Computational Models of Classical Conditioning - A Qualitative Evaluation and Comparison

Eduardo Alonso, Pavandeep Sahota, Esther Mondragón

2014

Abstract

Classical conditioning is a fundamental paradigm in the study of learning and thus in understanding cognitive processes and behaviour, for which we need comprehensive and accurate models. This paper aims at evaluating and comparing a collection of influential computational models of classical conditioning by analysing the models themselves and against one another qualitatively. The results will clarify the state of the art in the area and help develop a standard model of classical conditioning.

References

  1. Alonso, E., & Mondragón, E. (Eds.)(2011), Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications, Hershey, PA: IGI Global.
  2. Alonso, E., & Mondragón, E. (2012), Uses, Abuses and Misuses of Computational Models in Classical Conditioning. In N. Rußwinkel, U. Drewitz & H. van Rijn (Eds.), Proceedings 11th International Conference on Cognitive Modeling (ICCM-12), pp. 96-100. Berlin, Germany: Universitaetsverlag der TU Berlin.
  3. Alonso, E., Mondragón, E., & Fernandez, A. (2012), A Java simulator of Rescorla and Wagner's prediction error model and configural cue extensions, Computer Methods and Programs in Biomedicine, 108, 346-355.
  4. Alonso, E., & Schmajuk, N. (2012), Computational Models of Classical Conditioning guest editors' introduction, Learning & Behavior, 40(3), 231-240.
  5. Baum, W. M. (1983), Matching, Statistics, and Common Sense, Journal of the Experimental Analysis of Behavior, 39, 499- 501.
  6. Dayan, P., & Abbott, L. F. (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press.
  7. Hall, G. (2002), Associative structures in Pavlovian and instrumental conditioning. In H. Pashler, S. Yantis, D. Medin, R. Gallistel an J. Wixted (Eds.), Stevens' Handbook of Experimental Psychology, Volume 3, pp 1-45. Hoboken, NJ: John Wiley and Sons.
  8. Haselgrove, M., & Hogarth, L. (2011), Clinical Applications of Learning Theory. London, UK: Psychology Press.
  9. Mackintosh, N. J. (1994)(Ed.), Animal Learning and Cognition, San Diego, CA: Academic Press.
  10. Mondragón, E., Alonso, E., Fernandez, A., & Gray, J. (2013a), A Rescorla and Wagner simulator with context compounds, Computer Methods and Programs in Biomedicine. DOI: 10.1016/j.cmpb.2013.01.016.
  11. Mondragón, E., Gray, J., & Alonso, E. (2013b), A Complete Serial Compound Temporal Difference Simulator for Compound stimuli, Configural cues and Context representation, Neuroinformatics. DOI: 10.1007/s12021-012-9172-z.
  12. Pearce, J. M., & Bouton, M. E. (2001), Theories of associative learning in animals. Annual Review of Psychology, 52, 111-139.
  13. Schachtman, T. R., & Reilly, S. (2011), Associative Learning and Conditioning Theory: Human and NonHuman Applications. Oxford, UK: Oxford University Press.
  14. Schmajuk, N. A. (1997), Animal Learning and Cognition: A Neural Network Approach. Cambridge, UK: Cambridge University Press.
  15. Schmajuk, N. A. (2010a), Mechanisms in Classical Conditioning: A Computational Approach. Cambridge, UK: Cambridge University Press.
  16. Schmajuk, N. A. (2010b)(Ed.), Computational Models of Conditioning. Cambridge, UK: Cambridge University Press.
  17. Schmajuk, N. A., & Alonso, E. (Eds.)(2012). Computational Models of Classical Conditioning, Learning & Behavior, 40(3).
  18. Schultheis, H., Thorwart, A., & Lachnit., H. (2008a), HMS: A MATLAB simulator of the Harris model of associative learning, Behavior Research Methods, 40, 442-449.
  19. Schultheis, H., Thorwart, A., & Lachnit, H. (2008b), Rapid-REM: A MATLAB simulator of the replaced elements model, Behavior Research Methods, 40, 435- 441.
  20. Shanks, D.R. (1995), The Psychology of Associative Learning. Cambridge, UK: Cambridge University Press.
  21. Shiffrin, R. M., Lee, M. D., Kim, W., & Wagenmakers, E.-J. (2008), A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science, 32, 1248-1284.
  22. Thorwart, A., Schultheis, H., König, S., & Lachnit, H. (2009), ALTSim: A MATLAB simulator for current associative learning theories, Behavior Research Methods, 41(1), 29-34.
  23. Vogel, E. H., Castro, M. E., & Saavedra, M. A. (2004), Quantitative models of Pavlovian conditioning, Brain Research Bulletin, 63, 173-202.
  24. Wills, A.J. & Pothos, E.M. (2012a), On the adequacy of current empirical evaluations of formal models of categorization, Psychological Bulletin, 138, 102-125.
  25. Wills, A. J. & Pothos, E. M. (2012b) On the adequacy of Bayesian evaluations of categorization models: Reply to Vanpaemel & Lee (2012), Psychological Bulletin, 138, 1259-1261.
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Paper Citation


in Harvard Style

Alonso E., Sahota P. and Mondragón E. (2014). Computational Models of Classical Conditioning - A Qualitative Evaluation and Comparison . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 544-547. DOI: 10.5220/0004903105440547


in Bibtex Style

@conference{icaart14,
author={Eduardo Alonso and Pavandeep Sahota and Esther Mondragón},
title={Computational Models of Classical Conditioning - A Qualitative Evaluation and Comparison},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={544-547},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004903105440547},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Computational Models of Classical Conditioning - A Qualitative Evaluation and Comparison
SN - 978-989-758-015-4
AU - Alonso E.
AU - Sahota P.
AU - Mondragón E.
PY - 2014
SP - 544
EP - 547
DO - 10.5220/0004903105440547