 0). Note also that the group represented by the blue line crosses the peak of the prior in order to ‘get away’ from the other group. (b) Cultural evolution of five groups, assuming u_p = 0, v_p = 5 and v = 1. The equilibrium distribution remains symmetrical. Note that the yellow group initially moves away from more plausible prior values in order to distance itself from the green group. (c) Cultural evolution of 10 groups, assuming u_p = 0, v_p = 5 and v = 1. At equilibrium, groups near the peak are more tightly clustered than those on the extremities. This is for two reasons: (1) groups near the peak of the prior are surrounded by other groups that causes a cultural ‘compression’; and (2) for groups on the extremities the prior is increasingly flat and so it exerts less pressure on their equilibrium. (d–f) The equilibrium variation between the mean values of each group (i.e. between-group variation), is indicated by the colour gradient from red to yellow, with yellow signifying high variation and red signifying low or none. This variation is a function of the prior variation (v_p) and of the variation within each group (v), assuming n =  2, 5 and 10, respectively. For each heatmap, the term ‘variation’ denotes the standard deviation between groups rather than the variance. Additionally, in (d), the equilibrium between-group variation is multiplied by 1.5 to enhance the gradient's visibility, making it easier to identify. This visual representation of the system's equilibrium states demonstrates that higher prior variation (v_p) tends to support the preservation of between-group variation, whereas lower values facilitate the convergence of groups.