Chapter

Social Learning and Connectionism

Frank Van Overwalle

in Associative Learning and Conditioning Theory

Published in print March 2011 | ISBN: 9780199735969
Published online May 2011 | e-ISBN: 9780199894529 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199735969.003.0104
Social Learning and Connectionism

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This chapter reviews evidence to demonstrate that many judgments and biases in social cognition can be understood from a connectionist perspective. A basic feature of connectionist modelling is that many social judgments are driven by basic associative learning processes, most often by an error-minimizing algorithm as illustrated in the delta learning algorithm. Two major emergent properties falling naturally out from this learning algorithm are acquisition (sample size effects) and competition (discounting and augmentation). These properties are unique to error minimizing algorithms like delta learning. Empirical evidence is reviewed showing that causal en dispositional attributions are strongly determined by these emergent properties. In addition, a number of simulations are reviewed to illustrate that many other social judgments and biases might result from such connectionist learning processes. These simulations include person impression formation, assimilation and contrast, illusory correlations in groups, subtyping of extreme dissidents, cognitive dissonance, attitude formation through persuasive communication, and recent findings of brain imaging research on person perception. The common theme in this chapter is that a single connectionist learning mechanism—the delta algorithm—is capable of producing emerging properties that explain a rich set of empirical data in social cognition.

Keywords: social connectionism; connectionist simulations; impression formation; group biases; attitude formation; social neuroscience

Chapter.  16341 words.  Illustrated.

Subjects: Cognitive Psychology

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