Group impressions are dynamic configurations. The tensor product model (TPM), a connectionist model of memory and learning, is used to describe the process of group impression formation and change, emphasizing the structured and contextualized nature of group impressions and the dynamic evolution of group impressions over time. TPM is first shown to be consistent with algebraic models of social judgment (the weighted averaging model; N. Anderson, 1981) and exemplar-based social category learning (the context model; E. R. Smith & M. A. Zárate, 1992), providing a theoretical reduction of the algebraic models to the present connectionist framework. TPM is then shown to describe a common process that underlies both formation and change of group impressions despite the often-made assumption that they constitute different psychological processes. In particular, various time-dependent properties of both group impression formation (e.g., time variability, response dependency, and order effects in impression judgments) and change (e.g., stereotype change and group accentuation) are explained, demonstrating a hidden unity beneath the diverse array of empirical findings. Implications of the model for conceptualizing stereotype formation and change are discussed.

Using simulation, we contrast global network structures-in particular, small world properties-with the local patterning that generates the network. We show how to simulate Markov graph distributions based on assumptions about simple local social processes. We examine the resulting global structures against appropriate Bernoulli graph distributions and provide examples of stochastic global "worlds," including small worlds, long path worlds, and nonclustered worlds with many four-cycles. In light of these results we suggest a locally specified social process that produces small world properties. In examining movement from structure to randomness, parameter scaling produces a phase transition at a "temperature" where regular structures "melt" into stochastically based counterparts. We provide examples of "frozen" structures, including "caveman" graphs, bipartite structures, and cyclic structures.

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