Collective minds: social network topology shapes collective cognition

Abstract

Human cognition is not solitary, it is shaped by collective learning and memory. Unlike swarms or herds, human social networks have diverse topologies, serving diverse modes of collective cognition and behaviour. Here, we review research that combines network structure with psychological and neural experiments and modelling to understand how the topology of social networks shapes collective cognition. First, we review graph-theoretical approaches to behavioural experiments on collective memory, belief propagation and problem solving. These results show that different topologies of communication networks synchronize or integrate knowledge differently, serving diverse collective goals. Second, we discuss neuroimaging studies showing that human brains encode the topology of one's larger social network and show similar neural patterns to neural patterns of our friends and community ties (e.g. when watching movies). Third, we discuss cognitive similarities between learning social and non-social topologies, e.g. in spatial and associative learning, as well as common brain regions involved in processing social and non-social topologies. Finally, we discuss recent machine learning approaches to collective communication and cooperation in multi-agent artificial networks. Combining network science with cognitive, neural and computational approaches empowers investigating how social structures shape collective cognition, which can in turn help design goal-directed social network topologies. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

The emergence of collective knowledge and cumulative culture in animals, humans and machines

“…They argue that by splitting the burden of cultural knowledge across individuals in this way, social ratcheting has become a key component of human collective intelligence. Such conclusions from these real-world investigations converge encouragingly with reports from various laboratory and modelling studies in this issue [ 98 , 99 , 105 , 106 , 108 , 159 , 160 ].…”

“…Recognizing this, but going far beyond it, articles in this issue address the significance of cases where collective knowledge exists only at the level of the collective, and not in any one individual. The partial or variant knowledge of different individuals that this implies may be distributed across a population in space, and/or over time, with subsequent combinations thence leading to innovations that can drive CCE [ 82 , 93 , 153 – 155 , 159 , 160 ]. Studies are progressively revealing that how these effects play out may be shaped by numerous interacting factors including the form of social structures and networks [ 93 , 159 , 160 ], relationships between individuals such as in degree of tolerance [ 153 ] or coordination [ 157 ], adaptive biases in model selection such as conformity [ 153 , 163 ], and the socio-cognitive capabilities of participant individuals, such as theory of mind [ 29 , 158 ] and inventiveness.…”

“…The partial or variant knowledge of different individuals that this implies may be distributed across a population in space, and/or over time, with subsequent combinations thence leading to innovations that can drive CCE [ 82 , 93 , 153 – 155 , 159 , 160 ]. Studies are progressively revealing that how these effects play out may be shaped by numerous interacting factors including the form of social structures and networks [ 93 , 159 , 160 ], relationships between individuals such as in degree of tolerance [ 153 ] or coordination [ 157 ], adaptive biases in model selection such as conformity [ 153 , 163 ], and the socio-cognitive capabilities of participant individuals, such as theory of mind [ 29 , 158 ] and inventiveness. The findings of the studies in this issue, alongside recent complementary explorations [ 91 , 162 , 164 ], provide a deepening understanding of the diverse manifestations of culture and its evolution in both natural and human-engineered contexts.…”

“…Mommenejad [ 160 ] provides complementary perspectives on how different ‘social network topologies' shape collective memory and social transmission, drawing together insights from behavioural and cognitive experiments, neuroimaging of people's representations of social networks, and the emergence of collective phenomena in multi-agent machine-learning networks, the latter linking with the robotics articles in the issue.…”

Social learning in swarm robotics

“…collective object transport, or search and rescue). Second, swarm density may completely change the swarm dynamics, even if the environment and task remained the same (see [ 32 ] for a discussion on the impact of social network topology on collective cognition).…”