A Small-World Mind: Towards a General Computational Principle of Social Cognition across Contexts

People describe social inferences using hundreds of words. How do people mentally represent these inferences? A long-standing tradition suggests that social inferences are underpinned by a small number of dimensions (e.g., warmth, competence). Another long-standing perspective argues they are more complex and require many dimensions to capture – an idea that has regained support by recent work using naturalistic designs. Motivated by this debate, we propose a network theory explaining how social inferences are mentally structured and generated in response to social information. Specifically, we propose that mental representations of social inferences form a small-world network with two principles: growth – the network expands by adding inference concepts, and preferential attachment – new concepts preferentially connect to well-connected ones. This representation is scalable while remaining cognitively parsimonious. We discuss the plausibility of this account based on existing social, developmental, and neurological findings. Our network theory accounts for classic phenomena—halo effects and psychological dimensions—as emergent network dynamics. Specifically, in small-world networks, nodes (social inferences) are connected with only a few steps from one another, so initial activation in a small set of nodes from constrained stimuli propagates broadly, producing synchronous covariation that mimics halo effects or low-dimensional structures. Dimensionality of activation gradually increases with input complexity. This network account reveals that established psychological phenomena may emerge from computations performed on mental representations in response to specific contextual inputs. By reconciling seemingly contradictory findings, our theory advances understanding of how the mind efficiently organizes vast social knowledge while adapting to environmental complexity.