A Gaussian Mixture Model Approach to Classifying Response Types

  • Owen E. ParsonsEmail author
Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)


Visual perception is influenced by prior experiences and learned expectations. One example of this is the ability to rapidly resume visual search after an interruption to the stimuli. The occurrence of this phenomenon within an interrupted search task has been referred to as rapid resumption. Previous attempts to quantify individual differences in the extent to which rapid resumption occurs across participants relied on using an operationally defined cutoff criteria to classify response types within the task. This approach is potentially limited in its accuracy and could be improved by turning to data-driven alternatives for classifying response types. In this chapter, I present an alternative approach to classifying participant responses on the interrupted search task by fitting a Gaussian mixture model to response distributions. The parameter estimates obtained from fitting this model can then be used in a naïve Bayesian classifier to allow for probabilistic classification of individual responses. The theoretical basis and practical application of this approach are covered, detailing the use of the Expectation-Maximisation algorithm to estimate the parameters of the Gaussian mixture model as well as applying a naïve classifier to data and interpreting the results.


Visual search Interrupted search Rapid resumption Prior expectations Attention Gaussian mixture models Expectation-maximisation 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of CambridgeCambridgeUK

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