From partners to populations: A hierarchical Bayesian account of coordination and convention

away to a smaller number of more general terms; in the fine and mixed conditions, however, agents become more confident of subordinate terms. Partners successfully learn to communicate First, we compare the model’s learning curves across context conditions (Fig. 10A). We focus on the coarse and fine conditions for simplicity, since this single comparison captures the core phenomena of interest. In a mixed-effects logic regression, we find that communicative accuracy steadily improves over time across all conditions, b = 0.72, z = 16.9, p < 0.001. However, accuracy also differed across conditions: adding a main effect of condition significantly improves model fit, χ2(2) = 9.6, p = 0.008. Accuracy is significantly higher in the coarse condition than the fine condition b = −0.71, z = 9.3, p < 0.001 and marginally higher than the mixed condition. Lexical conventions are shaped by context As an initial marker of context sensitivity, we examine the effective vocabulary sizes used by speakers in each condition. We operationalized this measure by counting the total number of unique words produced within each repetition block. This measure takes a value of 8 when a different word is consistently used for every object, and a value of 1 when exactly the same word is used for every object. In an mixed-effects regression model including intercepts and random effects of trial number for each simulated trajectory, we find an overall main effect of condition, with agents in the fine condition using significantly fewer words across all repetition blocks (m = 4.7 in coarse, m = 6.5 in fine, t = 4.5, p < 0.001). However, we also found a significant interaction: the effective vocabulary size gradually increased over time in the fine condition, while it stayed roughly constant in in the coarse condition, b = 0.18, t = 8.1, p < 0.001, see Fig. 10B.