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Related Experiment Videos

Reply to Duffy and Smith's (2018) reexamination.

L Elizabeth Crawford1

  • 1University of Richmond, Richmond, VA, USA. lcrawfor@richmond.edu.

Psychonomic Bulletin & Review
|March 24, 2019
PubMed
Summary

This study re-examines central tendency bias in memory, finding that responses are primarily influenced by the long-run average, not just recent stimuli. This challenges recent reanalyses and supports original findings on memory biases.

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Area of Science:

  • Cognitive Psychology
  • Memory Research
  • Perceptual Bias

Background:

  • The central tendency bias describes the tendency to reproduce stimuli closer to the average of a presented set.
  • Previous research (Duffy et al., 2010) suggested this bias reflects a tendency towards the long-run mean.
  • A reanalysis (Duffy & Smith, 2018) proposed bias towards more recent stimuli.

Purpose of the Study:

  • To re-evaluate the Duffy and Smith (2018) reanalysis of central tendency bias data.
  • To determine whether memory reproductions are biased by the overall distribution mean or recent stimuli.
  • To assess the theoretical implications for Bayesian models of cognition.

Main Methods:

  • Re-analysis of existing data from Duffy et al. (2010).
  • Application of a different analytical strategy to assess memory bias.
  • Comparison of findings against the original study's conclusions and the reanalysis claims.

Main Results:

  • The reanalysis by Duffy and Smith (2018) may have overlooked a true effect of the running mean.
  • The original findings, suggesting bias towards the long-run mean, are largely supported by the data.
  • Evidence for bias towards only the most recent stimuli was not found.

Conclusions:

  • The data predominantly support the original conclusion that central tendency bias reflects a bias toward the long-run mean.
  • The reanalysis by Duffy and Smith (2018) is critiqued for potentially missing a running mean effect.
  • The findings, including null results, may not significantly alter the theoretical standing of category adjustment and similar Bayesian models.
Keywords:
CategorizationHuman memoryStatistical inference

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