Biology subjects

Chimento, M.

Publications and source records attributed to Chimento, M..

4 recordsLinked to original sources

bioRxiv2024

The contribution of movement to social network structure and spreading dynamics under simple and complex transmission

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here we examine the effect of the social-spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic, and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity, and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but-- importantly--the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules.

animal behavior and cognition↗

bioRxiv2022

Optimal population turnover regimes for cultural evolution depend on network size, density and behavioral transmissibility

A change to a populations social network is a change to the substrate of cultural transmission, affecting behavioral diversity and adaptive cultural evolution. While features of network structure such as population size and density have been well studied, less is understood about the influence of social processes such as population turnover-- or the repeated replacement of individuals. Experimental data has led to the hypothesis that naive learners can drive cultural evolution by being better samplers, although this hypothesis has only been expressed verbally. We conduct a formal exploration of this hypothesis using a generative model that concurrently simulates its two key ingredients: social transmission and reinforcement learning. We explore how variation in turnover influences changes in the distributions of cultural behaviors over long and short time-scales. We simulate competition between a high and low reward behavior, while varying turnover magnitude and tempo. We find optimal turnover regimes that amplify the production of higher reward behaviors. We also find that these optimal regimes result in a new population composition, where fewer agents which know both behaviors, and more agents know only the high reward behavior. These two effects depend on network size, density, behavioral transmissibility, and characteristics of the learners. Our model provides formal theoretical support for, and predictions about, the hypothesis that naive learners can shape cultural change through their enhanced sampling ability, identified by previous experimental studies. By moving from experimental data to theory, we illuminate an under-discussed generative process arising from an interaction between social dynamics and learning that can lead to changes in cultural behavior.

animal behavior and cognition↗

bioRxiv2021

Cultural diffusion dynamics depend on behavioural production rules

Culture is an outcome of both the acquisition of knowledge about behaviour through social transmission, and its subsequent production by individuals. Acquisition and production are often discussed interchangeably or modeled separately, yet to date, no study has accounted for both processes and explored their interaction. We present a generative model that integrates the two to explore how variation in production rules might shape cultural diffusion dynamics. Agents make behavioural choices that change as they learn from their productions. Their repertoires also change over time, and the social transmission of behaviours depends on their frequency. We diffuse a novel behaviour through social networks across a large parameter space to demonstrate how individual-level behavioural production rules influence population-level diffusion dynamics. We then investigate how linking transmission and production might affect the performance of two commonly used inferential models for social learning; Network-based Diffusion Analysis, and Experienced Weighted Attraction models. Clarifying the distinction between acquisition and production yields predictions for how production influences diffusion that are generalisable across species, and has consequences for how inferential methods are applied to empirical data. Our model illuminates the differences between social learning and social influence, demonstrates the overlooked role of reinforcement learning in cultural diffusions, and allows for clearer discussions about social learning strategies.

animal behavior and cognition↗

bioRxiv2020

Social network architecture and the tempo of cumulative cultural evolution

The ability to build upon previous knowledge--cumulative cultural evolution--is a hallmark of human societies. While cumulative cultural evolution depends on the interaction between social systems, cognition and the environment, there is increasing evidence that cumulative cultural evolution is facilitated by larger and more structured societies. However, such effects may be interlinked with patterns of social wiring, thus the relative importance of social network architecture as an additional factor shaping cumulative cultural evolution remains unclear. By simulating innovation and diffusion of cultural traits in populations with stereotyped social structures, we disentangle the relative contributions of network architecture from those of population size and connectivity. We demonstrate that while more structured networks, such as those found in multilevel societies, can promote the recombination of cultural traits into high-value products, they also hinder spread and make products more likely to go extinct. We find that transmission mechanisms are therefore critical in determining the outcomes of cumulative cultural evolution. Our results highlight the complex interaction between population size, structure and transmission mechanisms, with important implications for future research.

evolutionary biology↗