Rates of ecological knowledge learning in Pemba, Tanzania: Implications for childhood evolution

Abstract

Humans live in diverse, complex niches where survival and reproduction are conditional on the acquisition of knowledge. Humans also have long childhoods, spending more than a decade before they become net producers. Whether the time needed to learn has been a selective force in the evolution of long human childhood is unclear, because there is little comparative data on the growth of ecological knowledge throughout childhood. We measured ecological knowledge at different ages in Pemba, Zanzibar (Tanzania), interviewing 93 children and teenagers between 4 and 26 years. We developed Bayesian latent-trait models to estimate individual knowledge and its association with age, activities, household family structure and education. In the studied population, children learn during the whole pre-reproductive period, but at varying rates, with the fastest increases in young children. Sex differences appear during middle childhood and are mediated by participation in different activities. In addition to providing a detailed empirical investigation of the relationship between knowledge acquisition and childhood, this study develops and documents computational improvements to the modelling of knowledge development.

Keywords: Ecological knowledge; Item Response Theory; childhood evolution; sex differences.

Graphical abstract

Figure 1.

Growth curves for different body tissues in dashed grey lines, adapted from Bogin (1997), with overlaying possible growth curves for knowledge, continuous lines. We could expect knowledge to follow the trajectory of the green curve, if learning rates depended exclusively on brain development, for example if learning was to begin only after the brain reached a certain volume, but could then proceed quite fast, in a ‘brain-threshold model’. On the contrary, if knowledge was a pre-requisite for reproduction, and could be acquired in a short period of time, it could simply increase before sexual maturity, as described by the purple curve, a ‘learn-to-reproduce’ hypothesis. Finally, the blue line shows a slow increase in knowledge with age, potentially continuing even after sexual maturity, which is consistent with the hypothesis that filling one's brain requires time.

Figure 2.

Pemba lies offshore Tanzania. Data were collected in the shehia of Kifundi, coloured in green, which is still partially covered by the forest of Ngezi.

Figure 3.

Direct acyclic graph describing relationships between analysed variables. Note that age does not ‘cause’ knowledge directly. Rather, the effect of age is mediated by other variables, including activities practised and school attendance, but also other unmeasured events and developmental changes. In the models, as we introduce activities or schooling, age remains as a proxy for these unmeasured factors, represented by the circled U in the graph. Moreover, we do not assume a direct effect of sex on knowledge, as we do not expect innate differences between sexes in knowledge acquisition. On the contrary, the sexes differ in the frequency with which they engage in activities, although we do not discuss whether this has an innate or cultural origin.

Figure 4.

The points in the figure represent individual ecological knowledge K_i as measured by the item response theory model, colour coded for sex. The average knowledge for an individual of a certain age and sex, as predicted by model 1 in equation (3), is represented by the continuous lines, where the darker one is the mean of the distribution and the lighter lines represent 150 draws from the posterior.

Figure 5.

Individual ecological knowledge K_i and predicted values by age and sex in three dimensions.

Figure 6.

The plot describes how much advantage, in terms of time, an individual can gain from practising an activity with respect to undifferentiated ecological knowledge. The x-axis represents the years gained, i.e. how much earlier (positive values) or later (negative ones) an individual practising each activity would reach the same ecological knowledge of a 20-year-old individual not practising that activity, all else kept constant. Activities such as shellfish collection and bird hunting could give up to 10 years’ advantage in terms of ecological knowledge to practising individuals – although most people would gain two to five years. On the contrary, doing agricultural work (bottom row) seems to slow down learning. Notice that the tail of the distributions for the bottom five activities extends outside of the limit of the plot, with the fifth or smaller percentile below −20. Activities description: seashells – extracting seashells from a sandy bottom; livestock – herding and watering cattle or goats; birds – hunting birds with various traps or slingshots; fishing – fishing using small-to-medium-sized boats, often in small groups; game – hunting larger game with the help of dogs, often targeting crop predators such as monkeys; algae – farming seaweed to sell; diving – advanced fishing, with masks and sometimes air tanks; cloves – paid labour in picking clove buds from trees; household – chores such as water collecting or cooking; agriculture – participating on work in family fields.