Peer Reviewed Publications

The raw data and analysis scripts are available for many papers, at least for those papers where I was first author. Just click on “Links”.

Citation information can be found at my Google Scholar profile.

Submitted manuscripts (i.e., under review or in revision)

He, Y., Kellen, D., & Singmann, H. (submitted). A Critical Test of Signal Detection Theory in Visual Working Memory.

Publications

Note: Publications “in press” are displayed as published in the upcoming year.

74 entries « ‹ 1 of 2 › »

2027
74.	Meyer-Grant, Constantin G.; Kellen, David; Harding, Samuel; Singmann, Henrik: Extreme-Value Signal Detection Theory for Recognition Memory: The Parametric Road Not Taken. In: Psychological Review, Forthcoming. (Type: Journal Article \| Abstract \| Links \| BibTeX \| Tags: critical tests, extreme value distributions, invariance properties, Recognition memory, signal detection theory) @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} } Close 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. Close preprint link Close
2026
73.	Deans-Browne, Calvin; Singmann, Henrik: For Everyday Arguments Prior Beliefs Play a Larger Role on Perceived Argument Quality than Argument Quality Itself. In: vol. 266, pp. 106257, 2026, ISSN: 0010-0277. (Type: Journal Article \| Abstract \| Links \| BibTeX \| Tags: Argument evaluation, Argument quality, Belief-based reasoning, Everyday reasoning, Informal reasoning) @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} } Close 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. Close publisher PDF publisher website doi:10.1016/j.cognition.2025.106257 Close
2025
72.	Liu, Xiaotong; Bröder, Arndt; Singmann, Henrik: Evaluating the Role of Mental Sampling in Probability Judgments: Illogical Rankings Occur in a Predictable Manner. In: Cognition, vol. 263, pp. 106125, 2025. (Type: Journal Article \| Abstract \| Links \| BibTeX \| Tags: mental sampling, Probabilistic reasoning, rationality) @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} } Close 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. Close publisher PDF preprint doi:10.1016/j.cognition.2025.106125 Close
71.	Newall, Philip WS; Hayes, Ty; and Henrik Singmann,; Weiss-Cohen, Leonardo; Walasek, Lukasz; Ludvig, Elliot A: Evaluation of the 'take time to think' safer gambling message: a randomised, online experimental study. In: Behavioural Public Policy, vol. 9, iss. 4, pp. 762-779, 2025. (Type: Journal Article \| Links \| BibTeX \| Tags: applied, gambling) @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} } Close publisher website (open access) doi:10.1017/bpp.2023.2 Close
70.	Kellen, David; Meyer-Grant, Constantin G.; Singmann, Henrik; Klauer, Karl Christoph: Critical Testing in Recognition Memory: Selective Influence, Single-Item Generalization, and the High-Threshold Hypothesis. In: Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 51, iss. 8, pp. 1259-1280, 2025. (Type: Journal Article \| Links \| BibTeX \| Tags: discrete states, mathematical modeling, measurement models, memory, Recognition memory, Signal detection) @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} } Close publisher PDF accepted preprint code on OSF PsyArXiv preprint Close
69.	Deans-Browne, Calvin Christopher James Lee; Roth, Pia; Echterbeck, Carolin; Singmann, Henrik: Differential Memory for Belief-Congruent versus Belief-Incongruent Arguments Cannot Explain Belief-Driven Argument Evaluation. In: Proceedings of the Annual Meeting of the Cognitive Science Society, 2025. (Type: Proceedings Article \| Abstract \| Links \| BibTeX \| Tags: Belief bias, Belief-based reasoning, Everyday reasoning, Informal reasoning, real-world reasoning, Reasoning, Recognition memory) @inproceedings{deans-browneDifferentialMemoryBeliefCongruent2025b, title = {Differential Memory for Belief-Congruent versus Belief-Incongruent Arguments Cannot Explain Belief-Driven Argument Evaluation}, author = {Calvin Christopher James Lee Deans-Browne and Pia Roth and Carolin Echterbeck and Henrik Singmann}, url = {https://escholarship.org/uc/item/5nn3w7xz, CogSci Proceedings link}, year = {2025}, date = {2025-07-27}, urldate = {2025-01-01}, booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society}, volume = {47}, abstract = {People often rely more on their prior beliefs than the presented evidence when evaluating arguments. We investigate the cognitive mechanisms underlying this phenomenon. We hypothesise that when individuals encounter an argument that is congruent with their beliefs, it activates related information in memory. For belief-congruent arguments, people should therefore be more likely to both correctly recognise previously encountered information and incorrectly recognise new information as previously seen. To test this, we first investigated the effect of participants' beliefs about political claims on their evaluation of corresponding arguments that varied in quality. We then employed a surprise memory test to assess participants' recognition memory for these arguments. While we replicated the finding that prior beliefs drive argument evaluations, prior beliefs did not affect memory performance for all arguments in the same way. Our results indicate that individuals may use prior beliefs to aid memory only when the memory task is difficult.}, keywords = {Belief bias, Belief-based reasoning, Everyday reasoning, Informal reasoning, real-world reasoning, Reasoning, Recognition memory}, pubstate = {published}, tppubtype = {inproceedings} } Close People often rely more on their prior beliefs than the presented evidence when evaluating arguments. We investigate the cognitive mechanisms underlying this phenomenon. We hypothesise that when individuals encounter an argument that is congruent with their beliefs, it activates related information in memory. For belief-congruent arguments, people should therefore be more likely to both correctly recognise previously encountered information and incorrectly recognise new information as previously seen. To test this, we first investigated the effect of participants' beliefs about political claims on their evaluation of corresponding arguments that varied in quality. We then employed a surprise memory test to assess participants' recognition memory for these arguments. While we replicated the finding that prior beliefs drive argument evaluations, prior beliefs did not affect memory performance for all arguments in the same way. Our results indicate that individuals may use prior beliefs to aid memory only when the memory task is difficult. Close CogSci Proceedings link Close
68.	Gong, Tianwei; Hou, Yining; Singmann, Henrik; Bramley, Neil R.: Identifying "when" and "whether" causation: How people distinguish generation, hastening, prevention, and delay . In: Proceedings of the Annual Meeting of the Cognitive Science Society, 2025. (Type: Proceedings Article \| Abstract \| Links \| BibTeX \| Tags: causal reasoning, causality, Reasoning) @inproceedings{gongIdentifyingWhenWhether2025b, title = {Identifying "when" and "whether" causation: How people distinguish generation, hastening, prevention, and delay }, author = {Tianwei Gong and Yining Hou and Henrik Singmann and Neil R. Bramley}, url = {https://escholarship.org/uc/item/8kj385bv, CogSci Proceedings link}, year = {2025}, date = {2025-07-27}, urldate = {2025-01-01}, booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society}, volume = {47}, abstract = {Causal relationships in the real world can have diverse mechanisms with differing statistical signatures. We investigate whether people can distinguish between causes that merely change the timing of events ("when" causes) and those that bring about or prevent those events ("whether" causes). We designed experiments where the rate of an event varies over time due to one such causal influence. Events were shown in real time in Experiment 1 and as a timeline visualization in Experiment 2. Our results suggest that people are capable of identifying "when" and "whether" causes but with a distinctive pattern of confusability: People confuse Generation with Hastening; and Prevention with Delaying. We develop a Causal Abstraction from Summarizing Events (CASE) model, which explains people's judgments as mediated by their rate-change-event detection. We discuss how this line of research can be extended to study human cognition about dynamic causal influences and its relevance to real-life judgment and decision-making.}, keywords = {causal reasoning, causality, Reasoning}, pubstate = {published}, tppubtype = {inproceedings} } Close Causal relationships in the real world can have diverse mechanisms with differing statistical signatures. We investigate whether people can distinguish between causes that merely change the timing of events ("when" causes) and those that bring about or prevent those events ("whether" causes). We designed experiments where the rate of an event varies over time due to one such causal influence. Events were shown in real time in Experiment 1 and as a timeline visualization in Experiment 2. Our results suggest that people are capable of identifying "when" and "whether" causes but with a distinctive pattern of confusability: People confuse Generation with Hastening; and Prevention with Delaying. We develop a Causal Abstraction from Summarizing Events (CASE) model, which explains people's judgments as mediated by their rate-change-event detection. We discuss how this line of research can be extended to study human cognition about dynamic causal influences and its relevance to real-life judgment and decision-making. Close CogSci Proceedings link Close
67.	Rey-Mermet, Alodie; Singmann, Henrik; Oberauer, Klaus: 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. In: Psychonomic Bulletin & Review, vol. 32, pp. 2585–2632, 2025. (Type: Journal Article \| Abstract \| Links \| BibTeX \| Tags: Diffusion model, executive functions, hierarchical-Bayesian modeling, individual differences) @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} } Close 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. Close publisher PDF preprint doi:10.3758/s13423-025-02696-4 Close
66.	Maier, Maximilian; Harris, Adam J. L.; Kellen, David; Singmann, Henrik: Decision Making under Extinction Risk. In: vol. 159, pp. 101735, 2025, ISSN: 0010-0285. (Type: Journal Article \| Abstract \| Links \| BibTeX \| Tags: Computational modelling, Decision Making, Extinction, Extreme outcomes, Mixture modelling, Risky choice) @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} } Close 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. Close https://www.sciencedirect.com/science/article/pii/S0010028525000234 doi:10.1016/j.cogpsych.2025.101735 Close
65.	Boag, Russell J.; Innes, Reilly J.; Stevenson, Niek; Bahg, Giwon; Busemeyer, Jerome R.; Cox, Gregory E.; Donkin, Chris; Frank, Michael J.; Hawkins, Guy E.; Heathcote, Andrew; Hedge, Craig; Lerche, Veronika; Lilburn, Simon D.; Logan, Gordon D.; Matzke, Dora; Miletić, Steven; Osth, Adam F.; Palmeri, Thomas J.; Sederberg, Per B.; Singmann, Henrik; Smith, Philip L.; Stafford, Tom; Steyvers, Mark; Strickland, Luke; Trueblood, Jennifer S.; Tsetsos, Konstantinos; Turner, Brandon M.; Usher, Marius; van Maanen, Leendert; van Ravenzwaaij, Don; Vandekerckhove, Joachim; Voss, Andreas; Weichart, Emily R.; Weindel, Gabriel; White, Corey N.; Evans, Nathan J.; Brown, Scott D.; Forstmann, Birte U.: An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling. In: vol. 8, no. 2, pp. 25152459251336127, 2025, ISSN: 2515-2459. (Type: Journal Article \| Abstract \| Links \| BibTeX \| Tags: ) @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} } Close 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. Close https://doi.org/10.1177/25152459251336127 doi:10.1177/25152459251336127 Close
2024
64.	Niu, Xiaoxiao; Singmann, Henrik; Wyatt, Faye; Putra, Agie W.; Taat, Azlai; Panti, Jehan S.; Hoang, Lam; Moron, Lorenzo A.; Osman, Sazali; Novikarany, Riefda; Tran, Diep Quang; Beckett, Rebecca; Harris, Adam JL.: Judgment and Decision Strategies Used by Weather Scientists in Southeast Asia to Classify Impact Severity. In: vol. 113, pp. 104799, 2024, ISSN: 2212-4209. (Type: Journal Article \| Links \| BibTeX \| Tags: applied, Decision Making) @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} } Close Publisher PDF doi:10.1016/j.ijdrr.2024.104799 Close
63.	Singmann, Henrik; Heck, Daniel W; Barth, Marius; Erdfelder, Edgar; Arnold, Nina R; Aust, Frederik; Calanchini, Jimmy; Gümüsdagli, Fabian E; Horn, Sebastian S; Kellen, David; Klauer, Karl C.; Matzke, Dora; Meissner, Franziska; Michalkiewicz, Martha; Schaper, Marie Luisa; Stahl, Christoph; Kuhlmann, Beatrice G.; Groß, Julia: Evaluating the Robustness of Parameter Estimates in Cognitive Models: A Meta-Analytic Review of Multinomial Processing Tree Models Across the Multiverse of Estimation Methods. In: Psychological Bulletin, vol. 150, iss. 8, pp. 965-1003, 2024. (Type: Journal Article \| Links \| BibTeX \| Tags: hierarchical-Bayesian modeling, mathematical modeling, measurement models, Meta Analysis, MPT models, Statistics - Computation) @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} } Close journal PDF accepted manuscript preprint on OSF doi:10.1037/bul0000434 Close
62.	Chen, Tianshu; Bechlivanidis, Christos; Singmann, Henrik; Lagnado, David: The Attraction of Anticipation: How Causal Interactions Draw People’s Attention in Visual Tasks. In: Samuelson, Larissa K; Frank, Stefan; Toneva, Mariya; Mackey, Allyson P.; Hazeltine, Eliot (Ed.): Proceedings of the 46th Annual Conference of the Cognitive Science Society, 2024. (Type: Proceedings Article \| Links \| BibTeX \| Tags: causality, MPT models, perception) @inproceedings{chenAttractionAnticipationHow2024, title = {The Attraction of Anticipation: How Causal Interactions Draw People’s Attention in Visual Tasks}, author = {Tianshu Chen and Christos Bechlivanidis and Henrik Singmann and David Lagnado}, editor = {Larissa K Samuelson and Stefan Frank and Mariya Toneva and Allyson P. Mackey and Eliot Hazeltine}, url = {http://singmann.org/download/publications/Chen%20et%20al.%20-%202024%20-%20The%20attraction%20of%20anticipation%20How%20causal%20interac.pdf, final PDF}, year = {2024}, date = {2024-07-24}, urldate = {2025-02-01}, booktitle = {Proceedings of the 46th Annual Conference of the Cognitive Science Society}, keywords = {causality, MPT models, perception}, pubstate = {published}, tppubtype = {inproceedings} } Close final PDF Close
61.	Deans-Browne, Calvin; Băitanu, Alexandra; Dubinska, Yuliya; Singmann, Henrik: Inconsistent Arguments Are Perceived as Better Than Appeals to Authority: An Extension of the Everyday Belief Bias. In: Samuelson, Larissa K; Frank, Stefan; Toneva, Mariya; Mackey, Allyson P.; Hazeltine, Eliot (Ed.): Proceedings of the 46th Annual Conference of the Cognitive Science Society, 2024. (Type: Proceedings Article \| Abstract \| Links \| BibTeX \| Tags: Belief bias, real-world reasoning, Reasoning) @inproceedings{deans-browneInconsistentArgumentsAre2024, title = {Inconsistent Arguments Are Perceived as Better Than Appeals to Authority: An Extension of the Everyday Belief Bias}, author = {Calvin Deans-Browne and Alexandra Băitanu and Yuliya Dubinska and Henrik Singmann}, editor = {Larissa K Samuelson and Stefan Frank and Mariya Toneva and Allyson P. Mackey and Eliot Hazeltine}, url = {http://singmann.org/download/publications/Deans-Browne%20et%20al.%20-%202024%20-%20Inconsistent%20Arguments%20are%20Perceived%20as%20Better%20Tha.pdf, final PDF https://osf.io/j2xn3, OSF link}, doi = {10.31234/osf.io/j2xn3}, year = {2024}, date = {2024-07-24}, urldate = {2025-01-15}, booktitle = {Proceedings of the 46th Annual Conference of the Cognitive Science Society}, abstract = {Social media is often used as a platform where individuals engage in debate regarding topics that are important to them. 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. In a replication of our Everyday Belief Bias Task (Deans-Browne & Singmann, 2024), we investigate this question using a paradigm that consists of two parts. In the first part, we measure participant’s individual beliefs about eight claims each referring to a political topic (e.g., Abortion should be legal). In the second part, participants rated an argument for each of these claims that was deemed as either good, inconsistent (containing internal inconsistencies), or authority-based (being centered around appeals to authority). We replicated the belief consistency effect – participants preferred arguments that were also in line with their beliefs. We also found that authority-based arguments were rated as worse than inconsistent arguments, and that both types of arguments were rated as worse than good arguments. The implications are first that people do not evaluate arguments independently of the background beliefs held about them. Secondly, people are willing to ignore inconsistencies in arguments more than they are willing to accept the endorsement of authority figures as adequate evidence for arguments.}, keywords = {Belief bias, real-world reasoning, Reasoning}, pubstate = {published}, tppubtype = {inproceedings} } Close Social media is often used as a platform where individuals engage in debate regarding topics that are important to them. 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. In a replication of our Everyday Belief Bias Task (Deans-Browne & Singmann, 2024), we investigate this question using a paradigm that consists of two parts. In the first part, we measure participant’s individual beliefs about eight claims each referring to a political topic (e.g., Abortion should be legal). In the second part, participants rated an argument for each of these claims that was deemed as either good, inconsistent (containing internal inconsistencies), or authority-based (being centered around appeals to authority). We replicated the belief consistency effect – participants preferred arguments that were also in line with their beliefs. We also found that authority-based arguments were rated as worse than inconsistent arguments, and that both types of arguments were rated as worse than good arguments. The implications are first that people do not evaluate arguments independently of the background beliefs held about them. Secondly, people are willing to ignore inconsistencies in arguments more than they are willing to accept the endorsement of authority figures as adequate evidence for arguments. Close final PDF OSF link doi:10.31234/osf.io/j2xn3 Close
60.	Maier, Maximilian; Harris, Adam; Kellen, David; Singmann, Henrik: Introducing the Extinction Gambling Task. In: Samuelson, Larissa K; Frank, Stefan; Toneva, Mariya; Mackey, Allyson P.; Hazeltine, Eliot (Ed.): Proceedings of the 46th Annual Conference of the Cognitive Science Society, OSF, 2024. (Type: Proceedings Article \| Abstract \| Links \| BibTeX \| Tags: Decision Making, optimality, risky-choice) @inproceedings{maierIntroducingExtinctionGambling2024, title = {Introducing the Extinction Gambling Task}, author = {Maximilian Maier and Adam Harris and David Kellen and Henrik Singmann}, editor = {Larissa K Samuelson and Stefan Frank and Mariya Toneva and Allyson P. Mackey and Eliot Hazeltine}, url = {http://singmann.org/download/publications/Maier%20et%20al.%20-%202024%20-%20Introducing%20the%20Extinction%20Gambling%20Task.pdf, final PDF https://osf.io/g3s7u, OSF link }, doi = {10.31234/osf.io/g3s7u}, year = {2024}, date = {2024-07-24}, urldate = {2025-01-30}, booktitle = {Proceedings of the 46th Annual Conference of the Cognitive Science Society}, publisher = {OSF}, abstract = {Decisions about extinction risks are ubiquitous in everyday life and for our continued existence as a species. We introduce a new risky-choice task that can be used to study this topic: The Extinction Gambling Task. Here, we investigate two versions of this task: a Keep variant, where participants cannot accumulate any more earnings after the extinction event, and a Lose variant, where extinction also wipes out all previous earnings. We derive optimal solutions for both variants and compare them to behavioural data. Our findings suggest that people understand the difference between the two variants and their behaviour is qualitatively in line with the optimal solution. Further, we find evidence for risk-aversion in the Keep condition but not in the Lose condition. We hope that this task can facilitate further research on this vital topic.}, keywords = {Decision Making, optimality, risky-choice}, pubstate = {published}, tppubtype = {inproceedings} } Close Decisions about extinction risks are ubiquitous in everyday life and for our continued existence as a species. We introduce a new risky-choice task that can be used to study this topic: The Extinction Gambling Task. Here, we investigate two versions of this task: a Keep variant, where participants cannot accumulate any more earnings after the extinction event, and a Lose variant, where extinction also wipes out all previous earnings. We derive optimal solutions for both variants and compare them to behavioural data. Our findings suggest that people understand the difference between the two variants and their behaviour is qualitatively in line with the optimal solution. Further, we find evidence for risk-aversion in the Keep condition but not in the Lose condition. We hope that this task can facilitate further research on this vital topic. Close final PDF OSF link doi:10.31234/osf.io/g3s7u Close
59.	Singmann, Henrik; Xiong, Yunxi; Song, Yue; Breen, Mia; Baumann, Christiane: Full-Information Optimal-Stopping Problems: Providing People with the Optimal Policy Does Not Improve Performance. In: Samuelson, Larissa K; Frank, Stefan; Toneva, Mariya; Mackey, Allyson P.; Hazeltine, Eliot (Ed.): Proceedings of the 46th Annual Conference of the Cognitive Science Society, 2024. (Type: Proceedings Article \| Links \| BibTeX \| Tags: Decision Making, optimal stopping, optimality) @inproceedings{singmannFullinformationOptimalstoppingProblems2024, title = {Full-Information Optimal-Stopping Problems: Providing People with the Optimal Policy Does Not Improve Performance}, author = {Henrik Singmann and Yunxi Xiong and Yue Song and Mia Breen and Christiane Baumann}, editor = {Larissa K Samuelson and Stefan Frank and Mariya Toneva and Allyson P. Mackey and Eliot Hazeltine}, url = {http://singmann.org/download/publications/Singmann%20et%20al.%20-%202024%20-%20Full-information%20optimal-stopping%20problems%20Provid.pdf, final PDF}, year = {2024}, date = {2024-07-24}, urldate = {2025-01-31}, booktitle = {Proceedings of the 46th Annual Conference of the Cognitive Science Society}, keywords = {Decision Making, optimal stopping, optimality}, pubstate = {published}, tppubtype = {inproceedings} } Close final PDF Close
58.	Lenneis, Anita; Das-Friebel, Ahuti; Tang, Nicole K. Y.; Sanborn, Adam N.; Lemola, Sakari; Singmann, Henrik; Wolke, Dieter; von Muhlenen, Adrian; Realo, Anu: The Influence of Sleep on Subjective Well-Being: An Experience Sampling Study. In: Emotion, vol. 24, iss. 2, no. 451-464, 2024. (Type: Journal Article \| Links \| BibTeX \| Tags: emotion, mixed models) @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} } Close journal PDF accepted manuscript doi:10.1037/emo0001268 Close
57.	Newall, Philip; Allami, Youssef; Andrade, Maira; Ayton, Peter; Baker-Frampton, Rosalind; Bennett, Daniel; Browne, Matthew; Bunn, Christopher; Bush-Evans, Reece; Chen, Sonia; Collard, Sharon; Jans, Steffi De; Derevensky, Jeffrey; Dowling, Nicki A.; Dymond, Simon; Froude, Andree; Goyder, Elizabeth; Heirene, Robert M.; Hing, Nerilee; Hudders, Liselot; Hunt, Kate; James, Richard J. E.; Li, En; Ludvig, Elliot A.; Marionneau, Virve; McGrane, Ellen; Merkouris, Stephanie S.; Orford, Jim; Parrado-González, Alberto; Pryce, Robert; Rockloff, Matthew; Romild, Ulla; Rossi, Raffaello; Russell, Alex M. T.; Singmann, Henrik; Quosai, Trudy Smit; Stark, Sasha; Suomi, Aino; Swanton, Thomas B.; Talberg, Niri; Thoma, Volker; Torrance, Jamie; Tulloch, Catherine; Holst, Ruth J.; Walasek, Lukasz; Wardle, Heather; West, Jane; Wheaton, Jamie; Xiao, Leon Y.; Young, Matthew M.; Bellringer, Maria E.; Sharman, Steve; Roberts, Amanda: `No Evidence of Harm' Implies No Evidence of Safety: Framing the Lack of Causal Evidence in Gambling Advertising Research. In: Addiction, vol. 119, no. 2, pp. 391–396, 2024, ISSN: 1360-0443. (Type: Journal Article \| Links \| BibTeX \| Tags: Causal evidence, gambling advertising, gambling marketing, gambling-related harm, policy, regulation) @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} } Close doi:10.1111/add.16369 Close
2023
56.	Nickson, David; Singmann, Henrik; Meyer, Caroline; Toro, Carla; Walasek, Lukasz: Replicability and reproducibility of predictive models for diagnosis of depression among young adults using Electronic Health Records. In: Diagnostic and Prognostic Research, vol. 7, pp. 25, 2023. (Type: Journal Article \| Links \| BibTeX \| Tags: applied, clinical, prediction, replication, Statistics - Computation) @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} } Close open access publisher website accepted manuscript doi:10.1186/s41512-023-00160-2 Close
55.	Meyer-Grant, Constantin G.; Cruz, Nicole; Singmann, Henrik; Winiger, Samuel; Goswani, Spriha; Hayes, Brett K.; Klauer, Karl Christoph: Are Logical Intuitions Only Make-Believe? Reexamining the Logic-Liking Effect. In: Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 49, iss. 8, pp. 1280-1305, 2023. (Type: Journal Article \| Links \| BibTeX \| Tags: Conditional reasoning, Intuition, Reasoning, syllogistic reasoning) @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} } Close journal PDF accepted manuscript doi:10.1037/xlm0001152 Close
54.	Singmann, Henrik; Cox, Gregory Edward; Kellen, David; Chandramouli, Suyog; Davis-Stober, Clintin; Dunn, John C; Gronau, Quentin Frederik; Kalish, Michael; McMullin, Sara D; Navarro, Danielle; Shiffrin, Richard M: Statistics in the Service of Science: Don't let the Tail Wag the Dog. In: Computational Brain & Behavior, vol. 6, iss. 1, pp. 64-83, 2023. (Type: Journal Article \| Links \| BibTeX \| Tags: Bayes Factor, Bayesian modelling, Statistics - Computation) @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} } Close publisher pdf publisher website accepted manuscript Close
53.	van Doorn, Johnny; Haaf, Julia M.; Stefan, Angelika M.; Wagenmakers, Eric-Jan; Cox, Gregory Edward; Davis-Stober, Clintin P.; Heathcote, Andrew; Heck, Daniel W.; Kalish, Michael; Kellen, David; Matzke, Dora; Morey, Richard D.; Nicenboim, Bruno; van Ravenzwaaij, Don; Rouder, Jeffrey N.; Schad, Daniel J.; Shiffrin, Richard M.; Singmann, Henrik; Vasishth, Shravan; Veríssimo, João; Bockting, Florence; Chandramouli, Suyog; Dunn, John C.; Gronau, Quentin F.; Linde, Maximilian; McMullin, Sara D.; Navarro, Danielle; Schnuerch, Martin; Yadav, Himanshu; Aust, Frederik: Bayes Factors for Mixed Models: a Discussion. In: Computational Brain & Behavior, vol. 6, iss. 1, pp. 140-158, 2023. (Type: Journal Article \| Links \| BibTeX \| Tags: Bayes Factor, mixed models, Statistics - Computation) @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} } Close publisher PDF publisher website Close
2022
52.	Newall, Philip WS; Weiss-Cohen, Leonardo; Singmann, Henrik; Walasek, Lukasz; Ludvig, Elliot A: Impact of the" when the fun stops, stop" safer gambling message on online gambling behaviour: a randomised online experimental study. In: The Lancet Public Health, vol. 7, iss. 5, pp. e437-e446, 2022. (Type: Journal Article \| Links \| BibTeX \| Tags: applied, Bayesian modelling, gambling) @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} } Close published version doi:10.1016/S2468-2667(21)00279-6 Close
51.	Newall, Philip WS; Weiss-Cohen, Leonardo; Singmann, Henrik; Boyce, W Paul; Walasek, Lukasz; Rockloff, Matthew J: A speed-of-play limit reduces gambling expenditure in an online roulette game: Results of an online experiment. In: Addictive Behaviors, vol. 127, pp. 107229, 2022. (Type: Journal Article \| Links \| BibTeX \| Tags: applied, Bayesian modelling, gambling) @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} } Close journal website (open access) doi:10.1016/j.addbeh.2021.107229 Close
2021
50.	Kominsky, Jonathan; Gerstenberg, Tobias; Pelz, Madeline; Sheskin, Mark; Singmann, Henrik; Schulz, Laura; Keil, Frank C.: The trajectory of counterfactual simulation in development. In: Developmental Psychology, vol. 57, no. 2, pp. 253-268, 2021. (Type: Journal Article \| Links \| BibTeX \| Tags: causality, measurement models, MPT models, Reasoning) @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} } Close published version accepted version data Close
49.	Kellen, David; Winiger, Samuel; Dunn, John; Singmann, Henrik: Testing the Foundations of Signal Detection Theory in Recognition Memory.. In: Psychological Review, vol. 128, iss. 6, no. 1022-1050, 2021. (Type: Journal Article \| Links \| BibTeX \| Tags: mathematical modeling, Recognition memory, Signal detection) @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} } Close publisher PDF accepted preprint OSF link to data and scripts Close
48.	Winiger, Samuel; Singmann, Henrik; Kellen, David: Bias in Confidence: A critical test for discrete-state models of visual working memory.. In: Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 47, no. 3, pp. 387-401, 2021. (Type: Journal Article \| Links \| BibTeX \| Tags: mathematical modeling, measurement models, memory, MPT models, Recognition memory, Signal detection) @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} } Close Journal PDF accepted preprint data Close
47.	Lenneis, Anita; Das-Friebel, Ahuti; Singmann, Henrik; Teder-Laving, Maris; Lemola, Sakari; Wolke, Dieter; Tang, Nicole K. Y.; von Mühlenen, Adrian; Allik, Jüri; Realo, Anu: Intraindividual Variability and Temporal Stability of Mid-Sleep on Free and Workdays. In: Journal of Biological Rhythms, vol. 36, no. 2, pp. 169-184, 2021. (Type: Journal Article \| Links \| BibTeX \| Tags: mixed models) @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} } Close journal PDF journal website Close
46.	Stewart, Andrew J.; Singmann, Henrik; Haigh, Matthew; Wood, Jeffrey S.; Douven, Igor: Tracking the eye of the beholder: is explanation subjective?. In: Journal of Cognitive Psychology, vol. 33, no. 2, pp. 199-206, 2021. (Type: Journal Article \| Links \| BibTeX \| Tags: explanations, mixed models, Reasoning) @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} } Close Journal PDF Close
2020
45.	Douven, Igor; Elqayam, Shira; Singmann, Henrik; van Wijnbergen-Huitink, Janneke: Conditionals and Inferential Connections: Toward a New Semantics. In: Thinking & Reasoning, vol. 26, no. 3, pp. 311-351, 2020. (Type: Journal Article \| Links \| BibTeX \| Tags: Conditional reasoning, Reasoning) @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} } Close Journal PDF Accepted Manuscript doi:10.1080/13546783.2019.1619623 Close
44.	Baumann, Chrisitiane; Singmann, Henrik; Gershman, Samuel; von Helversen, Bettina: A Linear Threshold Model for Optimal Stopping Behavior. In: Proceedings of the National Academy of Sciences, vol. 117, no. 23, pp. 12750-12755, 2020. (Type: Journal Article \| Links \| BibTeX \| Tags: Decision Making, hierarchical-Bayesian modeling, mathematical modeling, measurement models) @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} } Close preprint Close
43.	Gronau, Quentin F; Singmann, Henrik; Wagenmakers, Eric-Jan: bridgesampling: An R Package for Estimating Normalizing Constants. In: Journal of Statistical Software, vol. 92, no. 10, 2020. (Type: Journal Article \| Abstract \| Links \| BibTeX \| Tags: hierarchical-Bayesian modeling, R, Software, Statistics - Computation) @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} } Close 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. Close published version preprint on ArXiV doi:10.18637/jss.v092.i10 Close
2019
42.	Starns, Jeffrey J.; Cataldo, Andrea M.; Rotello, Caren M.; Annis, Jeffrey; Aschenbrenner, Andrew; Bröder, Arndt; Cox, Gregory; Criss, Amy; Curl, Ryan A.; Dobbins, Ian G.; Dunn, John; Enam, Tasnuva; Evans, Nathan J.; Farrell, Simon; Fraundorf, Scott H.; Gronlund, Scott D.; Heathcote, Andrew; Heck, Daniel W.; Hicks, Jason L.; Huff, Mark J.; Kellen, David; Key, Kylie N.; Kilic, Asli; Klauer, Karl Christoph; Kraemer, Kyle R.; Leite, Fábio P.; Lloyd, Marianne E.; Malejka, Simone; Mason, Alice; McAdoo, Ryan M.; McDonough, Ian M.; Michael, Robert B.; Mickes, Laura; Mizrak, Eda; Morgan, David P.; Mueller, Shane T.; Osth, Adam; Reynolds, Angus; Seale-Carlisle, Travis M.; Singmann, Henrik; Sloane, Jennifer F.; Smith, Andrew M.; Tillman, Gabriel; van Ravenzwaaij, Don; Weidemann, Christoph T.; Wells, Gary L.; White, Corey N.; Wilson, Jack: Assessing theoretical conclusions with blinded inference to investigate a potential inference crisis. In: Advances in Methods and Practices in Psychological Science, vol. 2, no. 4, pp. 335-349, 2019. (Type: Journal Article \| Links \| BibTeX \| Tags: mathematical modeling, measurement models, memory, model selection, Recognition memory) @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} } Close published version accepted manuscript Close
41.	Niklaus, Marcel; Singmann, Henrik; Oberauer, Klaus: Two Distinct Mechanisms of Selection in Working Memory: Additive Last-Item and Retro-Cue Benefits. In: Cognition, vol. 183, pp. 282-302, 2019. (Type: Journal Article \| Links \| BibTeX \| Tags: mathematical modeling, measurement models, Signal detection, working memory) @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} } Close published manuscript PsyArXiv preprint Close
40.	Singmann, Henrik; Kellen, David: An introduction to linear mixed modeling in experimental psychology. In: New Methods in Cognitive Psychology, pp. 4–31, Psychology Press, 2019. (Type: Book Section \| Links \| BibTeX \| Tags: mixed models, R, Statistics - Computation) @incollection{Singmann2019, title = {An introduction to linear mixed modeling in experimental psychology}, author = {Henrik Singmann and David Kellen}, url = {http://singmann.org/download/publications/singmann_kellen-introduction-mixed-models.pdf, preprint}, year = {2019}, date = {2019-11-11}, booktitle = {New Methods in Cognitive Psychology}, pages = {4–31}, publisher = {Psychology Press}, keywords = {mixed models, R, Statistics - Computation}, pubstate = {published}, tppubtype = {incollection} } Close preprint Close
39.	Kominsky, Jonathan F.; Gerstenberg, Tobias; Pelz, Madeline; Sheskin, Mark; Singmann, Henrik; Schulz, Laura; Keil, Frank C.: The trajectory of counterfactual simulation in development. In: Proceedings of the 41st Annual Conference of the Cognitive Science Society, 2019. (Type: Proceedings Article \| Links \| BibTeX \| Tags: causal reasoning, development, mathematical modeling, measurement models, MPT models) @inproceedings{Kominsky2019, title = {The trajectory of counterfactual simulation in development}, author = {Jonathan F. 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_2019_CogSci_Trajectory-counterfactual-development.pdf, Final version}, year = {2019}, date = {2019-07-20}, booktitle = {Proceedings of the 41st Annual Conference of the Cognitive Science Society}, keywords = {causal reasoning, development, mathematical modeling, measurement models, MPT models}, pubstate = {published}, tppubtype = {inproceedings} } Close Final version Close
38.	Eva, Ben; Hartmann, Stephan; Singmann, Henrik: A New Probabilistic Explanation of the Modus Ponens-Modus Tollens Asymmetry . In: 2019. (Type: Proceedings Article \| Links \| BibTeX \| Tags: Conditional reasoning, mathematical modeling, new paradigm psychology of reasoning, Probabilistic reasoning) @inproceedings{Eva2019, title = {A New Probabilistic Explanation of the Modus Ponens-Modus Tollens Asymmetry }, author = {Ben Eva and Stephan Hartmann and Henrik Singmann}, url = {http://singmann.org/download/publications/Eva_CogSci_2019_MP_MT_Asymmetry.pdf, Final version}, year = {2019}, date = {2019-07-19}, keywords = {Conditional reasoning, mathematical modeling, new paradigm psychology of reasoning, Probabilistic reasoning}, pubstate = {published}, tppubtype = {inproceedings} } Close Final version Close
2018
37.	Trippas, Dries; Kellen, David; Singmann, Henrik; Pennycook, Gordon; Koehler, Derek J.; Fugelsang, Jonathan A.; Dubé, Chad: Characterizing Belief Bias in Syllogistic Reasoning: A Hierarchical-Bayesian Meta-Analysis of ROC Data. In: Psychonomic Bulletin & Review, vol. 25, no. 6, pp. 2141–2174, 2018. (Type: Journal Article \| Links \| BibTeX \| Tags: hierarchical-Bayesian modeling, mathematical modeling, measurement models, Meta Analysis, Reasoning, Signal detection, syllogistic reasoning) @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} } Close published version accepted manuscript data and modeling code Close
36.	Boehm, Udo; Annis, Jeff; Frank, Michael; Hawkins, Guy; Heathcote, Andrew; Kellen, David; Krypotos, Angelos-Miltiadis; Lerche, Veronika; Logan, Gordon D; Palmeri, Thomas; van Ravenzwaaij, Don; Servant, Mathieu; Singmann, Henrik; Starns, Jeffrey; Voss, Andreas; Wiecki, Thomas; Matzke, Dora; Wagenmakers, Eric-Jan: Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations. In: Journal of Mathematical Psychology, vol. 87, pp. 46-75, 2018. (Type: Journal Article \| Abstract \| Links \| BibTeX \| Tags: Diffusion model, mathematical modeling, measurement models, Software) @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} } Close 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. Close preprint on OSF doi:10.31234/osf.io/km28u Close
35.	Kellen, David; Singmann, Henrik; Batchelder, William H.: Classic-Probability Accounts of Mirrored (Quantum-Like) Order Effects in Human Judgments. In: Decision, vol. 5, no. 4, pp. 323-338, 2018. (Type: Journal Article \| Links \| BibTeX \| Tags: mathematical modeling, measurement models, quantum cognition) @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} } Close publisher version accepted manuscript Close
34.	Singmann, Henrik; Kellen, David; Mizrak, Eda; Öztekin, Ilke: Using Ensembles of Cognitive Models to Answer Substantive Questions. In: Rogers, Tim; Rau, Marina; Zhu, Jerry; Kalish, Chuck (Ed.): Proceedings of the 40th Annual Conference of the Cognitive Science Society, pp. 1070–1075, Austin TX: Cognitive Science Society, 2018. (Type: Proceedings Article \| Links \| BibTeX \| Tags: hierarchical-Bayesian modeling, mathematical modeling, measurement models, model selection) @inproceedings{singmann_using_2018, title = {Using Ensembles of Cognitive Models to Answer Substantive Questions}, author = {Henrik Singmann and David Kellen and Eda Mizrak and Ilke Öztekin}, editor = {Tim Rogers and Marina Rau and Jerry Zhu and Chuck Kalish}, url = {http://singmann.org/download/publications/Singmann-et-al.-2018-Using-Ensembles-of-Cognitive-Models-to-Answer-Subs.pdf, published version}, year = {2018}, date = {2018-07-29}, booktitle = {Proceedings of the 40th Annual Conference of the Cognitive Science Society}, pages = {1070--1075}, publisher = {Austin TX: Cognitive Science Society}, keywords = {hierarchical-Bayesian modeling, mathematical modeling, measurement models, model selection}, pubstate = {published}, tppubtype = {inproceedings} } Close published version Close
33.	Kellen, David; Singmann, Henrik; Chen, Sharon; Winiger, Samuel: Assumption Violations in Forced-Choice Recognition Judgments: Implications from the Area Theorem. In: Rogers, Tim; Rau, Marina; Zhu, Jerry; Kalish, Chuck (Ed.): Proceedings of the 40th Annual Conference of the Cognitive Science Society, pp. 598–603, Austin TX: Cognitive Science Societ, 2018. (Type: Proceedings Article \| Links \| BibTeX \| Tags: mathematical modeling, measurement models, Recognition memory, Signal detection) @inproceedings{Kellen2018, title = {Assumption Violations in Forced-Choice Recognition Judgments: Implications from the Area Theorem}, author = {David Kellen and Henrik Singmann and Sharon Chen and Samuel Winiger}, editor = {Tim Rogers and Marina Rau and Jerry Zhu and Chuck Kalish}, url = {http://singmann.org/download/publications/Kellen-et-al.-2018-Assumption-Violations-in-Forced-Choice-Recognition.pdf}, year = {2018}, date = {2018-07-26}, booktitle = {Proceedings of the 40th Annual Conference of the Cognitive Science Society}, pages = {598--603}, publisher = {Austin TX: Cognitive Science Societ}, keywords = {mathematical modeling, measurement models, Recognition memory, Signal detection}, pubstate = {published}, tppubtype = {inproceedings} } Close http://singmann.org/download/publications/Kellen-et-al.-2018-Assumption-Violatio[...] Close
32.	Winiger, Samuel; Singmann, Henrik; Kellen, David: Measuring Belief Bias with Ternary Response Sets. In: Rogers, Tim; Rau, Marina; Zhu, Jerry; Kalish, Chuck (Ed.): Proceedings of the 40th Annual Conference of the Cognitive Science Society, pp. 1171–1176, Austin TX: Cognitive Science Society, 2018. (Type: Proceedings Article \| Links \| BibTeX \| Tags: Belief bias, measurement models, MPT models, Reasoning, syllogistic reasoning) @inproceedings{winiger_measuring_2018, title = {Measuring Belief Bias with Ternary Response Sets}, author = {Samuel Winiger and Henrik Singmann and David Kellen}, editor = {Tim Rogers and Marina Rau and Jerry Zhu and Chuck Kalish}, url = {http://singmann.org/download/publications/Winiger-et-al.-2018-Measuring-Belief-Bias-with-Ternary-Response-Sets.pdf, published version}, year = {2018}, date = {2018-07-26}, booktitle = {Proceedings of the 40th Annual Conference of the Cognitive Science Society}, pages = {1171--1176}, publisher = {Austin TX: Cognitive Science Society}, keywords = {Belief bias, measurement models, MPT models, Reasoning, syllogistic reasoning}, pubstate = {published}, tppubtype = {inproceedings} } Close published version Close
31.	Baumann, Christiane; Singmann, Henrik; Kaxiras, Vassilios E; Gershman, Samuel; von Helversen, Bettina: Explaining Human Decision Making in Optimal Stopping Tasks. In: Rogers, Tim; Rau, Marina; Zhu, Jerry; Kalish, Chuck (Ed.): Proceedings of the 40th Annual Conference of the Cognitive Science Society, pp. 1343–1348, Austin TX: Cognitive Science Society, 2018. (Type: Proceedings Article \| Links \| BibTeX \| Tags: Decision Making, mathematical modeling, optimal stopping) @inproceedings{baumann_explaining_2018, title = {Explaining Human Decision Making in Optimal Stopping Tasks}, author = {Christiane Baumann and Henrik Singmann and Vassilios E Kaxiras and Samuel Gershman and Bettina von Helversen}, editor = {Tim Rogers and Marina Rau and Jerry Zhu and Chuck Kalish}, url = {http://singmann.org/download/publications/Baumann-et-al.-2018-Explaining-Human-Decision-Making-in-Optimal-Stoppi.pdf, published version}, year = {2018}, date = {2018-07-26}, booktitle = {Proceedings of the 40th Annual Conference of the Cognitive Science Society}, pages = {1343--1348}, publisher = {Austin TX: Cognitive Science Society}, keywords = {Decision Making, mathematical modeling, optimal stopping}, pubstate = {published}, tppubtype = {inproceedings} } Close published version Close
30.	Bartsch, Lea; Singmann, Henrik; Oberauer, Klaus: The Effects of Refreshing and Elaboration on Working Memory Performance, and their Contributions to Long-Term Memory Formation. In: Memory & Cognition, vol. 46, no. 5, pp. 796-808, 2018. (Type: Journal Article \| Links \| BibTeX \| Tags: hierarchical-Bayesian modeling, working memory) @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} } Close publisher version data and analysis code Close
29.	Markovits, Henry; Brisson, Janie; de Chantal, Pier-Luc; Singmann, Henrik: Multiple Layers of Information Processing in Deductive Reasoning: Combining Dual Strategy and Dual-Source Approaches to Reasoning. In: Journal of Cognitive Psychology, vol. 30, no. 4, pp. 394-405, 2018. (Type: Journal Article \| Links \| BibTeX \| Tags: Conditional reasoning, Probabilistic reasoning, Reasoning) @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} } Close published manuscript Close
28.	Douven, Igor; Elqayam, Shira; Singmann, Henrik; van Wijnbergen-Huitink, Janneke: Conditionals and Inferential Connections: A Hypothetical Inferential Theory. In: Cognitive Psychology, vol. 101, pp. 50-81, 2018. (Type: Journal Article \| Links \| BibTeX \| Tags: Conditional reasoning, Reasoning) @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} } Close accepted manuscript OSF link (includes all data and code) Close
27.	Mizrak, Eda; Singmann, Henrik; Öztekin, Ilke: Forgetting Emotional Material in Working Memory. In: Social Cognitive and Affective Neuroscience, vol. 13, no. 3, pp. 331–340, 2018. (Type: Journal Article \| Links \| BibTeX \| Tags: Diffusion model, emotion, measurement models, memory, neuro, working memory) @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} } Close free publisher pdf publisher website Close
2017
26.	Kellen, David; Singmann, Henrik: Memory Representations, Tree Structures, and Parameter Polysemy: Comment on Cooper, Greve, and Henson (2017). In: Cortex, vol. 96, pp. 148-155, 2017. (Type: Journal Article \| Links \| BibTeX \| Tags: mathematical modeling, measurement models, MPT models, source memory) @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} } Close accepted manuscript Close
25.	Skovgaard-Olsen, Niels; Singmann, Henrik; Klauer, Karl Christoph: Relevance and Reason Relations. In: Cognitive Science, vol. 41, pp. 1202-1215, 2017. (Type: Journal Article \| Links \| BibTeX \| Tags: Conditional reasoning, mixed models, new paradigm psychology of reasoning) @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} } Close publisher version accepted version OSF link (contains all data and code) supplemental materials Close

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Dormant manuscripts (manuscripts which might be picked up again at a later point in time)

Foster, K. & Singmann, H. (in revision). Another Approximation of the First-Passage Time Densities for the Ratcliff Diffusion Decision Model.

Winiger, S., Singmann, H., & Kellen, D. (in revision). Violations of Conditional Independence in Mixed-State Models of Visual Working Memory.

Misc

Other up-to-date lists of my publications can be found at my Google Scholar profile (which contains citation information), my ReseachGate profile, or my ORCID profile (can be a little outdated).

Henrik Singmann - Computational Psychology

Peer Reviewed Publications

Submitted manuscripts (i.e., under review or in revision)

Publications

2027

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2020

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2018

2017

Dormant manuscripts (manuscripts which might be picked up again at a later point in time)

Misc