Psychonomic Bulletin & Review

, Volume 26, Issue 2, pp 693–698 | Cite as

Reply to Duffy and Smith’s (2018) reexamination

  • L. Elizabeth CrawfordEmail author


Duffy, Huttenlocher, Hedges, and Crawford (2010, Psychonomic Bulletin & Review, 17[2], 224–230) examined whether the well-established central tendency bias in people’s reproductions of stimuli reflects bias toward the mean of an entire presented distribution or bias toward only recently seen stimuli. They reported evidence that responses were biased toward the long-run mean and found no evidence that they were biased toward the most recent stimuli. Duffy and Smith (2018) reexamine the data using a different analytical strategy and argue that estimates are biased by recent stimuli rather than toward the long-run mean. I argue that this reanalysis misses a true effect of the running mean and that the data are (mostly) consistent with the claims in the original work. I suggest that these results, and many other null results presented by Duffy and Smith, do not have major theoretical significance for the category adjustment model and similar Bayesian models. (Code and data available:


Human memory Statistical inference Categorization 



  1. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. doi: CrossRefGoogle Scholar
  2. Box, G. E. P. (1979). Robustness in the strategy of scientific model building. In R. L. Launer & G. N. Wilkinson (Eds.), Robustness in statistics (pp. 201–236). New York, NY: Academic Press.CrossRefGoogle Scholar
  3. Choplin, J. M., & Hummel, J. E. (2002). Magnitude comparisons distort mental representations of magnitude. Journal of Experimental Psychology: General, 131, 270–286. doi: CrossRefGoogle Scholar
  4. Cornell, J. A. (2011). A primer on experiments with mixtures. New York, NY: Wiley.CrossRefGoogle Scholar
  5. Crawford, L. E., Landy, D., & Salthouse, T. (2016). Spatial working memory capacity predicts bias in estimates of location. Journal of Experimental Psychology: Learning, Memory & Cognition 42, 1434–1447. doi: Google Scholar
  6. Duffy, S., & Crawford, L. E. (2008). Primacy or recency effects in forming inductive categories. Memory & Cognition, 36(3), 567–577. doi: CrossRefGoogle Scholar
  7. Duffy, S., Huttenlocher, J., & Crawford, L. E. (2006). Children use categories to maximize accuracy in estimation. Developmental Science, 9, 597–603. doi: CrossRefGoogle Scholar
  8. Duffy, S., Huttenlocher, J., Hedges, L. V., & Crawford, L. E. (2010). Category effects on stimulus estimation: Shifting and skewed frequency distributions. Psychonomic Bulletin & Review, 17(2), 224–230. doi: CrossRefGoogle Scholar
  9. Duffy, S., & Smith, J. (2018). Category effects on stimulus estimation: Shifting and skewed frequency distributions—A reexamination. Psychonomic Bulletin & Review, 25(5), 1740–1750. doi: CrossRefGoogle Scholar
  10. Huttenlocher, J., Hedges, L. V., & Duncan, S. (1991). Categories and particulars: Prototype effects in estimating spatial location. Psychological Review, 98, 352–376. doi: CrossRefGoogle Scholar
  11. Huttenlocher, J., Hedges, L. V., & Vevea, J. L. (2000). Why do categories affect stimulus judgment? Journal of Experimental Psychology: General, 129, 220–241. doi: CrossRefGoogle Scholar
  12. Tauber, S., Navarro, D. J., Perfors, A., & Steyvers, M. (2017). Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory. Psychological Review, 124(4), 410–441. doi: CrossRefGoogle Scholar
  13. Weisberg, S. (1985). Applied linear regression. New York, NY: John Wiley & Sons.Google Scholar
  14. Wickham, H. (2009). ggplot2: Elegant graphics for data analysis. New York, NY: Springer-Verlag.CrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.University of RichmondRichmondUSA

Personalised recommendations