Evidence map›Paper›PMID 29047071›Full record

ArticlePsychonomic bulletin & review2018

Category effects on stimulus estimation: Shifting and skewed frequency distributions-A reexamination.

Sean Duffy, John Smith

Abstract readComment
PubMed Publisher
In one paragraph

Article in Psychonomic bulletin & review, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Prior experience informs ensemble encoding.Psychonomic bulletin & review · 2019
    Article
  6. Reply to Duffy and Smith's (2018) reexamination.Psychonomic bulletin & review · 2019
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Sean DuffyDepartment of Psychology, Rutgers University-Camden, 311 N. 5th Street, Camden, NJ, 08102, USA.
John SmithDepartment of Economics, Rutgers University-Camden, 311 N. 5th Street, Camden, NJ, 08102, USA. smithj@camden.rutgers.edu.

Funding

Rutgers University Research Council Grant #202297
6 · The paper itself

Abstract

Duffy, Huttenlocher, Hedges, and Crawford (Psychonomic Bulletin & Review, 17(2), 224-230, 2010) report on experiments where participants estimate the lengths of lines. These studies were designed to test the category adjustment model (CAM), a Bayesian model of judgments. The authors report that their analysis provides evidence consistent with CAM: that there is a bias toward the running mean and not recent stimuli. We reexamine their data. First, we attempt to replicate their analysis, and we obtain different results. Second, we conduct a different statistical analysis. We find significant recency effects, and we identify several specifications where the running mean is not significantly related to judgment. Third, we conduct tests of auxiliary predictions of CAM. We do not find evidence that the bias toward the mean increases with exposure to the distribution. We also do not find that responses longer than the maximum of the distribution or shorter than the minimum become less likely with greater exposure to the distribution. Fourth, we produce a simulated dataset that is consistent with key features of CAM, and our methods correctly identify it as consistent with CAM. We conclude that the Duffy et al. (2010) dataset is not consistent with CAM. We also discuss how conventions in psychology do not sufficiently reduce the likelihood of these mistakes in future research. We hope that the methods that we employ will be used to evaluate other datasets.

Indexed as

Bayes TheoremJudgmentBiasData CollectionBayesian judgmentsCategory adjustment modelCentral tendency biasJudgmentMemoryRecency effects

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.