@article{Meyer-Grant2027,

title = {Extreme-Value Signal Detection Theory for Recognition Memory: The Parametric Road Not Taken},

author = {Constantin G. Meyer-Grant and David Kellen and Samuel Harding and Henrik Singmann},

url = {https://osf.io/qhrfj, preprint link},

year  = {2027},

date = {2027-08-01},

urldate = {2025-08-01},

journal = {Psychological Review},

publisher = {OSF},

abstract = {Signal Detection Theory has long served as a cornerstone of psychological research, particularly in recognition memory. Yet its conventional application hinges almost exclusively on the Gaussian assumption—an adherence rooted more in historical convenience than theoretical necessity that comes with a number of well-documented drawbacks. In this work, we critically examine these limitations and introduce a principled parametric alternative: the Gumbel_min model, based on extreme-value distributions of event minima. A key feature of this model is its grounding in a behavioral principle of invariance under uniform choice-set expansions—a prediction we empirically validate in a novel recognition-memory experiment. We further benchmark the Gumbel_min model against its Gaussian counterpart across multiple recognition-memory tasks, including confidence-rating, ranking, forced-choice, and detection-plus-identification paradigms. Our findings highlight the model's parsimonious yet successful characterization of recognition-memory judgments, as well as the utility of its associated discriminability index, g', which can be directly computed from a single pair of hit and false-alarm rates.},

keywords = {critical tests, extreme value distributions, invariance properties, Recognition memory, signal detection theory},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{deans-browneEverydayArgumentsPrior2026,

title = {For Everyday Arguments Prior Beliefs Play a Larger Role on Perceived Argument Quality than Argument Quality Itself},

author = {Calvin Deans-Browne and Henrik Singmann},

url = {http://singmann.org/download/publications/Deans-Browne%20and%20Singmann%20-%202026%20-%20For%20everyday%20arguments%20prior%20beliefs%20play%20a%20larger%20role%20on%20perceived%20argument%20quality%20than%20argument.pdf, publisher PDF

https://www.sciencedirect.com/science/article/pii/S0010027725001970, publisher website},

doi = {10.1016/j.cognition.2025.106257},

issn = {0010-0277},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

volume = {266},

pages = {106257},

abstract = {Not all arguments are equally convincing, and whilst a given argument may be persuasive to some people, it is often seen as inadequate by others. We are interested in both the individual and argument level differences that make ‘everyday’ arguments such as those on social media persuasive. We investigate this question using a paradigm that consists of two parts. In the first part, we measure participants' individual beliefs about eight claims each referring to a political topic (e.g., Abortion should be legal). In the second part, participants rated the quality of an argument for each of these claims. Arguments were good or bad (Experiments 1 and 2) or good, inconsistent, or authority-based (Experiment 3). Good, inconsistent, and authority-based arguments summarised arguments from an educational bipartisan website, contained internal inconsistencies, or were based on appeals to authority, respectively. We found that participants preferred arguments that were also in line with their beliefs. We also found that participants were able to discriminate the qualities of different arguments – good arguments were rated as better than any other type of argument. In Experiment 3, inconsistent arguments were rated as better than those making appeals to authority. Importantly, the maximum effect of belief was larger than the maximum effect of argument quality. Thus, people do not evaluate arguments independently of the background beliefs held about them, which play at least as large a role in evaluating the quality of the argument as does the actual quality of the argument itself.},

keywords = {Argument evaluation, Argument quality, Belief-based reasoning, Everyday reasoning, Informal reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2025,

title = {Evaluating the Role of Mental Sampling in Probability Judgments: Illogical Rankings Occur in a Predictable Manner},

author = {Xiaotong Liu and Arndt Bröder and Henrik Singmann},

url = {http://singmann.org/download/publications/Liu%20et%20al.%20-%202025%20-%20Evaluating%20the%20role%20of%20mental%20sampling%20in%20probability%20judgments%20Illogical%20rankings%20occur%20in%20a%20predi.pdf, publisher PDF

https://osf.io/bfwpe_v3/download/, preprint},

doi = {10.1016/j.cognition.2025.106125},

year  = {2025},

date = {2025-10-01},

urldate = {2026-03-15},

journal = {Cognition},

volume = {263},

pages = {106125},

abstract = {People’s probability judgments often appear to be probabilistically incoherent, as exemplified by the conjunction fallacy. Recently, various sampling-based models have been proposed as an integrative account for different biases and fallacies in probability judgments. In the current study, the novel Event Ranking Task was used to investigate sampling-based models of probability judgments. On each trial of the Event Ranking Task, participants were asked to provide a ranking for an event set consisting of four events, A, not-A, B, and not-B, in terms of their perceived likelihoods. Qualitative predictions were formally derived by assuming direct sampling from a fixed underlying probability distribution. Adding read-out noise in the sampling process – as suggested in the Probability Theory plus Noise model (Costello & Watts, 2014) – did not change the qualitative predictions. Two online experiments, where participants ranked twelve different event sets, yielded results in line with the qualitative predictions, providing evidence for the idea that mental sampling underlies probability judgments.},

keywords = {mental sampling, Probabilistic reasoning, rationality},

pubstate = {published},

tppubtype = {article}

}

@article{Newall2025b,

title = {Evaluation of the 'take time to think' safer gambling message: a randomised, online experimental study},

author = {Philip WS Newall and Ty Hayes and and Henrik Singmann and Leonardo Weiss-Cohen and Lukasz Walasek and Elliot A Ludvig},

url = {https://doi.org/10.1017/bpp.2023.2, publisher website (open access)},

doi = {10.1017/bpp.2023.2},

year  = {2025},

date = {2025-09-01},

urldate = {2026-01-01},

journal = {Behavioural Public Policy},

volume = {9},

issue = {4},

pages = {762-779},

keywords = {applied, gambling},

pubstate = {published},

tppubtype = {article}

}

@article{Kellen2025,

title = {Critical Testing in Recognition Memory: Selective Influence, Single-Item Generalization, and the High-Threshold Hypothesis},

author = { David Kellen and Constantin G. Meyer-Grant and Henrik Singmann and Karl Christoph Klauer },

url = {http://singmann.org/download/publications/Kellen%20et%20al.%20-%202025%20-%20Critical%20testing%20in%20recognition%20memory%20Selective%20influence%2C%20single-item%20generalization%2C%20and%20the%20hig.pdf, publisher PDF

https://osf.io/fnsk2/download/, accepted preprint

https://osf.io/qz5re/, code on OSF

https://doi.org/10.31234/osf.io/fnsk2, PsyArXiv preprint

},

year  = {2025},

date = {2025-08-01},

urldate = {2025-08-01},

journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},

volume = {51},

issue = {8},

pages = {1259-1280},

keywords = {discrete states, mathematical modeling, measurement models, memory, Recognition memory, Signal detection},

pubstate = {published},

tppubtype = {article}

}

@article{Rey-Mermet2025,

title = {Neither Measurement Error nor Speed-Accuracy Trade-Offs Explain the Difficulty of Establishing Attentional Control as a Psychometric Construct: Evidence from a Latent-Variable Analysis Using Diffusion Modeling},

author = {Alodie Rey-Mermet and Henrik Singmann and Klaus Oberauer},

url = {http://singmann.org/download/publications/Rey-Mermet%20et%20al.%20-%202025%20-%20Neither%20measurement%20error%20nor%20speed%E2%80%93accuracy%20trade-offs%20explain%20the%20difficulty%20of%20establishing%20atten.pdf, publisher PDF

https://osf.io/3h26y_v2/download/, preprint},

doi = {10.3758/s13423-025-02696-4},

year  = {2025},

date = {2025-07-01},

urldate = {2025-07-01},

journal = {Psychonomic Bulletin & Review},

volume = {32},

pages = {2585–2632},

abstract = {Attentional control refers to the ability to maintain and implement a goal and goal-relevant information when facing distraction. So far, previous research has failed to substantiate strong evidence for a psychometric construct of attentional control. This could result from two methodological shortcomings: (a) the neglect of individual differences in speed-accuracy trade-offs when only speed or accuracy is used as dependent variable, and (b) the difficulty of isolating attentional control from measurement error. To overcome both issues, we combined hierarchical-Bayesian Wiener diffusion modeling with structural equation modeling. We re-analyzed six datasets, which included data from three to eight attentional-control tasks, and data from young and older adults. Overall, the results showed that measures of attentional control failed to correlate with each other and failed to load on a latent variable. Therefore, limiting the impact of differences in speed-accuracy trade-offs and of measurement error does not solve the difficulty of establishing attentional control as a psychometric construct. These findings strengthen the case against a psychometric construct of attentional control.},

keywords = {Diffusion model, executive functions, hierarchical-Bayesian modeling, individual differences},

pubstate = {published},

tppubtype = {article}

}

@article{maierDecisionMakingExtinction2025a,

title = {Decision Making under Extinction Risk},

author = {Maximilian Maier and Adam J. L. Harris and David Kellen and Henrik Singmann},

url = {https://www.sciencedirect.com/science/article/pii/S0010028525000234},

doi = {10.1016/j.cogpsych.2025.101735},

issn = {0010-0285},

year  = {2025},

date = {2025-07-01},

urldate = {2025-06-24},

volume = {159},

pages = {101735},

abstract = {In everyday life, people routinely make decisions that involve irredeemable risks such as death (e.g., while driving). Even though these decisions under extinction risk are common, practically important, and have different properties compared to the types of decisions typically studied by decision scientists, they have received little research attention. The present work advances the formal understanding of decision making under extinction risk by introducing a novel experimental paradigm, the Extinction Gambling Task (EGT). We derive optimal strategies for three different types of extinction and near-extinction events, and compare them to participants’ choices in three experiments. Leveraging computational modelling to describe strategies at the individual level, we document strengths and shortcomings in participants’ decisions under extinction risk. Specifically, we find that, while participants are relatively good in terms of the qualitative strategies they employ, their decisions are nevertheless affected by loss chasing, scope insensitivity, and opportunity cost neglect. We hope that by formalising decisions under extinction risk and providing a task to study them, this work will facilitate future research on an important topic that has been largely ignored.},

keywords = {Computational modelling, Decision Making, Extinction, Extreme outcomes, Mixture modelling, Risky choice},

pubstate = {published},

tppubtype = {article}

}

@article{boagExpertGuidePlanning2025,

title = {An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling},

author = {Russell J. Boag and Reilly J. Innes and Niek Stevenson and Giwon Bahg and Jerome R. Busemeyer and Gregory E. Cox and Chris Donkin and Michael J. Frank and Guy E. Hawkins and Andrew Heathcote and Craig Hedge and Veronika Lerche and Simon D. Lilburn and Gordon D. Logan and Dora Matzke and Steven Miletić and Adam F. Osth and Thomas J. Palmeri and Per B. Sederberg and Henrik Singmann and Philip L. Smith and Tom Stafford and Mark Steyvers and Luke Strickland and Jennifer S. Trueblood and Konstantinos Tsetsos and Brandon M. Turner and Marius Usher and Leendert {van Maanen} and Don {van Ravenzwaaij} and Joachim Vandekerckhove and Andreas Voss and Emily R. Weichart and Gabriel Weindel and Corey N. White and Nathan J. Evans and Scott D. Brown and Birte U. Forstmann},

url = {https://doi.org/10.1177/25152459251336127},

doi = {10.1177/25152459251336127},

issn = {2515-2459},

year  = {2025},

date = {2025-04-01},

urldate = {2025-04-01},

volume = {8},

number = {2},

pages = {25152459251336127},

publisher = {SAGE Publications Inc},

abstract = {Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{niuJudgmentDecisionStrategies2024,

title = {Judgment and Decision Strategies Used by Weather Scientists in Southeast Asia to Classify Impact Severity},

author = {Xiaoxiao Niu and Henrik Singmann and Faye Wyatt and Agie W. Putra and Azlai Taat and Jehan S. Panti and Lam Hoang and Lorenzo A. Moron and Sazali Osman and Riefda Novikarany and Diep Quang Tran and Rebecca Beckett and Adam JL. Harris},

url = {http://singmann.org/download/publications/Niu-et-al-2024.pdf, Publisher PDF},

doi = {10.1016/j.ijdrr.2024.104799},

issn = {2212-4209},

year  = {2024},

date = {2024-10-15},

urldate = {2024-10-15},

volume = {113},

pages = {104799},

keywords = {applied, Decision Making},

pubstate = {published},

tppubtype = {article}

}

@article{Singmann2024,

title = {Evaluating the Robustness of Parameter Estimates in Cognitive Models: A Meta-Analytic Review of Multinomial Processing Tree Models Across the Multiverse of Estimation Methods},

author = {Henrik Singmann and Daniel W Heck and Marius Barth and Edgar Erdfelder and Nina R Arnold and Frederik Aust and Jimmy Calanchini and Fabian E Gümüsdagli and Sebastian S Horn and David Kellen and Karl C. Klauer and Dora Matzke and Franziska Meissner and Martha Michalkiewicz and Marie Luisa Schaper and Christoph Stahl and Beatrice G. Kuhlmann and Julia Groß},

url = {https://psycnet.apa.org/fulltext/2024-99104-001.pdf, journal PDF

http://singmann.org/download/publications/MPT-multiverse.pdf, accepted manuscript

https://osf.io/preprints/psyarxiv/sd4xp, preprint on OSF},

doi = {10.1037/bul0000434},

year  = {2024},

date = {2024-08-28},

urldate = {2024-08-28},

journal = {Psychological Bulletin},

volume = {150},

issue = {8},

pages = {965-1003},

publisher = {PsyArXiv},

keywords = {hierarchical-Bayesian modeling, mathematical modeling, measurement models, Meta Analysis, MPT models, Statistics - Computation},

pubstate = {published},

tppubtype = {article}

}

@article{Lenneis2024,

title = {The Influence of Sleep on Subjective Well-Being: An Experience Sampling Study},

author = {Anita Lenneis and Ahuti Das-Friebel and Nicole K. Y. Tang and Adam N. Sanborn and Sakari Lemola and Henrik Singmann and Dieter Wolke and Adrian von Muhlenen and Anu Realo },

url = {http://singmann.org/download/publications/Lenneis%20et%20al.%20-%202024%20-%20The%20influence%20of%20sleep%20on%20subjective%20well-being%20A.pdf, journal PDF

http://singmann.org/download/publications/Lenneis-et-al.-Sleep-and-SWB-Emotion-Accepted-Version.pdf, accepted manuscript},

doi = {10.1037/emo0001268},

year  = {2024},

date = {2024-02-01},

urldate = {2024-06-15},

journal = {Emotion},

volume = {24},

number = {451-464},

issue = {2},

keywords = {emotion, mixed models},

pubstate = {published},

tppubtype = {article}

}

@article{newallNoEvidenceHarm2024a,

title = {`No Evidence of Harm' Implies No Evidence of Safety: Framing the Lack of Causal Evidence in Gambling Advertising Research},

author = {Philip Newall and Youssef Allami and Maira Andrade and Peter Ayton and Rosalind Baker-Frampton and Daniel Bennett and Matthew Browne and Christopher Bunn and Reece Bush-Evans and Sonia Chen and Sharon Collard and Steffi De Jans and Jeffrey Derevensky and Nicki A. Dowling and Simon Dymond and Andree Froude and Elizabeth Goyder and Robert M. Heirene and Nerilee Hing and Liselot Hudders and Kate Hunt and Richard J. E. James and En Li and Elliot A. Ludvig and Virve Marionneau and Ellen McGrane and Stephanie S. Merkouris and Jim Orford and Alberto Parrado-González and Robert Pryce and Matthew Rockloff and Ulla Romild and Raffaello Rossi and Alex M. T. Russell and Henrik Singmann and Trudy Smit Quosai and Sasha Stark and Aino Suomi and Thomas B. Swanton and Niri Talberg and Volker Thoma and Jamie Torrance and Catherine Tulloch and Ruth J. Holst and Lukasz Walasek and Heather Wardle and Jane West and Jamie Wheaton and Leon Y. Xiao and Matthew M. Young and Maria E. Bellringer and Steve Sharman and Amanda Roberts},

doi = {10.1111/add.16369},

issn = {1360-0443},

year  = {2024},

date = {2024-02-01},

urldate = {2024-01-01},

journal = {Addiction},

volume = {119},

number = {2},

pages = {391–396},

keywords = {Causal evidence, gambling advertising, gambling marketing, gambling-related harm, policy, regulation},

pubstate = {published},

tppubtype = {article}

}

@article{Nickson2023,

title = {Replicability and reproducibility of predictive models for diagnosis of depression among young adults using Electronic Health Records},

author = {David Nickson and Henrik Singmann and Caroline Meyer and Carla Toro and Lukasz Walasek},

url = {https://diagnprognres.biomedcentral.com/articles/10.1186/s41512-023-00160-2, open access publisher website

http://singmann.org/download/publications/Nickson-DAPR-Replication-accepted.docx, accepted manuscript},

doi = {10.1186/s41512-023-00160-2},

year  = {2023},

date = {2023-12-05},

urldate = {2024-10-10},

journal = {Diagnostic and Prognostic Research},

volume = {7},

pages = {25},

keywords = {applied, clinical, prediction, replication, Statistics - Computation},

pubstate = {published},

tppubtype = {article}

}

@article{Meyer-Grant2023b,

title = {Are Logical Intuitions Only Make-Believe? Reexamining the Logic-Liking Effect},

author = {Constantin G. Meyer-Grant and Nicole Cruz and Henrik Singmann and Samuel Winiger and Spriha Goswani and Brett K. Hayes and Karl Christoph Klauer},

url = {http://singmann.org/download/publications/Meyer-Grant%20et%20al.%20-%202022%20-%20Are%20logical%20intuitions%20only%20make-believe%20Reexamin.pdf, journal PDF

http://singmann.org/download/publications/Are_logical_intuitions_only_make-believe_clean.pdf, accepted manuscript

},

doi = {10.1037/xlm0001152},

year  = {2023},

date = {2023-08-01},

urldate = {2024-02-01},

journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},

volume = {49},

issue = {8},

pages = {1280-1305},

keywords = {Conditional reasoning, Intuition, Reasoning, syllogistic reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Statistics in the Service of Science: Don't let the Tail Wag the Dog},

author = {Henrik Singmann and Gregory Edward Cox and David Kellen and Suyog Chandramouli and Clintin Davis-Stober and John C Dunn and Quentin Frederik Gronau and Michael Kalish and Sara D McMullin and Danielle Navarro and Richard M Shiffrin},

url = {https://link.springer.com/content/pdf/10.1007/s42113-022-00129-2.pdf?pdf=button, publisher pdf

https://link.springer.com/article/10.1007/s42113-022-00129-2, publisher website

https://psyarxiv.com/kxhfu/download?format=pdf, accepted manuscript},

year  = {2023},

date = {2023-03-01},

urldate = {2023-04-01},

journal = {Computational Brain & Behavior},

volume = {6},

issue = {1},

pages = {64-83},

keywords = {Bayes Factor, Bayesian modelling, Statistics - Computation},

pubstate = {published},

tppubtype = {article}

}

@article{vanDoorn2023,

title = {Bayes Factors for Mixed Models: a Discussion},

author = {Johnny van Doorn and Julia M. Haaf and Angelika M. Stefan and Eric-Jan Wagenmakers and Gregory Edward Cox and Clintin P. Davis-Stober and Andrew Heathcote and Daniel W. Heck and Michael Kalish and David Kellen and Dora Matzke and Richard D. Morey and Bruno Nicenboim and Don van Ravenzwaaij and Jeffrey N. Rouder and Daniel J. Schad and Richard M. Shiffrin and Henrik Singmann and Shravan Vasishth and João Veríssimo and Florence Bockting and Suyog Chandramouli and John C. Dunn and Quentin F. Gronau and Maximilian Linde and Sara D. McMullin and Danielle Navarro and Martin Schnuerch and Himanshu Yadav and Frederik Aust },

url = {https://link.springer.com/content/pdf/10.1007/s42113-022-00160-3.pdf?pdf=button, publisher PDF

https://link.springer.com/article/10.1007/s42113-022-00160-3, publisher website},

year  = {2023},

date = {2023-03-01},

urldate = {2023-03-01},

journal = {Computational Brain & Behavior},

volume = {6},

issue = {1},

pages = {140-158},

keywords = {Bayes Factor, mixed models, Statistics - Computation},

pubstate = {published},

tppubtype = {article}

}

@article{Newall2022b,

title = {Impact of the" when the fun stops, stop" safer gambling message on online gambling behaviour: a randomised online experimental study},

author = {Philip WS Newall and Leonardo Weiss-Cohen and Henrik Singmann and Lukasz Walasek and Elliot A Ludvig},

url = {http://singmann.org/download/publications/Newall-et-al.-2022-Impact-of-the-when-the-fun-stops-stop-gambling-.pdf, published version},

doi = {10.1016/S2468-2667(21)00279-6},

year  = {2022},

date = {2022-04-27},

urldate = {2022-04-27},

journal = {The Lancet Public Health},

volume = {7},

issue = {5},

pages = {e437-e446},

keywords = {applied, Bayesian modelling, gambling},

pubstate = {published},

tppubtype = {article}

}

@article{Newall2022,

title = {A speed-of-play limit reduces gambling expenditure in an online roulette game: Results of an online experiment},

author = {Philip WS Newall and Leonardo Weiss-Cohen and Henrik Singmann and W Paul Boyce and Lukasz Walasek and Matthew J Rockloff},

url = {https://www.sciencedirect.com/science/article/pii/S0306460321004147, journal website (open access)},

doi = {10.1016/j.addbeh.2021.107229},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

journal = {Addictive Behaviors},

volume = {127},

pages = {107229},

keywords = {applied, Bayesian modelling, gambling},

pubstate = {published},

tppubtype = {article}

}

@article{Kominsky2021,

title = {The trajectory of counterfactual simulation in development},

author = {Jonathan Kominsky and Tobias Gerstenberg and Madeline Pelz and Mark Sheskin and Henrik Singmann and Laura Schulz and Frank C. Keil},

url = {http://singmann.org/download/publications/Kominsky-et-al.-2021-The-trajectory-of-counterfactual-simulation-in-dev.pdf, published version

http://singmann.org/download/publications/Kominsky_in-press_Tracing_counterfactual_development.pdf, accepted version

https://osf.io/5jw6y/, data, code, and materials (OSF)},

year  = {2021},

date = {2021-11-04},

journal = {Developmental Psychology},

volume = {57},

number = {2},

pages = {253-268},

keywords = {causality, measurement models, MPT models, Reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{Kellen2022,

title = {Testing the Foundations of Signal Detection Theory in Recognition Memory.},

author = {David Kellen and Samuel Winiger and John Dunn and Henrik Singmann },

url = {http://singmann.org/download/publications/kellen-2021-sdt.pdf, publisher PDF

https://psyarxiv.com/p5rj9/, accepted preprint

https://osf.io/zw9yr/, OSF link to data and scripts},

year  = {2021},

date = {2021-11-01},

urldate = {2021-11-01},

journal = {Psychological Review},

volume = {128},

number = {1022-1050},

issue = {6},

keywords = {mathematical modeling, Recognition memory, Signal detection},

pubstate = {published},

tppubtype = {article}

}

@article{Winiger2022,

title = {Bias in Confidence: A critical test for discrete-state models of visual working memory.},

author = {Samuel Winiger and Henrik Singmann and David Kellen},

url = {http://singmann.org/download/publications/Winiger-et-al.-2020-Bias-in-confidence-A-critical-test-for-discrete-s.pdf, Journal PDF

https://psyarxiv.com/6cmsp/, accepted preprint

https://osf.io/es2rw/, data, code, and materials (OSF)},

year  = {2021},

date = {2021-06-01},

journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},

volume = {47},

number = {3},

pages = {387-401},

keywords = {mathematical modeling, measurement models, memory, MPT models, Recognition memory, Signal detection},

pubstate = {published},

tppubtype = {article}

}

@article{Lenneis2022,

title = {Intraindividual Variability and Temporal Stability of Mid-Sleep on Free and Workdays},

author = {Anita Lenneis and Ahuti Das-Friebel and Henrik Singmann and Maris Teder-Laving and Sakari Lemola and Dieter Wolke and Nicole K. Y. Tang and Adrian von Mühlenen and Jüri Allik and

Anu Realo

},

url = {http://singmann.org/download/publications/Lenneis-et-al.-Intraindividual-Variability-and-Temporal-Stability.pdf, journal PDF

https://journals.sagepub.com/doi/10.1177/0748730420974842, journal website},

year  = {2021},

date = {2021-04-01},

journal = {Journal of Biological Rhythms},

volume = {36},

number = {2},

pages = {169-184},

keywords = {mixed models},

pubstate = {published},

tppubtype = {article}

}

@article{Stewart2022,

title = {Tracking the eye of the beholder: is explanation subjective?},

author = {Andrew J. Stewart and Henrik Singmann and Matthew Haigh and Jeffrey S. Wood and Igor Douven },

url = {http://singmann.org/download/publications/Stewart-et-al.-2021-Tracking-the-eye-of-the-beholder-is-explanation-s.pdf, Journal PDF},

year  = {2021},

date = {2021-04-01},

journal = {Journal of Cognitive Psychology},

volume = {33},

number = {2},

pages = {199-206},

keywords = {explanations, mixed models, Reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{Douven2021,

title = {Conditionals and Inferential Connections: Toward a New Semantics},

author = {Igor Douven and Shira Elqayam and Henrik Singmann and Janneke van Wijnbergen-Huitink},

url = {http://singmann.org/download/publications/Douven-et-al.-2020-Conditionals-and-inferential-connections-toward-a.pdf, Journal PDF

http://singmann.org/download/publications/Douven_et_al_Conditionals_and_Inferential_Connections_TR.pdf, Accepted Manuscript},

doi = {10.1080/13546783.2019.1619623},

year  = {2020},

date = {2020-07-02},

journal = {Thinking & Reasoning},

volume = {26},

number = {3},

pages = {311-351},

keywords = {Conditional reasoning, Reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{Baumann2021,

title = {A Linear Threshold Model for Optimal Stopping Behavior},

author = {Chrisitiane Baumann and Henrik Singmann and Samuel Gershman and Bettina von Helversen},

url = {http://singmann.org/download/publications/Baumann_Optimal_Stopping_PNAS_preprint.pdf, preprint},

year  = {2020},

date = {2020-06-09},

journal = {Proceedings of the National Academy of Sciences},

volume = {117},

number = {23},

pages = {12750-12755},

keywords = {Decision Making, hierarchical-Bayesian modeling, mathematical modeling, measurement models},

pubstate = {published},

tppubtype = {article}

}

@article{Gronau2020b,

title = {bridgesampling: An R Package for Estimating Normalizing Constants},

author = {Quentin F Gronau and Henrik Singmann and Eric-Jan Wagenmakers},

url = {https://www.jstatsoft.org/index.php/jss/article/view/v092i10/v92i10.pdf, published version

https://arxiv.org/pdf/1710.08162.pdf, preprint

http://arxiv.org/abs/1710.08162, on ArXiV},

doi = {10.18637/jss.v092.i10},

year  = {2020},

date = {2020-02-27},

urldate = {2018-09-25},

journal = {Journal of Statistical Software},

volume = {92},

number = {10},

abstract = {Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods). These normalizing constants are notoriously difficult to obtain, as they usually involve high-dimensional integrals that cannot be solved analytically. Here we introduce an R package that uses bridge sampling (Meng & Wong, 1996; Meng & Schilling, 2002) to estimate normalizing constants in a generic and easy-to-use fashion. For models implemented in Stan, the estimation procedure is automatic. We illustrate the functionality of the package with three examples.},

keywords = {hierarchical-Bayesian modeling, R, Software, Statistics - Computation},

pubstate = {published},

tppubtype = {article}

}

@article{Starns2020,

title = {Assessing theoretical conclusions with blinded inference to investigate a potential inference crisis},

author = {Jeffrey J. Starns and Andrea M. Cataldo and Caren M. Rotello and Jeffrey Annis and Andrew Aschenbrenner and Arndt Bröder and Gregory Cox and Amy Criss and Ryan A. Curl and Ian G. Dobbins and John Dunn and Tasnuva Enam and Nathan J. Evans and Simon Farrell and Scott H. Fraundorf and Scott D. Gronlund and Andrew Heathcote and Daniel W. Heck and Jason L. Hicks and Mark J. Huff and David Kellen and Kylie N. Key and Asli Kilic and Karl Christoph Klauer and Kyle R. Kraemer and Fábio P. Leite and Marianne E. Lloyd and Simone Malejka and Alice Mason and Ryan M. McAdoo and Ian M. McDonough and Robert B. Michael and Laura Mickes and Eda Mizrak and David P. Morgan and Shane T. Mueller and Adam Osth and Angus Reynolds and Travis M. Seale-Carlisle and Henrik Singmann and Jennifer F. Sloane and Andrew M. Smith and Gabriel Tillman and Don van Ravenzwaaij and Christoph T. Weidemann and Gary L. Wells and Corey N. White and Jack Wilson},

url = {http://singmann.org/download/publications/Starns-et-al.-2019-Assessing-Theoretical-Conclusions-With-Blinded-Inf.pdf, published version

http://singmann.org/download/publications/AMPPS_manuscript_revision2_Submitted.docx, accepted manuscript},

year  = {2019},

date = {2019-12-01},

journal = {Advances in Methods and Practices in Psychological Science},

volume = {2},

number = {4},

pages = {335-349},

keywords = {mathematical modeling, measurement models, memory, model selection, Recognition memory},

pubstate = {published},

tppubtype = {article}

}

@article{Niklaus2019,

title = {Two Distinct Mechanisms of Selection in Working Memory: Additive Last-Item and Retro-Cue Benefits},

author = {Marcel Niklaus and Henrik Singmann and Klaus Oberauer},

url = {http://singmann.org/download/publications/Niklaus-et-al.-2019-Two-distinct-mechanisms-of-selection-in-working-me.pdf, published manuscript

https://psyarxiv.com/bcav6/, PsyArXiv preprint},

year  = {2019},

date = {2019-11-30},

journal = {Cognition},

volume = {183},

pages = {282-302},

keywords = {mathematical modeling, measurement models, Signal detection, working memory},

pubstate = {published},

tppubtype = {article}

}

@article{Trippas2018,

title = {Characterizing Belief Bias in Syllogistic Reasoning: A Hierarchical-Bayesian Meta-Analysis of ROC Data},

author = {Dries Trippas and David Kellen and Henrik Singmann and Gordon Pennycook and Derek J. Koehler and Jonathan A. Fugelsang and Chad Dubé},

url = {http://singmann.org/download/publications/Trippas-et-al.-2018-Characterizing-belief-bias-in-syllogistic-reasonin.pdf, published version

http://singmann.org/download/publications/trippas_kellen_singmann_et_al_submitted_online.pdf, accepted manuscript

https://osf.io/8dfyv/, data and modeling code},

year  = {2018},

date = {2018-12-01},

journal = {Psychonomic Bulletin & Review},

volume = {25},

number = {6},

pages = {2141–2174},

keywords = {hierarchical-Bayesian modeling, mathematical modeling, measurement models, Meta Analysis, Reasoning, Signal detection, syllogistic reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{Boehm2018,

title = {Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations},

author = {Udo Boehm and Jeff Annis and Michael Frank and Guy Hawkins and Andrew Heathcote and David Kellen and Angelos-Miltiadis Krypotos and Veronika Lerche and Gordon D Logan and Thomas Palmeri and Don van Ravenzwaaij and Mathieu Servant and Henrik Singmann and Jeffrey Starns and Andreas Voss and Thomas Wiecki and Dora Matzke and Eric-Jan Wagenmakers},

url = {https://psyarxiv.com/km28u/, preprint on OSF},

doi = {10.31234/osf.io/km28u},

year  = {2018},

date = {2018-12-01},

urldate = {2018-09-25},

journal = {Journal of Mathematical Psychology},

volume = {87},

pages = {46-75},

abstract = {For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM’s success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM’s between-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM’s across-trial variability parameters.},

keywords = {Diffusion model, mathematical modeling, measurement models, Software},

pubstate = {published},

tppubtype = {article}

}

@article{Kellen2018b,

title = {Classic-Probability Accounts of  Mirrored (Quantum-Like) Order Effects in Human Judgments},

author = {David Kellen and Henrik Singmann and William H. Batchelder },

url = {http://singmann.org/download/publications/kellen_singmann_batchelder_inpress_decision.pdf, publisher version

http://singmann.org/download/publications/kellen_singmann_batchelder_classic-probability-accounts_in_press.pdf, accepted manuscript},

year  = {2018},

date = {2018-10-01},

journal = {Decision},

volume = {5},

number = {4},

pages = {323-338},

keywords = {mathematical modeling, measurement models, quantum cognition},

pubstate = {published},

tppubtype = {article}

}

@article{Bartsch2018,

title = {The Effects of Refreshing and Elaboration on Working Memory Performance, and their Contributions to Long-Term Memory Formation},

author = {Lea Bartsch and Henrik Singmann and Klaus Oberauer},

url = {http://singmann.org/download/publications/Bartsch-et-al.-2018-The-effects-of-refreshing-and-elaboration-on-worki.pdf, publisher version

https://osf.io/weuc2/, data and analysis code},

year  = {2018},

date = {2018-07-01},

journal = {Memory & Cognition},

volume = {46},

number = {5},

pages = {796-808},

keywords = {hierarchical-Bayesian modeling, working memory},

pubstate = {published},

tppubtype = {article}

}

@article{Markovits2018,

title = {Multiple Layers of Information Processing in Deductive Reasoning: Combining Dual Strategy and Dual-Source Approaches to Reasoning},

author = {Henry Markovits and Janie Brisson and Pier-Luc de Chantal and Henrik Singmann},

url = {http://singmann.org/download/publications/Markovits-et-al.-2018-Multiple-layers-of-information-processing-in-deduc.pdf, published manuscript},

year  = {2018},

date = {2018-06-30},

journal = {Journal of Cognitive Psychology},

volume = {30},

number = {4},

pages = {394-405},

keywords = {Conditional reasoning, Probabilistic reasoning, Reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{Douven2017,

title = {Conditionals and Inferential Connections: A Hypothetical Inferential Theory},

author = {Igor Douven and Shira Elqayam and Henrik Singmann and Janneke van Wijnbergen-Huitink},

url = {http://singmann.org/download/publications/Douven_Elqayam_Singmann_Wijnbergen-Huitink-HIT_in_press.pdf, accepted manuscript

https://osf.io/3uajq/, OSF link (includes all data and code)},

year  = {2018},

date = {2018-03-01},

journal = {Cognitive Psychology},

volume = {101},

pages = {50-81},

keywords = {Conditional reasoning, Reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{Mizrak2018,

title = {Forgetting Emotional Material in Working Memory},

author = {Eda Mizrak and Henrik Singmann and Ilke Öztekin},

url = {https://academic.oup.com/scan/article-pdf/13/3/331/24238824/nsx145.pdf, free publisher pdf

https://academic.oup.com/scan/article/13/3/331/4767720, publisher website},

year  = {2018},

date = {2018-03-01},

journal = {Social Cognitive and Affective Neuroscience},

volume = {13},

number = {3},

pages = {331–340},

keywords = {Diffusion model, emotion, measurement models, memory, neuro, working memory},

pubstate = {published},

tppubtype = {article}

}

@article{Kellen2017,

title = {Memory Representations, Tree Structures, and Parameter Polysemy: Comment on Cooper, Greve, and Henson (2017)},

author = {David Kellen and Henrik Singmann},

url = {http://singmann.org/download/publications/kellen_singmann-memory-representations-tree_in-press.pdf, accepted manuscript},

year  = {2017},

date = {2017-11-01},

journal = {Cortex},

volume = {96},

pages = {148-155},

keywords = {mathematical modeling, measurement models, MPT models, source memory},

pubstate = {published},

tppubtype = {article}

}

@article{Olsen2017,

title = {Relevance and Reason Relations},

author = {Niels Skovgaard-Olsen and Henrik Singmann and Karl Christoph Klauer},

url = {http://singmann.org/download/publications/Skovgaard-Olsen-et-al.-2016-Relevance-and-Reason-Relations.pdf, publisher version

http://singmann.org/download/publications/submitted/Final-Relevance-and-Reason-Relations.pdf, accepted version

https://osf.io/fdbq2/, OSF link (contains all data and code)

http://singmann.org/download/publications/supplemental/Sup_Mat_Relevance-and-Reason-Relations.pdf, supplemental materials},

year  = {2017},

date = {2017-05-01},

journal = {Cognitive Science},

volume = {41},

pages = {1202-1215},

keywords = {Conditional reasoning, mixed models, new paradigm psychology of reasoning},

pubstate = {published},

tppubtype = {article}

}