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Modeling psychological stereotypes in self-organizing maps

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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

Cognitive psychology defines stereotypes as categories associating information according to perceived similarity rather than according to logical criteria [1]. Self-organizing maps (SOMs) [2] are used as models of human perception in an experiment on stereotype formation. Two groups of SOMs were trained with coded newspaper information, one with information from a conservative newspaper, the other with information from a progressive one. The SOMs performed well in mapping the major differences between the newspapers (r=0.99). Further, each group generated distinct associations of dissimilar information which could be interpreted as ‘artificial stereotypes’. The SOM is suggested as a research tool for cognitive research on stereotype change.

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References

  1. Fiske, S. T. and Taylor, S. E. Taylor: Social Cognition. Second Edition. McGraw-Hill, New York 1991.

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  2. Kohonen, T.: Self-Organized Formation of Topology Correct Feature Maps. Biological Cybernetics, 34: 59–69, 1982.

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Authors and Affiliations

  1. University of Tübingen, Sand 13, D-72076, Tübingen, Germany

    Thomas Crämer, Josef Göppert & Wolfgang Rosenstiel

Authors
  1. Thomas Crämer
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  2. Josef Göppert
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  3. Wolfgang Rosenstiel
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Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Crämer, T., Göppert, J., Rosenstiel, W. (1996). Modeling psychological stereotypes in self-organizing maps. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_152

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  • DOI: https://doi.org/10.1007/3-540-61510-5_152

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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