Skip to main content
View expanded cover

Likelihood-Free Methods for Cognitive Science pp 55–79Cite as

A Tutorial

  • 379 Accesses

Part of the Computational Approaches to Cognition and Perception book series (CACP)

Abstract

In this chapter, we apply some of the likelihood-free algorithms discussed in previous chapters in a tutorial fashion with an application to the Minerva 2 model, a global matching model of recognition memory. First, we will briefly describe the model. Then, we will fit the Minerva 2 model to simulated data using three different likelihood-free algorithms and compare how well each technique provides fits to the data. Finally, we will fit a hierarchical version of the Minerva 2 model to real-world data using a combination of the probability density approximation method and the Gibbs ABC algorithm.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-72425-6_3
  • Chapter length: 25 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-72425-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Hardcover Book
USD   99.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 3.1
Fig. 3.2
Fig. 3.3
Fig. 3.4
Fig. 3.5
Fig. 3.6
Fig. 3.7

Notes

  1. 1.

    The responses are arbitrarily coded as either a one for an “old” response, or a zero for a “new” response.

  2. 2.

    In our simulations, we tested a few different values of δ ABC until we arrived at the smallest value that still produced good mixing behavior across the chains.

  3. 3.

    Dennis et al. [4] also used words of different frequency (high and low) to construct their study and test lists. Because Minerva 2 lacks a mechanism for explaining word frequency effects in recognition memory, for the purposes of this demonstration we collapsed across both word frequency classes to produce a single hit and false alarm rate for each experimental condition.

  4. 4.

    After fitting the model, we noticed that no marginal distribution for η i went below four, so while our choice was made out of convenience, it had little effect on the posterior estimates.

References

  1. S. Dennis, M. Lee, A. Kinnell, J. Math. Psychol. 59, 361 (2008)

    Google Scholar 

  2. M.D. Lee, Psychon. Bull. Rev. 15, 1 (2008)

    CrossRef  PubMed  Google Scholar 

  3. R.M. Shiffrin, M.D. Lee, W. Kim, E.J. Wagenmakers, Cogn. Sci. 32, 1248 (2008)

    CrossRef  PubMed  Google Scholar 

  4. M.D. Lee, E.J. Wagenmakers, Bayesian Modeling for Cognitive Science: A Practical Course (Cambridge University Press, Cambridge, 2013)

    CrossRef  Google Scholar 

  5. D.L. Hintzman, Behav. Res. Methods Instrum. Comput. 16, 96–101 (1984)

    CrossRef  Google Scholar 

  6. C.F. Sheu, J. Math. Psychol. 36, 592 (1992)

    CrossRef  Google Scholar 

  7. B.M. Turner, P.B. Sederberg, Psychon. Bull. Rev. 21, 227 (2014)

    CrossRef  PubMed  PubMed Central  Google Scholar 

  8. B.M. Turner, T. Van Zandt, J. Math. Psychol. 56, 69 (2012)

    CrossRef  Google Scholar 

  9. B.M. Turner, P.B. Sederberg, J. Math. Psychol. 56, 375 (2012)

    CrossRef  Google Scholar 

  10. B.M. Turner, P.B. Sederberg, S. Brown, M. Steyvers, Psychol. Methods 18, 368 (2013)

    CrossRef  PubMed  PubMed Central  Google Scholar 

  11. B.M. Turner, T. Van Zandt, Psychometrika 79, 185 (2014)

    CrossRef  PubMed  Google Scholar 

  12. D.M. Green, J.A. Swets, Signal Detection Theory and Psychophysics (Wiley, New York, 1966)

    Google Scholar 

  13. J.P. Egan, Recognition memory and the operating characteristic. Technical Report, AFCRC-TN-58-51, Hearing and Communication Laboratory, Indiana University, Bloomington, IN (1958)

    Google Scholar 

  14. B.B. Murdock, Psychol. Rev. 89, 609 (1982)

    CrossRef  Google Scholar 

  15. G. Gillund, R.M. Shiffrin, Psychol. Rev. 91, 1 (1984)

    CrossRef  PubMed  Google Scholar 

  16. M.S. Humphreys, J.D. Bain, R. Pike, Psychol. Rev. 96, 208 (1989)

    CrossRef  Google Scholar 

  17. R. Pike, Psychol. Rev. 91, 281 (1984)

    CrossRef  Google Scholar 

  18. S.E. Clark, S.D. Gronlund, Psychon. Bull. Rev. 3, 37 (1996)

    CrossRef  PubMed  Google Scholar 

  19. M.S. Humphreys, R. Pike, J.D. Bain, G. Tehan, J. Math. Psychol. 33, 36 (1989)

    CrossRef  Google Scholar 

  20. D.L. Hintzman, Psychol. Rev. 95, 528 (1988)

    CrossRef  Google Scholar 

  21. R. Ratcliff, C.F. Sheu, S.D. Gronlund, Psychol. Rev. 99, 518 (1992)

    CrossRef  PubMed  Google Scholar 

  22. R. Ratcliff, S.E. Clark, R.M. Shiffrin, J. Exp. Psychol. Learn. Mem. Cogn. 16, 163 (1990)

    CrossRef  PubMed  Google Scholar 

  23. M. Glanzer, J.K. Adams, Mem. Cogn. 13, 8 (1985)

    CrossRef  Google Scholar 

  24. R.M. Shiffrin, M. Steyvers, Psychon. Bull. Rev. 4, 145 (1997)

    CrossRef  PubMed  Google Scholar 

  25. J. McClelland, M. Chappell, Psychol. Rev. 105, 724 (1998)

    CrossRef  PubMed  Google Scholar 

  26. S. Dennis, M.S. Humphreys, Psychol. Rev. 108, 452 (2001)

    CrossRef  PubMed  Google Scholar 

  27. J. Arndt, E. Hirshman, J. Mem. Lang. 39, 371 (1998)

    CrossRef  Google Scholar 

  28. A.S. Benjamin, Psychol. Rev. 117(4), 1055 (2010)

    CrossRef  PubMed  PubMed Central  Google Scholar 

  29. M.R.P. Dougherty, C.F. Gettys, E.E. Ogden, Psychol. Rev. 106(1), 180 (1999)

    CrossRef  Google Scholar 

  30. R.P. Thomas, M.R.P. Dougherty, A.M. Sprenger, J.I. Harbison, Psychol. Rev. 115(1), 155 (2008)

    CrossRef  PubMed  Google Scholar 

  31. P.J. Kwantes, D.J.K. Mewhort, Can. J. Exp. Psychol. 53(4), 306 (1999)

    CrossRef  PubMed  Google Scholar 

  32. P.J. Kwantes, Psychon. Bull. Rev. 12(4), 703 (2005)

    CrossRef  PubMed  Google Scholar 

  33. B.M. Turner, S. Dennis, T. Van Zandt, Psychol. Rev. 120, 667 (2013)

    CrossRef  PubMed  PubMed Central  Google Scholar 

  34. S.E. Clark, R.M. Shiffrin, Mem. Cogn. 20, 580 (1992)

    CrossRef  Google Scholar 

  35. J. Annis, J.G. Lenes, H.A. Westfall, A.H. Criss, K.J. Malmberg, Genetics 85, 27 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, The Ohio State University, Columbus, OH, USA

    James J. Palestro, Per B. Sederberg, Trisha Van Zandt & Brandon M. Turner

  2. University of Melbourne, Parkville, VIC, Australia

    Adam F. Osth

Authors
  1. James J. Palestro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Per B. Sederberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adam F. Osth
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Trisha Van Zandt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brandon M. Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Palestro, J.J., Sederberg, P.B., Osth, A.F., Zandt, T.V., Turner, B.M. (2018). A Tutorial. In: Likelihood-Free Methods for Cognitive Science. Computational Approaches to Cognition and Perception. Springer, Cham. https://doi.org/10.1007/978-3-319-72425-6_3

Download citation