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Network Analysis in AP

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An Introduction to Artificial Psychology

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Abstract

Further examples of network analysis using the directed graphs introduced in Chap. 4 are given. These networks are graphs showing the degree of relationship between variables. Given the complexity of relationships between concepts, network models are multivariate and often highly dimensional, so there is often a need to reduce the number of variables using techniques such as exploratory factor analysis and LASSO, which uses a tuning parameter specifying the threshold for the degree of removal of variables. New aspects of networks introduced in this chapter include Markov random fields, which are used to estimate the networks, the betweenness index to show the location of “hubs” in the network, clustering of nodes, and assessment of model fit and robustness using unbiased methods such as bootstrapping. One application using the connectome measuring the degree of connectivity between neurons and regions in an individual’s brain is introduced together with the size of such systems (microscopic, macroscopic) and types of connectivity. Independent component analysis can look at changes in brain networks over time. A worked example fitting and using the aspects of networks discussed earlier in the chapter to interpret the results is presented using R functions at the end of the chapter.

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Notes

  1. 1.

    Paul Broca

  2. 2.

    Carl Wernicke

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Authors and Affiliations

  1. Tarbiat Modares University, Tehran, Iran

    Hojjatollah Farahani, Parviz Azadfallah, Forough Esrafilian & Sara Saljoughi

  2. University of Kragujevac, Kragujevac, Serbia

    Marija Blagojević

  3. University of Cambridge, Cambridge, UK

    Peter Watson

Authors
  1. Hojjatollah Farahani
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  2. Marija Blagojević
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  3. Parviz Azadfallah
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  4. Peter Watson
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  5. Forough Esrafilian
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  6. Sara Saljoughi
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Farahani, H., Blagojević, M., Azadfallah, P., Watson, P., Esrafilian, F., Saljoughi, S. (2023). Network Analysis in AP. In: An Introduction to Artificial Psychology. Springer, Cham. https://doi.org/10.1007/978-3-031-31172-7_5

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