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Author (up) Marchant, N.; Canessa, E.; Chaigneau, S.E. doi  openurl
  Title An adaptive linear filter model of procedural category learning Type
  Year 2022 Publication Cognitive Processing Abbreviated Journal Cogn. Process.  
  Volume 23 Issue 3 Pages 393-405  
  Keywords Category learning; Procedural categorization; Adaptive filter; Mathematical modeling  
  Abstract We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1612-4782 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000791062800001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1567  
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