ORCID Profile
0000-0003-2600-5937
Current Organisation
University of California, Irvine
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Publisher: Center for Open Science
Date: 14-12-2018
Abstract: Despite its many advocates, Bayesian inference is currently employed by only a minority of social and behavioural scientists. One possible barrier is a lack of consensus on how best to conduct and report such analyses. Employing Bayesian methods involves making choices about prior distributions, likelihood functions and robustness checks, as well as about how to present, visualize and interpret the results (for a glossary of the main Bayesian statistical concepts, see Box 1). Some researchers may find this wide range of choices too daunting to use Bayesian inference in their own study. This paper highlights the areas of agreement and the arguments behind disagreements, established on the back of a self-questionnaire provided and explained in detail on OSF (osf.io/6eqx5/).
Publisher: Springer Science and Business Media LLC
Date: 31-03-2017
DOI: 10.3758/S13428-017-0879-5
Abstract: People often interact with environments that can provide only a finite number of items as resources. Eventually a book contains no more chapters, there are no more albums available from a band, and every Pokémon has been caught. When interacting with these sorts of environments, people either actively choose to quit collecting new items, or they are forced to quit when the items are exhausted. Modeling the distribution of how many items people collect before they quit involves untangling these two possibilities, We propose that censored geometric models are a useful basic technique for modeling the quitting distribution, and, show how, by implementing these models in a hierarchical and latent-mixture framework through Bayesian methods, they can be extended to capture the additional features of specific situations. We demonstrate this approach by developing and testing a series of models in two case studies involving real-world data. One case study deals with people choosing jokes from a recommender system, and the other deals with people completing items in a personality survey.
Publisher: Springer Science and Business Media LLC
Date: 24-04-2019
Publisher: Frontiers Media SA
Date: 09-12-2014
Publisher: Proceedings of the National Academy of Sciences
Date: 12-03-2018
Abstract: We describe and demonstrate an empirical strategy useful for discovering and replicating empirical effects in psychological science. The method involves the design of a metastudy, in which many independent experimental variables—that may be moderators of an empirical effect—are indiscriminately randomized. Radical randomization yields rich datasets that can be used to test the robustness of an empirical claim to some of the vagaries and idiosyncrasies of experimental protocols and enhances the generalizability of these claims. The strategy is made feasible by advances in hierarchical Bayesian modeling that allow for the pooling of information across unlike experiments and designs and is proposed here as a gold standard for replication research and exploratory research. The practical feasibility of the strategy is demonstrated with a replication of a study on subliminal priming.
Publisher: Center for Open Science
Date: 08-08-2023
Abstract: The circular drift-diffusion model (CDDM) is a sequential s ling model designed to account for decisions and response times in decision-making tasks with a circular set of choice alternatives. We present and demonstrate a fully Bayesian implementation and extension of the CDDM. This development allows researchers to apply the CDDM to data from complex experiments and draw conclusions about targeted hypotheses. The Bayesian implementation relies on a custom JAGS module. We describe the module and demonstrate its adequacy through a simulation study. We then illustrate the advantages of the implementation by revisiting data from a continuous orientation judgment task. We develop a graphical model for the analysis that is based on the CDDM, but extends it with hierarchical and latent-mixture structures.We then demonstrate how these extensions are used to accommodate the design of the experiment and to implement psychological assumptions about in idual differences, the difficulty of different stimulus conditions, and the impact of cues on decision making. Finally, we demonstrate how the computational Bayesian inference enabled by JAGS allows these assumptions to be tested and addresses psychological research questions about people's decision making.
Publisher: Center for Open Science
Date: 31-08-2019
Abstract: The target article on robust modeling (Lee et al.) generated a lot of commentary. In this reply, we discuss some of the common themes in the commentaries some are simple points of agreement while others are extensions of a practical or abstract nature. We also address a small number of disagreements or confusions.
Publisher: Springer Science and Business Media LLC
Date: 09-10-2019
Publisher: Springer Science and Business Media LLC
Date: 28-06-2016
Publisher: American Psychological Association (APA)
Date: 2011
DOI: 10.1037/A0021765
Abstract: Two-choice response times are a common type of data, and much research has been devoted to the development of process models for such data. However, the practical application of these models is notoriously complicated, and flexible methods are largely nonexistent. We combine a popular model for choice response times-the Wiener diffusion process-with techniques from psychometrics in order to construct a hierarchical diffusion model. Chief among these techniques is the application of random effects, with which we allow for unexplained variability among participants, items, or other experimental units. These techniques lead to a modeling framework that is highly flexible and easy to work with. Among the many novel models this statistical framework provides are a multilevel diffusion model, regression diffusion models, and a large family of explanatory diffusion models. We provide ex les and the necessary computer code.
Publisher: Springer Science and Business Media LLC
Date: 27-01-2020
Publisher: Center for Open Science
Date: 19-09-2020
Abstract: There are many ways to measure how people manage risk when they make decisions. A standard approach is to measure risk propensity using self-report questionnaires. An alternative approach is to use decision-making tasks that involve risk and uncertainty, and apply cognitive models of task behavior to infer parameters that measure people’s risk propensity. We report the results of a within-participants experiment that used three questionnaires and four decision-making tasks. The questionnaires are the Risk Propensity Scale, the Risk Taking Index, and the DomainSpecific Risk Taking Scale. The decision-making tasks are the Balloon Analogue Risk Task, the preferential choice gambling task, the optimal stopping problem, and the bandit problem. We analyze the relationships between the risk measures and cognitive parameters using Bayesian inferences about the patterns of correlation, and using a novel cognitive latent variable modeling approach. The results show that people’s risk propensity is generally consistent within different conditions for each of the decision-making tasks. There is, however, little evidence that the way people manage risk generalizes across the tasks, or that it corresponds to the questionnaire measures.
Publisher: Center for Open Science
Date: 11-07-2017
Abstract: We describe and demonstrate an empirical strategy useful for discovering and replicating empirical effects in psychological science. The method involves the design of a meta-study, in which many independent experimental variables—that may be moderators of an empirical effect—are indiscriminately randomized. Radical randomization yields rich data sets that can be used to test the robustness of an empirical claim to some of the vagaries and idiosyncrasies of experimental protocols and enhances the generalizability of these claims. The strategy is made feasible by advances in hierarchical Bayesian modeling which allow for the pooling of information across unlike experiments and designs, and is proposed here as a gold standard for replication research and exploratory research. The practical feasibility of the strategy is demonstrated with a replication of a study on subliminal priming. All materials and data are freely available online via osf.io/u2vwa/.
No related grants have been discovered for Joachim Vandekerckhove.