Dr Don Van Ravenzwaaij
School of Psychology
- Phone:(02) 4921 5662
My name is Don van Ravenzwaaij and I am a lecturer (Assistant Professor) in Cognitive and Computational Psychology at the University of Newcastle. I obtained my PhD in Quantitative Psychology at the University of Amsterdam in 2012. After graduating, I have worked as a post-doc at the University of New South Wales on developing computational models of judgment and decision making processes until July 2013. For more information on my research and my teaching, please visit my website at http://www.donvanravenzwaaij.com/Home.htmlResearch Expertise
The first pillar of my research has been the advancement and application of response time models, such as the drift diffusion model, to speeded decision making. In the past, I have published both theoretical and applied work on response time models in the fields of mathematical psychology, statistics, neurophysiology, intelligence research, social psychology, psychopharmacology, and clinical psychology. The second pillar of my research has been the application of Bayesian decision-making models to data from non-speeded decision environments. My interest is in developing computational models aimed at examining how decision makers gather information in new and uncertain decision environments. I have also worked on developing computational models for tasks that assess risk taking behavior.
I am currently the course co-ordinator of Psyc3000, the third year statistics and methodology course of psychology. I have also taught classes on cognitive science, Bayesian statistics, R and Matlab, and the typesetting program LaTeX.
CoursI am currently the course co-ordinator of Psyc3000, the third year statistics and methodology course of psychology. I was also co-organizer of the annual Mathematical Psychology Conference, which was hosted in Amsterdam in August 2009.
Response time modeling with Prof. Eric-Jan Wagenmakers, A/Prof. Scott Brown, and Prof. Francis Tuerlinckx.
- PhD (Behavioural Science), University of Amsterdam - Netherlands
- Master of Science (Psychology), University of Amsterdam - Netherlands
- Bayesian modeling
- Cognitive Science
- Judgment and decision making
- Response time modeling
- German (Fluent)
- Dutch (Fluent)
Fields of Research
|170112||Sensory Processes, Perception and Performance||60|
|Dates||Title||Organisation / Department|
|1/01/2014 - 31/12/2017||Membership - Australian Council Grant||Australian Council
|1/01/2014 -||Membership - Psychonomic Society||Psychonomic Society
|1/04/2012 - 1/07/2013||Post-doc||The University of New South Wales
School of Psychology
|1/01/2012 - 31/12/2012||Editorial Board - ANZAScA||Australian and New Zealand Architectural Science Association (ANZAScA)
|1/01/2012 - 31/12/2013||Membership - ANZAScA||Australian and New Zealand Architectural Science Association (ANZAScA)
|1/01/2008 - 1/12/2011||PhD Student||University of Amsterdam
School of Psychology
|1/01/2008 -||Membership - Society of Mathematical Psychology||Society of Mathematical Psychology
Australia Research Council
For publications that are currently unpublished or in-press, details are shown in italics.
Chapter (3 outputs)
Wetzels R, van Ravenzwaaij D, Wagenmakers EJ, 'Bayesian analysis', The Encyclopedia of Clinical Psychology, Wiley, Cham, Switzerland . (2015)
van Ravenzwaaij D, Lee MD, Wagenmakers EJ, 'The BART model of risk taking', Bayesian cognitive modelling: A practical course, Cambridge University Press, Cambridge 207-212 (2013)
Schulze C, van Ravenzwaaij D, Newell BR, 'Match me if you can: How smart choices are fuelled by competition', Proceedings of the 35th Annual Conference of the Cognitive Science Society, Cognitive Science Society [E3]
Journal article (30 outputs)
Schulze C, van Ravenzwaaij D, Newell BR, 'Hold It! The Influence of Lingering Rewards on Choice Diversification and Persistence', Journal of Experimental Psychology: Learning Memory and Cognition, (2017)
Â© 2017 APA, all rights reserved). Learning to choose adaptively when faced with uncertain and variable outcomes is a central challenge for decision makers. This study examines re... [more]
Â© 2017 APA, all rights reserved). Learning to choose adaptively when faced with uncertain and variable outcomes is a central challenge for decision makers. This study examines repeated choice in dynamic probability learning tasks in which outcome probabilities changed either as a function of the choices participants made or independently of those choices. This presence/absence of sequential choice-outcome dependencies was implemented by manipulating a single task aspect between conditions: the retention/withdrawal of reward across individual choice trials. The study addresses how people adapt to these learning environments and to what extent they engage in 2 choice strategies often contrasted as paradigmatic examples of striking violation of versus nominal adherence to rational choice: diversification and persistent probability maximizing, respectively. Results show that decisions approached adaptive choice diversification and persistence when sufficient feedback was provided on the dynamic rules of the probabilistic environments. The findings of divergent behavior in the 2 environments indicate that diversified choices represented a response to the reward retention manipulation rather than to the mere variability of outcome probabilities. Choice in both environments was well accounted for by the generalized matching law, and computational modeling-based strategy analyses indicated that adaptive choice arose mainly from reliance on reinforcement learning strategies. (PsycINFO Database Record
van Ravenzwaaij D, Provost A, Brown SD, 'A confirmatory approach for integrating neural and behavioral data into a single model', Journal of Mathematical Psychology, 76 131-141 (2017)
Â© 2016 Elsevier Inc. Recent decades have witnessed amazing advances in both mathematical models of cognition and in the field of cognitive neuroscience. These developments were i... [more]
Â© 2016 Elsevier Inc. Recent decades have witnessed amazing advances in both mathematical models of cognition and in the field of cognitive neuroscience. These developments were initially independent of one another, but recently the fields have started to become interested in joining forces. The resulting joint modeling of behavioral and neural data can be difficult, but has proved fruitful. We briefly review different approaches used in decision-making research for linking behavioral and neural data, and also provide an example. Our example provides a tight link between behavioral data and evoked scalp potentials measured during mental rotation. The example model illustrates a powerful hypothesis-driven way of linking such data sets. We demonstrate the use of such a model, provide a model comparison against interesting alternatives, and discuss the conclusions that follow from applying such a joint model.
Tillman G, Osth AF, van Ravenzwaaij D, Heathcote A, 'A diffusion decision model analysis of evidence variability in the lexical decision task', Psychonomic Bulletin and Review, 1-8 (2017)
Â© 2017 Psychonomic Society, Inc. The lexical-decision task is among the most commonly used paradigms in psycholinguistics. In both the signal-detection theory and Diffusion Decis... [more]
Â© 2017 Psychonomic Society, Inc. The lexical-decision task is among the most commonly used paradigms in psycholinguistics. In both the signal-detection theory and Diffusion Decision Model (DDM; Ratcliff, Gomez, & McKoon, Psychological Review, 111, 159Â¿182, 2004) frameworks, lexical-decisions are based on a continuous source of word-likeness evidence for both words and non-words. The Retrieving Effectively from Memory model of Lexical-Decision (REMÂ¿LD; Wagenmakers et al., Cognitive Psychology, 48(3), 332Â¿367, 2004) provides a comprehensive explanation of lexical-decision data and makes the prediction that word-likeness evidence is more variable for words than non-words and that higher frequency words are more variable than lower frequency words. To test these predictions, we analyzed five lexical-decision data sets with the DDM. For all data sets, drift-rate variability changed across word frequency and non-word conditions. For the most part, REMÂ¿LDÂ¿s predictions about the ordering of evidence variability across stimuli in the lexical-decision task were confirmed.
Van Ravenzwaaij D, Ioannidis JPA, 'A simulation study of the strength of evidence in the recommendation of medications based on two trials with statistically significant results', PLoS ONE, 12 (2017)
Â© This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful ... [more]
Â© This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. A typical rule that has been used for the endorsement of new medications by the Food and Drug Administration is to have two trials, each convincing on its own, demonstrating effectiveness. "Convincing" may be subjectively interpreted, but the use of p-values and the focus on statistical significance (in particular with p < .05 being coined significant) is pervasive in clinical research. Therefore, in this paper, we calculate with simulations what it means to have exactly two trials, each with p < .05, in terms of the actual strength of evidence quantified by Bayes factors. Our results show that different cases where two trials have a p-value below .05 have wildly differing Bayes factors. Bayes factors of at least 20 in favor of the alternative hypothesis are not necessarily achieved and they fail to be reached in a large proportion of cases, in particular when the true effect size is small (0.2 standard deviations) or zero. In a non-trivial number of cases, evidence actually points to the null hypothesis, in particular when the true effect size is zero, when the number of trials is large, and when the number of participants in both groups is low. We recommend use of Bayes factors as a routine tool to assess endorsement of new medications, because Bayes factors consistently quantify strength of evidence. Use of p-values may lead to paradoxical and spurious decision-making regarding the use of new medications.
van Ravenzwaaij D, Donkin C, Vandekerckhove J, 'The EZ diffusion model provides a powerful test of simple empirical effects', Psychonomic Bulletin and Review, 24 547-556 (2017)
Â© 2016, The Author(s). Over the last four decades, sequential accumulation models for choice response times have spread through cognitive psychology like wildfire. The most popul... [more]
Â© 2016, The Author(s). Over the last four decades, sequential accumulation models for choice response times have spread through cognitive psychology like wildfire. The most popular style of accumulator model is the diffusion model (Ratcliff Psychological Review, 85, 59Â¿108, 1978), which has been shown to account for data from a wide range of paradigms, including perceptual discrimination, letter identification, lexical decision, recognition memory, and signal detection. Since its original inception, the model has become increasingly complex in order to account for subtle, but reliable, data patterns. The additional complexity of the diffusion model renders it a tool that is only for experts. In response, Wagenmakers et al. (Psychonomic Bulletin & Review, 14, 3Â¿22, 2007) proposed that researchers could use a more basic version of the diffusion model, the EZ diffusion. Here, we simulate experimental effects on data generated from the full diffusion model and compare the power of the full diffusion model and EZ diffusion to detect those effects. We show that the EZ diffusion model, by virtue of its relative simplicity, will be sometimes better able to detect experimental effects than the dataÂ¿generating full diffusion model.
Cramer AOJ, van Ravenzwaaij D, Matzke D, Steingroever H, Wetzels R, Grasman RPPP, et al., 'Hidden multiplicity in exploratory multiway ANOVA: Prevalence and remedies', PSYCHONOMIC BULLETIN & REVIEW, 23 640-647 (2016) [C1]
Van Ravenzwaaij D, Cassey P, Brown SD, 'A Simple Introduction to Markov Chain Monte-Carlo', JOURNAL OF MATHEMATICAL PSYCHOLOGY, --- (2016)
van Ravenzwaaij D, Mulder MJ, Tuerlinckx F, Wagenmakers EJ, 'Paradoxes of optimal decision making: a response to Moran (2014)', Psychonomic Bulletin and Review, 22 307-308 (2015) [C3]
Schulze C, van Ravenzwaaij D, Newell BR, 'Of matchers and maximizers: How competition shapes choice under risk and uncertainty', Cognitive Psychology, 78 78-98 (2015) [C1]
Â© 2015 Elsevier Inc. In a world of limited resources, scarcity and rivalry are central challenges for decision makers-animals foraging for food, corporations seeking maximal prof... [more]
Â© 2015 Elsevier Inc. In a world of limited resources, scarcity and rivalry are central challenges for decision makers-animals foraging for food, corporations seeking maximal profits, and athletes training to win, all strive against others competing for the same goals. In this article, we establish the role of competitive pressures for the facilitation of optimal decision making in simple sequential binary choice tasks. In two experiments, competition was introduced with a computerized opponent whose choice behavior reinforced one of two strategies: If the opponent probabilistically imitated participant choices, probability matching was optimal; if the opponent was indifferent, probability maximizing was optimal. We observed accurate asymptotic strategy use in both conditions irrespective of the provision of outcome probabilities, suggesting that participants were sensitive to the differences in opponent behavior. An analysis of reinforcement learning models established that computational conceptualizations of opponent behavior are critical to account for the observed divergence in strategy adoption. Our results provide a novel appraisal of probability matching and show how this individually 'irrational' choice phenomenon can be socially adaptive under competition.
van Ravenzwaaij D, Boekel W, Forstmann BU, Ratcliff R, Wagenmakers E-J, 'Action Video Games Do Not Improve the Speed of Information Processing in Simple Perceptual Tasks', JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL, 143 1794-1805 (2014) [C1]
Van Ravenzwaaij D, Moore CP, Lee MD, Newell BR, 'A hierarchical bayesian modeling approach to searching and stopping in multi-attribute judgment', Cognitive Science, (2014) [C1]
In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. Ho... [more]
In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., "Does this city have a university?") before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, ) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take-the-best or the Weighted Additive heuristics. Â© 2014 Cognitive Science Society, Inc.
Huizenga HM, van Duijvenvoorde ACK, van Ravenzwaaij D, Wetzels R, Jansen BRJ, 'Is the unconscious, if it exists, a superior decision maker?', Behav Brain Sci, 37 32-33 (2014) [C3]
Newell BR, van Ravenzwaaij D, Donkin C, 'A quantum of truth? Querying the alternative benchmark for human cognition', Behavioral Brain Sciences, 36 300-302 (2013) [C3]
Newell BR, Koehler DJ, James G, Rakow T, van Ravenzwaaij D, 'Probability matching in risky choice: The interplay of feedback and strategy availability', MEMORY & COGNITION, 41 329-338 (2013) [C1]
Dutilh G, Van Ravenzwaaij D, Nieuwenhuis S, Van der Maas HLJ, Forstmann BU, Wagenmakers EJ, 'How to measure post-error slowing: A confound and a simple solution', Journal of Mathematical Psychology, 56 208-216 (2012) [C1]
In many response time tasks, people slow down after they make an error. This phenomenon of post-error slowing (PES) is thought to reflect an increase in response caution, that is,... [more]
In many response time tasks, people slow down after they make an error. This phenomenon of post-error slowing (PES) is thought to reflect an increase in response caution, that is, a heightening of response thresholds in order to increase the probability of a correct response at the expense of response speed. In many empirical studies, PES is quantified as the difference in response time (RT) between post-error trials and post-correct trials. Here we demonstrate that this standard measurement method is prone to contamination by global fluctuations in performance over the course of an experiment. Diffusion model simulations show how global fluctuations in performance can cause either spurious detection of PES or masking of PES. Both confounds are highly undesirable and can be eliminated by a simple solution: quantify PES as the difference in RT between post-error trials and the associated pre-error trials. Experimental data are used as an empirical illustration. Â© 2012 Elsevier Inc..
Van Ravenzwaaij D, Dutilh G, Wagenmakers EJ, 'A diffusion model decomposition of the effects of alcohol on perceptual decision making', Psychopharmacology, 219 1017-1025 (2012)
Rationale: Even in elementary cognitive tasks, alcohol consumption results in both cognitive and motor impairments (e.g., Schweizer and Vogel-Sprott, Exp Clin Psychopharmacol 16: ... [more]
Rationale: Even in elementary cognitive tasks, alcohol consumption results in both cognitive and motor impairments (e.g., Schweizer and Vogel-Sprott, Exp Clin Psychopharmacol 16: 240-250, 2008). Objectives: The purpose of this study is to quantify the latent psychological processes that underlie the alcohol-induced decrement in observed performance. Methods: In a double-blind experiment, we administered three different amounts of alcohol to participants on different days: a placebo dose (0 g/l), a moderate dose (0.5 g/l), and a high dose (1 g/l). Following this, participants performed a "moving dots" perceptual discrimination task. We analyzed the data using the drift diffusion model. Model parameters drift rate, boundary separation, and non-decision time allow a decomposition of the alcohol effect in terms of their respective cognitive components, that is, rate of information processing, response caution, and non-decision processes (e.g., stimulus encoding, motor processes). Results: We found that alcohol intoxication causes higher mean RTs and lower response accuracies. The diffusion model decomposition showed that alcohol intoxication caused a decrease in drift rate and an increase in non-decision time. Conclusions: In a simple perceptual discrimination task, even a moderate dose of alcohol decreased the rate of information processing and negatively affected the non-decision component. However, alcohol consumption left response caution largely intact. Â© 2011 The Author(s).
Van Ravenzwaaij D, Brown SD, Wagenmakers E-J, 'An integrated perspective on the relation between response speed and intelligence', Cognition, 119 381-393 (2011) [C1]
Keye D, Wilhelm O, Oberauer K, van Ravenzwaaij D, 'Erratum to Individual differences in conflict-monitoring: Testing means and covariance hypothesis about the Simon and the Eriksen Flanker task (Psychol Res, 10.1007/s00426-008-0188-9)', Psychological Research, 74 237-238 (2010)
Keye D, Wilhelm O, Oberauer K, van Ravenzwaaij D, 'Individual differences in conflict-monitoring: Testing means and covariance hypothesis about the Simon and the Eriksen flanker task', Psychological Research, 73 762-776 (2009) [C1]
Conflict and context slow-down have been proposed as indicators of a conflict-monitoring system that initiates cognitive control to resolve conflicts in information processing. We... [more]
Conflict and context slow-down have been proposed as indicators of a conflict-monitoring system that initiates cognitive control to resolve conflicts in information processing. We investigated individual differences in conflict-monitoring and their associations with working memory (WM) and impulsivity. A total of 150 adults completed a Simon and an Eriksen flanker task, together with measures of WM and impulsivity. On both tasks, responses were slower and less accurate on incompatible than on compatible trials (conflict effect), and the conflict effect was larger when the preceding trial was compatible than when it was incompatible (context effect). Stimulus repetition did not explain the context effect. Individual differences could be attributed to three separable factors for each task: general speeded performance, conflict effect, and context effect. Evidence for across-task generality of these factors was sparse. Associations of these factors with impulsivity were weak at best. WM was correlated with general speed, and also with some but not all factors reflecting conflict-related processes. Â© Springer-Verlag 2008.
|Show 27 more journal articles|
Conference (1 outputs)
van Ravenzwaaij D, Newell BR, Moore CP, Lee MD, 'Using recognition in multiÂ¿attribute decision environments', COGSCI 2013 (2013) [E2]
Grants and Funding
|Number of grants||4|
Click on a grant title below to expand the full details for that specific grant.
20143 grants / $392,883
Funding body: ARC (Australian Research Council)
|Funding body||ARC (Australian Research Council)|
|Project Team||Doctor Don Van Ravenzwaaij|
|Scheme||Discovery Early Career Researcher Award (DECRA)|
|Type Of Funding||Aust Competitive - Commonwealth|
How is Response Competition Implemented in the Brain? â A Critical Test and an Innovative Model$5,000
Funding body: University of Newcastle
|Funding body||University of Newcastle|
|Project Team||Doctor Don Van Ravenzwaaij|
|Scheme||New Staff Grant|
|Type Of Funding||Internal|
Funding body: Keats Endowment Research Fund
|Funding body||Keats Endowment Research Fund|
|Project Team||Doctor Don Van Ravenzwaaij|
|Type Of Funding||Grant - Aust Non Government|
20121 grants / $4,000
Funding body: University of Newcastle - Faculty of Science & IT
|Funding body||University of Newcastle - Faculty of Science & IT|
|Project Team||Doctor Don Van Ravenzwaaij|
|Scheme||ECR Visiting Fellowship|
|Type Of Funding||Internal|
Number of supervisions
|Year||Level of Study||Research Title||Program||Supervisor Type|
|2017||PhD||Advancing Methods and Mathematical Models of Perceptual Decision Making||PhD (Psychology - Science), Faculty of Science, The University of Newcastle||Co-Supervisor|
The map is a representation of a researchers co-authorship with collaborators across the globe. The map displays the number of publications against a country, where there is at least one co-author based in that country. Data is sourced from the University of Newcastle research publication management system (NURO) and may not fully represent the authors complete body of work.
|Country||Count of Publications|