Advertisement

The multivariate adaptive design for efficient estimation of the time course of perceptual adaptation

  • Ping Chen
  • Steve Engel
  • Chun WangEmail author
Article

Abstract

In experiments on behavioral adaptation, hundreds or even thousands of trials per subject are often required in order to accurately recover the many psychometric functions that characterize adaptation’s time course. More efficient methods for measuring perceptual changes over time would be beneficial to such efforts. In this article, we propose two methods to adaptively select the optimal stimuli sequentially in an experiment on adaptation: These are the minimum entropy (ME) method and the match probability (MP) method. The ME method minimizes the uncertainty about the joint posterior distribution of the function parameters at each trial and is mathematically equivalent to Zhao, Lesmes, and Lu’s (2019) method, which efficiently measures time courses of perceptual change by maximizing information gain. The MP method selects the next stimulus that makes the value of the psychometric function closest to .5—that is, where the probability of choosing either one of the two options for each stimulus is closest to .5. We extended Zhao et al.’s (2019) work by evaluating the ME method in a new domain (contrast adaptation) with two simulation studies that compared it to MP and two other methods (i.e., traditional staircase and random methods), and also explored the optimal block length. ME outperformed the other three methods in general, and using fewer longer blocks generally produced better parameter recovery than using more shorter blocks.

Keywords

Adaptive design Minimum entropy Perceptual adaptation Time course Tilt aftereffect 

Notes

Acknowledgements

This project was supported by grant numbers IES R305D160010, NSF SES-1659328, NIH R01HD079439, and NSFC 31300862.

Open Practice Statement

The simulated data and source code for all experiments are available at https://sites.uw.edu/pmetrics/publications-and-source-code/.

References

  1. Cavagnaro, D. R., Myung, J. I., Pitt, M. A., & Kujala, J. (2010). Adaptive design optimization: A mutual information-based approach to model discrimination in cognitive science. Neural Computation, 22, 887–905.CrossRefGoogle Scholar
  2. Cavagnaro, D. R., Pitt, M. A., & Myung, J. I. (2011). Model discrimination through adaptive experimentation. Psychonomic Bulletin & Review, 18, 204–210.CrossRefGoogle Scholar
  3. Chang, H.-H., & Ying, Z. L. (1999). a-stratified multistage computerized adaptive testing. Applied Psychological Measurement, 23, 211–222.CrossRefGoogle Scholar
  4. Chen, P., Wang, C., Xin, T., & Chang, H.-H. (2017). Developing new online calibration methods for multidimensional computerized adaptive testing. British Journal of Mathematical and Statistical Psychology, 70, 81–117.CrossRefGoogle Scholar
  5. Cheng, Y. (2008). Computerized adaptive testing—New developments and applications (Unpublished doctoral dissertation). University of Illinois at Urbana-Champaign, Urbana-Champaign, IL.Google Scholar
  6. Clifford, C. W. G., & Rhodes, G. (2005). Fitting the mind to the world: Adaptation and after-effects in high-level vision. New York, NY: Oxford University Press.Google Scholar
  7. Clifford, C. W. G., Webster, M. A., Stanley, G. B., Stocker, A. A., Kohn, A., Sharpee, T. O., & Schwartz, O. (2007). Visual adaptation: Neural, psychological and computational aspects. Vision Research, 47, 3125–3131.CrossRefGoogle Scholar
  8. Cobo-Lewis, A. (1996). An adaptive method for estimating multiple parameters of a psychometric function. Journal of Mathematical Psychology, 40, 353–354.Google Scholar
  9. Cornsweet, T. N. (1962). The staircase-method in psychophysics. American Journal of Psychology, 75, 485–491.CrossRefGoogle Scholar
  10. Hall, J. L. (1981). Hybrid adaptive procedure for estimation of psychometric functions. Journal of the Acoustical Society of America, 69, 1763–1769.CrossRefGoogle Scholar
  11. Kujala, J. V., & Lukka, T. J. (2006). Bayesian adaptive estimation: The next dimension. Journal of Mathematical Psychology, 50, 369–389.CrossRefGoogle Scholar
  12. Leek, M. R. (2001). Adaptive procedures in psychophysical research. Perception & Psychophysics, 63, 1279–1292.CrossRefGoogle Scholar
  13. Lesmes, L. A., Lu, Z.-L., Baek, J., & Albright, T. D. (2010). Bayesian adaptive estimation of the contrast sensitivity function: The quick CSF method. Journal of Vision, 10(3), 17:1–21. doi: https://doi.org/10.1167/10.3.17 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Lesmes, L. A., Lu, Z.-L., Baek, J., Tran, N., Dosher, B. A., & Albright, T. D. (2015). Developing Bayesian adaptive methods for estimating sensitivity thresholds (d’) in yes–no and forced-choice tasks. Frontiers in Psychology, 6, 1070. doi: https://doi.org/10.3389/fpsyg.2015.01070 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Levitt, H. (1971). Transformed up-down methods in psychoacoustics. Journal of the Acoustical Society of America, 49, 467–477.CrossRefGoogle Scholar
  16. Mei, G. X., Dong, X., & Bao, M. (2017). The timescale of adaptation at early and mid-level stages of visual processing. Journal of Vision, 17(1), 1:1–7. doi: https://doi.org/10.1167/17.1.1 CrossRefPubMedGoogle Scholar
  17. Mulder, J., & van der Linden, W. J. (2010). Multidimensional adaptive testing with Kullback–Leibler information item selection. In W. J. van der Linden & C. A. W. Glas (Eds.), Elements of adaptive testing (pp. 77–101). New York, NY: Springer.Google Scholar
  18. Myung, J. I., Cavagnaro, D. R., & Pitt, M. A. (2013). A tutorial on adaptive design optimization. Journal of Mathematical Psychology, 57, 53–67.CrossRefGoogle Scholar
  19. Patterson, C. A., Wissig, S. C., & Kohn, A. (2013). Distinct effects of brief and prolonged adaptation on orientation tuning in primary visual cortex. Journal of Neuroscience, 33, 532–543.CrossRefGoogle Scholar
  20. Pavan, A., Marotti, R. B., & Campana, G. (2012). The temporal course of recovery from brief (sub-second) adaptations to spatial contrast. Vision Research, 62, 116–124.CrossRefGoogle Scholar
  21. Pugh, E. N., Nikonov, S., & Lamb, T. D. (1999). Molecular mechanisms of vertebrate photoreceptor light adaptation. Current Opinion in Neurobiology, 9, 410–418.CrossRefGoogle Scholar
  22. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–656.CrossRefGoogle Scholar
  23. Segall, D. O. (1996). Multidimensional adaptive testing. Psychometrika, 61, 331–354.CrossRefGoogle Scholar
  24. Tuerlinckx, F., Rijmen, F., Verbeke, G., & De Boeck, P. (2006). Statistical inference in generalized linear mixed models: A review. British Journal of Mathematical and Statistical Psychology, 59, 225–255.Google Scholar
  25. van der Linden, W. J. (1999). Multidimensional adaptive testing with a minimum error-variance criterion. Journal of Educational and Behavioral Statistics, 24, 398–412.CrossRefGoogle Scholar
  26. Veldkamp, B. P., & van der Linden, W. J. (2002). Multidimensional adaptive testing with constraints on test context. Psychometrika, 67, 575–588.CrossRefGoogle Scholar
  27. Wang, C. (2015). On latent trait estimation in multidimensional compensatory item response models. Psychometrika, 80, 428–449.CrossRefGoogle Scholar
  28. Wang, C., & Chang, H.-H. (2011). Item selection in multidimensional computerized adaptive testing—Gaining information from different angles. Psychometrika, 76, 363–384.CrossRefGoogle Scholar
  29. Wang, C., Chang, H.-H., & Boughton, K. A. (2013). Deriving stopping rules for multidimensional computerized adaptive testing. Applied Psychological Measurement, 37, 99–122.CrossRefGoogle Scholar
  30. Watson, A. B., & Pelli, D. G. (1983). QUEST: A Bayesian adaptive psychometric method. Perception & Psychophysics, 33, 113–120. doi: https://doi.org/10.3758/BF03202828 CrossRefGoogle Scholar
  31. Zaidi, Q., Ennis, R., Cao, D. C., & Lee, B. (2012). Neural locus of color afterimages. Current Biology, 22, 220–224.CrossRefGoogle Scholar
  32. Zhao, Y. K., Lesmes, L. A., & Lu, Z.-L. (2017). The quick change detection method: Bayesian adaptive assessment of the time course of perceptual sensitivity change. Investigative Ophthalmology and Visual Science, 58, 5633.Google Scholar
  33. Zhao, Y. K., Lesmes, L., & Lu, Z.-L. (2019). Efficient assessment of the time course of perceptual sensitivity change. Vision Research, 154, 21–43.CrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Beijing Normal UniversityBeijingChina
  2. 2.University of MinnesotaMinneapolisUSA
  3. 3.University of WashingtonSeattleUSA

Personalised recommendations