Language is one of the most complex of human traits. There are many hypotheses about how it originated, what factors shaped its diversity, and what ongoing processes drive how it changes. We present the Causal Hypotheses in Evolutionary Linguistics Database (CHIELD, https://chield.excd.org/), a tool for expressing, exploring, and evaluating hypotheses. It allows researchers to integrate multiple theories into a coherent narrative, helping to design future research. We present design goals, a formal specification, and an implementation for this database. Source code is freely available for other fields to take advantage of this tool. Some initial results are presented, including identifying conflicts in theories about gossip and ritual, comparing hypotheses relating population size and morphological complexity, and an author relation network.

The distribution of cultural variants in a population is shaped by both neutral evolutionary dynamics and by selection pressures. The temporal dynamics of social network connectivity, that is, the order in which individuals in a population interact with each other, has been largely unexplored. In this paper, we investigate how, in a fully connected social network, connectivity dynamics, alone and in interaction with different cognitive biases, affect the evolution of cultural variants. Using agent‐based computer simulations, we manipulate population connectivity dynamics (early, mid, and late full‐population connectivity); content bias, or a preference for high‐quality variants; coordination bias, or whether agents tend to use self‐produced variants (egocentric bias), or to switch to variants observed in others (allocentric bias); and memory size, or the number of items that agents can store in their memory. We show that connectivity dynamics affect the time‐course of variant spread, with lower connectivity slowing down convergence of the population onto a single cultural variant. We also show that, compared to a neutral evolutionary model, content bias accelerates convergence and amplifies the effects of connectivity dynamics, while larger memory size and coordination bias, especially egocentric bias, slow down convergence. Furthermore, connectivity dynamics affect the frequency of high‐quality variants (adaptiveness), with late connectivity populations showing bursts of rapid change in adaptiveness followed by periods of relatively slower change, and early connectivity populations following a single‐peak evolutionary dynamic. We evaluate our simulations against existing data collected from previous experiments and show how our model reproduces the empirical patterns of convergence.

Individuals increasingly participate in online platforms where they copy, share and form they opinions. Social interactions in these platforms are mediated by digital institutions, which dictate algorithms that in turn affect how users form and evolve their opinions. In this work, we examine the conditions under which convergence on shared opinions can be obtained in a social network where connected agents repeatedly update their normalised cardinal preferences (i.e. value systems) under the influence of a non-constant reflexive signal (i.e. institution) that aggregates populations’ information using a proportional representation rule. We analyse the impact of institutions that aggregate (i) expressed opinions (i.e. opinion-aggregation institutions), and (ii) cardinal preferences (i.e. value-aggregation institutions). We find that, in certain regions of the parameter space, moderate institutional influence can lead to moderate consensus and strong institutional influence can lead to polarisation. In our randomised network, local coordination alone in the total absence of institutions does not lead to convergence on shared opinions, but very low levels of institutional influence are sufficient to generate a feedback loop that favours global conventions. We also show that opinion-aggregation may act as a catalyst for value change and convergence. When applied to digital institutions, we show that the best mechanism to avoid extremism is to increase the initial diversity of the value systems in the population.

In this paper we argue that ecological evolutionary developmental biology (ecoevo-devo) accounts of cognitive modernity are compatible with cultural evolution theories of language built upon iterated learning models. Cultural evolution models show that the emergence of near universal properties of language do not require the preexistence of strong specific constraints. Instead, the development of general abilities, unrelated to informational specificity, like the copying of complex signals and sharing of communicative intentions is required for cultural evolution to yield specific properties, such as language structure. We argue that eco-evo-devo provides the appropriate conceptual background to ground an account for the many interconnected genetic, environmental and developmental factors that facilitated the emergence of an organic system able to develop language through the iterated transmission of information. We use the concept of niche construction to connect evolutionary developmental accounts for sensory guided motor capacities and cultural evolution guided by iterated learning models. This integrated theoretical model aims to build bridges between biological and cultural approaches.

How does the order of individuals' interactions affect the emergence of shared conventions at the population level? The answer to this question is relevant for a number of fields, such as cultural evolution, linguistics, cognitive science or behavioral economics. In this study we investigate experimentally how two different network connectivity dynamics affect the evolution of the diversity of cultural variants of the communication system. We report an experiment in the lab in which participants engage in a Pictionary-like graphical communication task as members of a 4-participant micro-society, interacting in pairs with the other three members of the community across 4 rounds. The experiment has two main goals: First, to evaluate the effect of two network connectivity dynamics (early and late) on the evolution of the convergence of micro-societies on shared communicative conventions under controlled conditions. Second, to compare the predictions of the agent-based model described in a previous study (Segovia-Martín, Walker, Fay, & Tamariz, 2019) against experimental data, and calibrate the model to find the best-fitting parameter setting. Our experimental data shows that, as predicted by the model, an early connectivity dynamic increases convergence and a late connectivity dynamic slows down convergence. We found significant differences between conditions in round 3 and round 4. We estimate the best-fit parameter combination for the 96 data structures coded. Medium to high content bias, neutral to egocentric coordination bias and memory size of 3 rounds was associated with a better model fit. In the light of the model evaluation and the experiment outcome, we discuss the impact of our predictions on social influence research and possible factors that might help to improve model precision.