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A unified explanation of variability and bias in human probability judgments : how computational noise explains the mean-variance signature
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Sundh, J., Zhu, J -Q., Chater, Nick and Sanborn, Adam N. (2023) A unified explanation of variability and bias in human probability judgments : how computational noise explains the mean-variance signature. Journal of Experimental Psychology: General . doi:10.1037/xge0001414 ISSN 0096-3445. (In Press)
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Official URL: https://doi.org/10.1037/xge0001414
Abstract
Human probability judgments are both variable and subject to systematic biases. Most probability judgment models treat variability and bias separately: a deterministic model explains the origin of bias, to which a noise process is added to generate variability. But these accounts do not explain the characteristic inverse U-shaped signature linking mean and variance in probability judgments. By contrast, models based on sampling generate the mean and variance of judgments in a unified way: the variability in the response is an inevitable consequence of basing probability judgments on a small sample of remembered or simulated instances of events. We consider two recent sampling models, in which biases are explained either by the sample accumulation being further corrupted by retrieval noise (the Probability Theory + Noise account) or as a Bayesian adjustment to the uncertainty implicit in small samples (the Bayesian sampler). While the mean predictions of these accounts closely mimic one another, they differ regarding the predicted relationship between mean and variance. We show that these models can be distinguished by a novel linear regression method that analyses this crucial mean–variance signature. First, the efficacy of the method is established using model recovery, demonstrating that it more accurately recovers parameters than complex approaches. Second, the method is applied to the mean and variance of both existing and new probability judgment data, confirming that judgments are based on a small number of samples that are adjusted by a prior, as predicted by the Bayesian sampler.
| Item Type: | Journal Article | ||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Psychology Faculty of Social Sciences > Warwick Business School |
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| Journal or Publication Title: | Journal of Experimental Psychology: General | ||||||
| Publisher: | American Psychological Association | ||||||
| ISSN: | 0096-3445 | ||||||
| Official Date: | 2023 | ||||||
| Dates: |
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| DOI: | 10.1037/xge0001414 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| Reuse Statement (publisher, data, author rights): | ©American Psychological Association, 2023. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at: https://doi.org/10.1037/xge0001414 | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 22 February 2023 | ||||||
| Date of first compliant Open Access: | 24 February 2023 | ||||||
| RIOXX Funder/Project Grant: |
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