Group Dynamics
group dynamics
Prof Iain Couzin
Despite the fact that social transmission of information is vital to many group-living animals, the organizing principles governing the networks of interaction that give rise to collective properties of animal groups, remain poorly understood. The student will employ an integrated empirical and theoretical approach to investigate the relationship between individual computation (cognition at the level of the ‘nodes’ within the social network) and collective computation (computation arising from the structure of the social network). The challenge for individuals in groups is to be both robust to noise, and yet sensitive to meaningful (often small) changes in the physical or social environment, such as when a predator is present. There exist two, non mutually-exclusive, hypotheses for how individuals in groups could modulate the degree to which sensory input to the network is amplified; 1) it could be that individuals adjust internal state variable(s) (e.g. response threshold(s)), effectively adjusting the sensitivity of the “nodes” within the network to sensory input and/or 2) it could be that individuals change their spatial relationships with neighbors (such as by modulating density) such that it is changes in the structure and strength of connections in the network that modulates the information transfer capabilities, and thus collective responsiveness, of groups. Using schooling fish as a model system we will investigate these hypotheses under a range of highly controlled, ecologically-relevant scenarios that vary in terms of timescale and type of response, including during predator avoidance as well as the search for, and exploitation of, resources. We will employ technologies such as Bayesian inference and unsupervised learning techniques developed in computational neuroscience and machine learning to identify, reconstruct, and analyze the directed and time-varying sensory networks within groups, and to relate these to the functional networks of social influence. As in neuroscience, we care about stimulus-dependent, history-dependent discrete stochastic events, including burstiness, refractoriness and habituation and throughout we will seek to isolate principles that extend beyond the specificities of our system. For more information see: https://www.smartnets-etn.eu/collective-computation-in-large-animal-groups/
Unmotivated bias
In this talk, I will explore how social affective biases arise even in the absence of motivational factors as an emergent outcome of the basic structure of social learning. In several studies, we found that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes. Additional cognitive modeling discovered that approach and avoidance behavior based on biased beliefs not only influences the evaluative (positive or negative) impressions of group members, but also shapes the depth of the cognitive representations available to learn about individuals. In other words, people have richer cognitive representations of members of groups that are not avoided, akin to individualized vs group level categories. I will end presenting a series of multi-agent reinforcement learning simulations that demonstrate the emergence of these social-structural feedback loops in the development and maintenance of affective biases.
Three levels of variability in the collective behavior of locusts
Many aspects of collective behavior depend on interactions between conspecifics. This is especially true for the collective motion of locusts, which swarm in millions while maintaining synchrony among individuals. However, whether locusts share and maintain the same socio-behavioral patterns – between groups, individuals and situations – remains an open question. Studying marching locusts under lab conditions, we found that (1) different groups behave differently; (2) locusts within a group homogenize their behavior; and (3) individuals have different socio-behavioral tendencies and context-dependent states. These variability levels suggest that behavioral differences within and among individuals exist, affect others, and shape the collective behavior of the entire group.
The influence of prenatal CBD exposure on group dynamics and social behaviors in adult offspring
FENS Forum 2024