On the Category Adjustment Model: Another Look at Huttenlocher, Hedges, and Vevea (2000)

Duffy, Sean; Smith, John

doi:10.2139/ssrn.3067521

Cited by 3 publications

(3 citation statements)

References 79 publications

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“…As such, it remains our view that evidence for CAM is a statistical illusion that occurs when researchers analyze data averaged across trials and do not consider a recency bias. 19 Duffy and Smith (2019) does likewise with a dataset designed to replicate and they do not find any evidence consistent with CAM. Tables 1 and 2 In Table A1, we summarize the fixed-effects versions of Table 1.…”

Section: Discussionmentioning

confidence: 92%

Omitted-Variable Bias and Other Matters in the Defense of the Category Adjustment Model: A Reply to Crawford (2019)

Duffy¹,

Smith

2019

SSRN Journal

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The datasets from Duffy, Huttenlocher, Hedges, and Crawford (2010) [Psychonomic Bulletin & Review, 17(2), [224][225][226][227][228][229][230] were reanalyzed by Duffy and Smith (2018) [Psychonomic Bulletin & Review, 25(5), 1740-1750. Duffy and Smith (2018) conclude that the datasets are not consistent with the category adjustment model (CAM). Crawford (2019) [Psychonomic Bulletin & Review, 26(2), 693-698] offered a reply to Duffy and Smith (2018) that is based on three main points.Crawford proposes regressions that are, in part, based on a "deviation" analysis. Crawford offers a different simulation of data and claims that the techniques employed by Duffy and Smith (2018) are not sufficiently sensitive to detect a specific relationship that is claimed to be consistent with CAM. Crawford also appeals to a figure showing that the responses appear to be biased toward the overall running mean, and presumably not toward recently viewed lines. We show that Crawford's analysis suffers from an omitted-variable bias. Once this bias is corrected, the evidence in support of CAM disappears. When we produce a simulated dataset that is consistent with the specification suggested by Crawford, the techniques of Duffy and Smith (2018) correctly detect the true relationship. Despite the assertion otherwise, the simulated dataset that was analyzed by Crawford is not publicly available. Since the analysis of Crawford (2019) is incorrect, it remains our view that the datasets from Duffy, Huttenlocher, Hedges, and Crawford (2010) do not appear to be consistent with CAM or any Bayesian model of judgment.

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Section: Discussionmentioning

confidence: 92%

Omitted-Variable Bias and Other Matters in the Defense of the Category Adjustment Model: A Reply to Crawford (2019)

Duffy¹,

Smith

2019

SSRN Journal

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“…Later accounts of central tendency biases take a similar approach, expressing central tendency biases within a Bayesian framework (Jazayeri & Shadlen, 2010 ; Cicchini et al, 2012 ; Sciutti et al, 2014 ; Krügel et al, 2020 ). Recent publications question the ability of CAM, or Bayesian models more generally, to explain central tendency biases in perceptual judgements, suggesting instead that central tendency biases are the manifestation of a recency bias rather than a central bias (Duffy & Smith 2018 , 2020b ) but the debate is ongoing (Crawford, 2019 ; Duffy & Smith, 2020a ).…”

Section: Discussionmentioning

confidence: 99%

Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data

et al. 2021

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Observers in perceptual tasks are often reported to combine multiple sensory cues in a weighted average that improves precision—in some studies, approaching statistically optimal (Bayesian) weighting, but in others departing from optimality, or not benefitting from combined cues at all. To correctly conclude which combination rules observers use, it is crucial to have accurate measures of their sensory precision and cue weighting. Here, we present a new approach for accurately recovering these parameters in perceptual tasks with continuous responses. Continuous responses have many advantages, but are susceptible to a central tendency bias, where responses are biased towards the central stimulus value. We show that such biases lead to inaccuracies in estimating both precision gains and cue weightings, two key measures used to assess sensory cue combination. We introduce a method that estimates sensory precision by regressing continuous responses on targets and dividing the variance of the residuals by the squared slope of the regression line, “correcting-out” the error introduced by the central bias and increasing statistical power. We also suggest a complementary analysis that recovers the sensory cue weights. Using both simulations and empirical data, we show that the proposed methods can accurately estimate sensory precision and cue weightings in the presence of central tendency biases. We conclude that central tendency biases should be (and can easily be) accounted for to consistently capture Bayesian cue combination in continuous response data.

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“…Huttenlocher et al refer to this as a Bayesian model since the information about the distribution is employed to maximize the precision of the judgments. However, Duffy and Smith (2019a) analyze data from a replication of Huttenlocher et al (2000) and find that non-Bayesian explanations have more support in the data than the category adjustment model. Duffy and Smith (2018) analyze the data from Duffy, Huttenlocher, Hedges, and Crawford (2010), and come to a similar conclusion about the predictions of the category adjustment model.…”

Section: Introductionmentioning

confidence: 99%

Central tendency bias in belief elicitation

Crosetto

Filippin

Katuscak

et al. 2020

Journal of Economic Psychology

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We conduct an experiment in which subjects participate in a first-price auction against an automaton that bids randomly in a given range. The subjects first place a bid in the auction. They are then given an incentivized elicitation of their beliefs of the opponent's bid. Despite having been told that the bid of the opponent was drawn from a uniform distribution, we find that most of the subjects report beliefs that have a peak in the interior of the range. The result is robust across seven different experimental treatments. While not expected at the outset, these single-peaked beliefs have precedence in the experimental psychology judgments literature. Our results suggest that an elicitation of probability beliefs can result in responses that are more concentrated than the objectively known or induced truth. We provide indicative evidence that such individual belief reports can be rationalized by well-defined subjective beliefs that differ from the objective truth. Our findings offer an explanation for the conservatism and overprecision biases in Bayesian updating. Finally, our findings suggest that probabilistic forecasts of uncertain events might have less variance than the actual events.

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On the Category Adjustment Model: Another Look at Huttenlocher, Hedges, and Vevea (2000)

Cited by 3 publications

References 79 publications

Omitted-Variable Bias and Other Matters in the Defense of the Category Adjustment Model: A Reply to Crawford (2019)

Omitted-Variable Bias and Other Matters in the Defense of the Category Adjustment Model: A Reply to Crawford (2019)

Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data

Central tendency bias in belief elicitation

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