The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Parsimonious estimation of signal detection models from confidence ratings
Open accessAbstract
Signal detection theory (SDT) is used to quantify people's ability and bias in discriminating stimuli. The ability to detect a stimulus is often measured through confidence ratings. In SDT models, the use of confidence ratings necessitates the estimation of confidence category thresholds, a requirement that can easily result in models that are overly complex. As a parsimonious alternative, we propose a threshold SDT model that estimates these category thresholds using only two parameters. We fit the model to data from Pratte et al. (Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 224-232 2010) and illustrate its benefits over previous threshold SDT models.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Modelling data with approaches like that outlined by Selker et al. (2019) may make it more tractable to estimate an arbitrary number of thresholds via just two parameters per participant.
“We note, however, that modelling data in such cases may be made more tractable with statistical approaches like that outlined by Selker et al. (2019), which allows an arbitrary number of thresholds to be estimated via just two parameters per participant.”
From Rating norms should be calculated from cumulative link mixed effects models (2022) · cites this paper as Selker et al. (2019)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Bayesian Cognitive Modeling: A Practical Course
Lee, M. D., & Wagenmakers, E.-J. (2014) · doi:10.1017/cbo9781139087759 · Cited by 1,795
Self-citation
Flagged first, then by citations
Research works printed but not linked: 27
- Anders, R., & Batchelder, W. (2013). Cultural consensus theory for the ordinal data case. Psychometrika, 80, 151–181. · DOI
- Criss, A. H., Malmberg, K. J., & Shiffrin, R. M. (2011). Output interference in recognition memory. Journal of Memory and Language, 64(4), 316–326. · DOI
- DeCarlo, L. (2010). On the statistical and theoretical basis of signal detection theory and extensions: Unequal variance, random coefficient, and mixture models. Journal of Mathematical Psychology, 54, 304–313. · DOI
- Efron, B., & Morris, C. (1977). Stein’s paradox in statistics. Scientific American, 236, 119–127. · DOI
- Etz, A., Gronau, Q. F., Dablander, F., Edelsbrunner, P., & Baribault, B. (2016). How to become a Bayesian in eight easy steps: An annotated reading list. · DOI
- Fox, C. R., & Tversky, A (1995). Ambiguity aversion and comparative ignorance. The Quarterly Journal of Economics, 110(3), 585–603. · DOI
- Gelman, A., & Hill, J. (2006) Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press. · DOI · Paper page
- Gonzalez, R., & Wu, G (1999). On the shape of the probability weighting function. Cognitive Psychology, 38 (1), 129–166. · DOI
- Green, D. M., & Swets, J. (1966) Signal detection theory and psychophysics. Wiley: New York.
- Gronau, Q. F., Sarafoglou, A., Matzke, D., Ly, A., Boehm, U., Marsman, M., & Steingroever, H. (2017). A tutorial on bridge sampling. arXiv: 1703.05984 · DOI · Paper page
- Hanley, J. A., & McNeil, B. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29–36. · DOI · Paper page
- Jeffreys, H. (1961) Theory of probability. Oxford: Oxford University Press.
- Macmillan, N. A., & Creelman, C. (2005) Detection theory: A user’s guide Mahwah. NJ: Lawrence Erlbaum.
- Marden, J.I. (1996) Analyzing and modeling rank data. Boca Raton: CRC Press.
- Meng, X. L., & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: atheoretical exploration. Statistica Sinica, 6, 831–860.
- Mickes, L., Wixted, J. T., & Wais, P. (2007). A direct test of the unequal-variance signal detection model of recognition memory. Psychonomic Bulletin & Review, 14(5), 858–865. · DOI
- Morey, R. D., Pratte, M. S., & Rouder, J. (2008). Problematic effects of aggregation in z ROC analysis and a hierarchical modeling solution. Journal of Mathematical Psychology, 52, 376– 388. · DOI
- Mulder, J., & Wagenmakers, E. J. (2016). Editors’ introduction to the special issue ”Bayes factors for testing hypotheses in psychological research: Practical relevance and new developments”. Journal of Mathematical Psychology, 72, 1–5. · DOI
- Plummer, M. (2003). Jags: A program for analysis of Bayesian graphical models using Gibbs sampling. In K. Hornik, F. Leisch, & A. Zeileis (Eds.) Proceedings of the 3rd international workshop on distributed statistical computing (DSC 2003) (pp. 20–22).
- Pratte, M. S., Rouder, J. N., & Morey, R. (2010). Separating mnemonic process from participant and item effects in the assessment of ROC asymmetries. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 224–232.
- Pratte, M. S., & Rouder, J. (2011). Hierarchical single-and dual-process models of recognition memory. Journal of Mathematical Psychology, 55, 36–46. · DOI
- Rouder, J. N., & Lu, J. (2005). An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychonomic Bulletin & Review, 12, 573–604. · DOI
- Shiffrin, R. M., Lee, M. D., Kim, W., & Wagenmakers, E. J. (2008). A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science, 32, 1248–1284. · DOI
- Starns, J. J., & Ratcliff, R. (2014). Validating the unequal-variance assumption in recognition memory using response time distributions instead of ROC functions: A diffusion model analysis. Journal of Memory and Language, 70, 36–52. · DOI
- Swets, J. (1986). Form of empirical ROCs indiscrimination and diagnostic tasks: Implications for theory and measurement of performance. Psychological Bulletin, 99, 181–198. · DOI
- Tanner, Jr., W. P., & Swets, J. (1954). A decision-making theory of visual detection. Psychological Review, 61, 401–409. · DOI
- Wickens, T. (2001) Elementary signal detection theory. New York: Oxford University Press. · DOI
Cited by
Sorted by citations
Rating norms should be calculated from cumulative link mixed effects models
Taylor, J. E., Rousselet, G. A., Scheepers, C., & Sereno, S. C. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01814-7 · Cited by 126
Metadata
- Authors
- Selker, R., van den Bergh, D., Criss, A. H., & Wagenmakers, E.-J.
- Affiliations
- University of Amsterdam (NL)
- Syracuse University (US)
- Corresponding author
- van den Bergh, Don
- Venue
- Behavior Research Methods
- Published
- 2019-05-08
- Volume
- 51
- Issue
- 5
- Pages
- 1953-1967
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- PMC
- PMC6797662
- OpenAlex
- W2944642474
- Cited by
- 18
- References
- 1
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 6 · 2021: 4 · 2020: 4 · 2019: 1
- FWCI
- 2.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Behavior Research Methods (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (406-14-089)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- signal detection theory, confidence ratings, model parsimony, two-parameter estimation, detection performance, response bias
- MeSH terms
- Computer Simulation, Bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
