Boag, R. J., Strickland, L., Heathcote, A., Neal, A., Palada, H., & Loft, S. (2023). Evidence accumulation modelling in the wild: understanding safety-critical decisions. Trends in Cognitive Sciences, 27(2), 175-188. https://doi.org/10.1016/j.tics.2022.11.009[details]
Boehm, U., Evans, N. J., Gronau, Q. F., Matzke, D., Wagenmakers, E. J., & Heathcote, A. J. (2023). Inclusion Bayes Factors for Mixed Hierarchical Diffusion Decision Models. Psychological Methods. Advance online publication. https://doi.org/10.31234/osf.io/45t2w, https://doi.org/10.1037/met0000582
Castro, S. C., Heathcote, A., Cooper, J. M., & Strayer, D. L. (2023). Dynamic Workload Measurement and Modeling: Driving and Conversing. Journal of Experimental Psychology: Applied, 29(3), 445-653. https://doi.org/10.1037/xap0000431[details]
Ciobanu, L. G., Stankov, L., Ahmed, M., Heathcote, A., Clark, S. R., & Aidman, E. (2023). Multifactorial structure of cognitive assessment tests in the UK Biobank: A combined exploratory factor and structural equation modeling analyses. Frontiers in Psychology, 14, Article 1054707. https://doi.org/10.3389/fpsyg.2023.1054707[details]
Isherwood, S. J. S., Bazin, P., Miletić, S., Stevenson, N. R., Trutti, A. C., Tse, D. H. Y., Heathcote, A., Matzke, D., Innes, R. J., Habli, S., Sokołowski, D. R., Alkemade, A., Håberg, A. K., & Forstmann, B. U. (2023). Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI. NeuroImage, 271, Article 119988. https://doi.org/10.1016/j.neuroimage.2023.119988[details]
Isherwood, S., Bazin, P., Miletić, S., Stevenson, N., Trutti, A., H. Y. Tse, D., Heathcote, A., Matzke, D., Habli, S., Sokołowski, D. R., Alkemade, A., Håberg, A., Forstmann, B. & Innes, R. (8-6-2023). Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI data. Universiteit van Amsterdam. https://doi.org/10.21942/uva.22240393.v1
Kucina, T., Wells, L., Lewis, I., de Salas, K., Kohl, A., Palmer, M. A., Sauer, J. D., Matzke, D., Aidman, E., & Heathcote, A. (2023). Calibration of cognitive tests to address the reliability paradox for decision-conflict tasks. Nature Communications, 14, Article 2234. https://doi.org/10.1038/s41467-023-37777-2[details]
Kvam, P. D., Marley, A. A. J., & Heathcote, A. (2023). A Unified Theory of Discrete and Continuous Responding. Psychological Review, 130(2), 368-400. https://doi.org/10.1037/rev0000378[details]
Puri, R., Hinder, M. R., & Heathcote, A. (2023). What mechanisms mediate prior probability effects on rapid-choice decision-making? PLoS ONE, 18(7), Article e0288085. https://doi.org/10.1371/journal.pone.0288085[details]
Salomoni, S. E., Gronau, Q. F., Heathcote, A., Matzke, D., & Hinder, M. R. (2023). Proactive cues facilitate faster action reprogramming, but not stopping, in a response-selective stop signal task. Scientific Reports, 13, Article 19564. https://doi.org/10.1038/s41598-023-46592-0[details]
Strickland, L., Boag, R. J., Heathcote, A., Bowden, V., & Loft, S. (2023). Automated decision aids: When are they advisors and when do they take control of human decision making? Journal of Experimental Psychology: Applied, 29(4), 849-868. https://doi.org/10.1037/xap0000463[details]
Taylor, P., Walker, F. R., Heathcote, A., & Aidman, E. (2023). Effects of Multimodal Physical and Cognitive Fitness Training on Sustaining Mental Health and Job Readiness in a Military Cohort. Sustainability (Switzerland), 15(11), Article 9016. https://doi.org/10.3390/su15119016[details]
Weigard, A., Matzke, D., Tanis, C., & Heathcote, A. (2023). A cognitive process modeling framework for the ABCD study stop-signal task. Developmental Cognitive Neuroscience, 59, Article 101191. https://doi.org/10.1016/j.dcn.2022.101191[details]
Aidman, E., Fogarty, G. J., Crampton, J., Bond, J., Taylor, P., Heathcote, A., & Zaichkowsky, L. (2022). An app-enhanced cognitive fitness training program for athletes: The rationale and validation protocol. Frontiers in Psychology, 13, Article 957551. https://doi.org/10.3389/fpsyg.2022.957551[details]
Albertella, L., Kirkham, R., Adler, A. B., Crampton, J., Drummond, S. P. A., Fogarty, G. J., Gross, J. J., Zaichkowsky, L., Andersen, J. P., Bartone, P. T., Boga, D., Bond, J. W., Brunyé, T. T., Campbell, M. J., Ciobanu, L. G., Clark, S. R., Crane, M. F., Dietrich, A., Doty, T. J., ... Yücel, M. (2022). Building a transdisciplinary expert consensus on the cognitive drivers of performance under pressure: An international multi-panel Delphi study. Frontiers in Psychology, 13, 1017675. https://doi.org/10.3389/fpsyg.2022.1017675
Ballard, T., Neal, A., Farrell, S., Lloyd, E., Lim, J., & Heathcote, A. (2022). A General Architecture for Modeling the Dynamics of Goal-Directed Motivation and Decision-Making. Psychological Review, 129(1), 146-174. https://doi.org/10.1037/rev0000324[details]
Damaso, K. A. M., Castro, S. C., Todd, J., Strayer, D. L., Provost, A., Matzke, D., & Heathcote, A. (2022). A cognitive model of response omissions in distraction paradigms. Memory & Cognition, 50(5), 962-978. Advance online publication. https://doi.org/10.3758/s13421-021-01265-z[details]
Damaso, K. A. M., Williams, P. G., & Heathcote, A. (2022). What Happens After a Fast Versus Slow Error, and How Does It Relate to Evidence Accumulation? Computational Brain and Behavior, 5(4), 527-546. https://doi.org/10.1007/s42113-022-00137-2[details]
Elliott, D., Strickland, L., Loft, S., & Heathcote, A. (2022). Integrated responding improves prospective memory accuracy. Psychonomic Bulletin and Review, 29(3), 934-942. Advance online publication. https://doi.org/10.3758/s13423-021-02038-0[details]
Elliott, J. G. C., Gilboa-Schechtman, E., Grigorenko, E. L., Heathcote, A., Purdie-Greenaway, V. J., Uddin, L. Q., van der Maas, H. L. J., & Waldmann, M. R. (2022). Editorial. Psychological Review, 129(1), 1-3. https://doi.org/10.1037/rev0000359[details]
Hawkins, G. E., Mittner, M., Forstmann, B. U., & Heathcote, A. (2022). Self-reported mind wandering reflects executive control and selective attention. Psychonomic Bulletin and Review, 29(6), 2167-2180. https://doi.org/10.3758/s13423-022-02110-3[details]
He, J. L., Hirst, R. J., Puri, R., Coxon, J., Byblow, W., Hinder, M., Skippen, P., Matzke, D., Heathcote, A., Wadsley, C. G., Silk, T., Hyde, C., Parmar, D., Pedapati, E., Gilbert, D. L., Huddleston, D. A., Mostofsky, S., Leunissen, I., MacDonald, H. J., ... Puts, N. A. J. (2022). OSARI, an open-source anticipated response inhibition task. Behavior Research Methods, 54(3), 1530-1540. Advance online publication. https://doi.org/10.3758/s13428-021-01680-9[details]
Heathcote, A., & Matzke, D. (2022). Winner Takes All! What Are Race Models, and Why and How Should Psychologists Use Them? Current Directions in Psychological Science, 31(5), 383-394. https://doi.org/10.1177/09637214221095852[details]
Kumar, A., Benjamin, A. S., Heathcote, A., & Steyvers, M. (2022). Comparing models of learning and relearning in large-scale cognitive training data sets. NPJ Science of Learning, 7, Article 24. https://doi.org/10.1038/s41539-022-00142-x[details]
Strickland, L., Heathcote, A., Humphreys, M. S., & Loft, S. (2022). Target Learning in Event-Based Prospective Memory. Journal of Experimental Psychology: Learning Memory and Cognition, 48(8), 1110-1126. https://doi.org/10.1037/xlm0000900[details]
2021
Boehm, U., Matzke, D., Gretton, M., Castro, S., Cooper, J., Skinner, M., Strayer, D., & Heathcote, A. (2021). Real-time prediction of fluctuations in cognitive workload. Cognitive Research: Principles and Implications, 6, Article 30. https://doi.org/10.1186/s41235-021-00289-y[details]
Miletić, S., Boag, R. J., Trutti, A. C., Stevenson, N., Forstmann, B. U., & Heathcote, A. (2021). A new model of decision processing in instrumental learning tasks. eLife, 10, Article e63055. https://doi.org/10.7554/eLife.63055[details]
Tran, N-H., van Maanen, L., Heathcote, A., & Matzke, D. (2021). Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model. Frontiers in Psychology, 11, Article 608287. https://doi.org/10.3389/fpsyg.2020.608287[details]
Matzke, D., Logan, G. D., & Heathcote, A. (2020). A Cautionary Note on Evidence-Accumulation Models of Response Inhibition in the Stop-Signal Paradigm. Computational Brain & Behavior, 3(3), 269–288. Advance online publication. https://doi.org/10.1007/s42113-020-00075-x[details]
Skippen, P., Fulham, W. R., Michie, P. T., Matzke, D., Heathcote, A., & Karayanidis, F. (2020). Reconsidering electrophysiological markers of response inhibition in light of trigger failures in the stop-signal task. Psychophysiology, 57(10), Article e13619. https://doi.org/10.1111/psyp.13619[details]
Castro, S. C., Strayer, D. L., Matzke, D., & Heathcote, A. (2019). Cognitive workload measurement and modeling under divided attention. Journal of Experimental Psychology. Human Perception and Performance, 45(6), 826-839. Advance online publication. https://doi.org/10.1037/xhp0000638[details]
Dutilh, G., Annis, J., Brown, S. D., Cassey, P., Evans, N. J., Grasman, R. P. P. P., Hawkins, G. E., Heathcote, A., Holmes, W. R., Krypotos, A-M., Kupitz, C. N., Leite, F. P., Lerche, V., Lin, Y-S., Logan, G. D., Palmeri, T. J., Starns, J. J., Trueblood, J. S., van Maanen, L., ... Donkin, C. (2019). The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models. Psychonomic Bulletin & Review, 26(4), 1051-1069. Advance online publication. https://doi.org/10.3758/s13423-017-1417-2[details]
Heathcote, A., Lin, Y-S., Reynolds, A., Strickland, L., Gretton, M., & Matzke, D. (2019). Dynamic models of choice. Behavior Research Methods, 51(2), 961-985. https://doi.org/10.3758/s13428-018-1067-y[details]
Matzke, D., Curley, S., Gong, C. Q., & Heathcote, A. (2019). Inhibiting Responses to Difficult Choices. Journal of Experimental Psychology: General, 148(1), 124-142. https://doi.org/10.1037/xge0000525[details]
Skippen, P., Matzke, D., Heathcote, A., Fulham, W. R., Michie, P., & Karayanidis, F. (2019). Reliability of triggering inhibitory process is a better predictor of impulsivity than SSRT. Acta Psychologica, 192, 104-117. https://doi.org/10.1016/j.actpsy.2018.10.016[details]
Verbruggen, F., Aron, A. R., Band, G. P. H., Beste, C., Bissett, P. G., Brockett, A. T., Brown, J. W., Chamberlain, S. R., Chambers, C. D., Colonius, H., Colzato, L. S., Corneil, B. D., Coxon, J. P., Dupuis, A., Eagle, D. M., Garavan, H., Greenhouse, I., Heathcote, A., Huster, R. J., ... Boehler, C. N. (2019). A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLife, 8, Article e46323. https://doi.org/10.7554/eLife.46323[details]
Weigard, A., Heathcote, A., Matzke, D., & Huang-Pollock, C. (2019). Cognitive Modeling Suggests That Attentional Failures Drive Longer Stop-Signal Reaction Time Estimates in Attention Deficit/Hyperactivity Disorder. Clinical Psychological Science, 7(4), 856-872. https://doi.org/10.1177/2167702619838466[details]
Boehm, U., Annis, J., Frank, M. J., Hawkins, G. E., Heathcote, A., Kellen, D., Krypotos, A-M., Lerche, V., Logan, G. D., Palmeri, T. J., van Ravenzwaaij, D., Servant, M., Singmann, H., Starns, J. J., Voss, A., Wiecki, T. V., Matzke, D., & Wagenmakers, E-J. (2018). Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and recommendations. Journal of Mathematical Psychology, 87, 46-75. https://doi.org/10.1016/j.jmp.2018.09.004[details]
Evans, N. J., Brown, S. D., Mewhort, D. J. K., & Heathcote, A. (2018). Refining the Law of Practice. Psychological Review, 125(4), 592-605. https://doi.org/10.1037/rev0000105[details]
2017
Evans, N. J., Howard, Z. L., Heathcote, A., & Brown, S. D. (2017). Model flexibility analysis does not measure the persuasiveness of a fit. Psychological Review, 124(3), 339-345. https://doi.org/10.1037/rev0000057
Hawkins, G. E., Mittner, M., Forstmann, B. U., & Heathcote, A. (2017). On the efficiency of neurally-informed cognitive models to identify latent cognitive states. Journal of Mathematical Psychology, 76(Part B), 142-155. https://doi.org/10.1016/j.jmp.2016.06.007[details]
Matzke, D., Hughes, M., Badcock, J. C., Michie, P., & Heathcote, A. (2017). Failures of cognitive control or attention? The case of stop-signal deficits in schizophrenia. Attention, Perception & Psychophysics, 79(4), 1078-1086. Advance online publication. https://doi.org/10.3758/s13414-017-1287-8[details]
Matzke, D., Love, J., & Heathcote, A. (2017). A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm. Behavior Research Methods, 49(1), 267-281. Advance online publication. https://doi.org/10.3758/s13428-015-0695-8[details]
van Maanen, L., Forstmann, B. U., Keuken, M. C., Wagenmakers, E-J., & Heathcote, A. (2016). The impact of MRI scanner environment on perceptual decision-making. Behavior Research Methods, 48(1), 184-200. Advance online publication. https://doi.org/10.3758/s13428-015-0563-6[details]
Heathcote, A., Brown, S. D., & Wagenmakers, E-J. (2015). An introduction to good practices in cognitive modeling. In B. U. Forstmann, & E-J. Wagenmakers (Eds.), An introduction to model-based cognitive neuroscience (pp. 25-48). Springer. https://doi.org/10.1007/978-1-4939-2236-9_2[details]
Terry, A., Marley, A. A. J., Barnwal, A., Wagenmakers, E. J., Heathcote, A., & Brown, S. D. (2015). Generalising the drift rate distribution for linear ballistic accumulators. Journal of Mathematical Psychology, 68-69, 49-58. https://doi.org/10.1016/j.jmp.2015.09.002[details]
2014
Heathcote, A., Wagenmakers, E-J., & Brown, S. D. (2014). The falsifiability of actual decision-making models. Psychological Review, 121(4), 676-678. https://doi.org/10.1037/a0037771[details]
Mittner, M., Boekel, W., Tucker, A. M., Turner, B. M., Heathcote, A., & Forstmann, B. U. (2014). When the brain takes a break: A model-based analysis of mind wandering. The Journal of Neuroscience, 34(49), 16286-16295. https://doi.org/10.1523/JNEUROSCI.2062-14.2014[details]
Donkin, C., Brown, S., Heathcote, A., & Wagenmakers, E-J. (2011). Diffusion versus linear ballistic accumulation: different models but the same conclusions about psychological processes? Psychonomic Bulletin & Review, 18(1), 61-69. https://doi.org/10.3758/s13423-010-0022-4[details]
Mansfield, E. L., Karayanidis, F., Jamadar, S., Heathcote, A., & Forstmann, B. U. (2011). Adjustments of response threshold during task switching: a model-based functional magnetic resonance imaging study. The Journal of Neuroscience, 31(41), 14688-14692. https://doi.org/10.1523/JNEUROSCI.2390-11.2011[details]
2010
Heathcote, A., Brown, S., Wagenmakers, E-J., & Eidels, A. (2010). Distribution-free tests of stochastic dominance for small samples. Journal of Mathematical Psychology, 54(5), 454-463. https://doi.org/10.1016/j.jmp.2010.06.005[details]
Karayanidis, F., Jamadar, S., Ruge, H., Phillips, N., Heathcote, A., & Forstmann, B. U. (2010). Advance preparation in task-switching: converging evidence from behavioral, brain activation and model-based approaches. Frontiers in Psychology, 1, 25. Article 25. https://doi.org/10.3389/fpsyg.2010.00025[details]
van Doorn, J., Haaf, J. M., Stefan, A. M., Wagenmakers, E.-J., Cox, G. E., Davis-Stober, C. P., Heathcote, A., Heck, D. W., Kalish, M., Kellen, D., Matzke, D., Morey, R. D., Nicenboim, B., van Ravenzwaaij, D., Rouder, J. N., Schad, D. J., Shiffrin, R. M., Singmann, H., Vasishth, S., ... Aust, F. (2023). Bayes Factors for Mixed Models: a Discussion. Computational Brain and Behavior, 6(1), 140–158. https://doi.org/10.1007/s42113-022-00160-3[details]
Boehm, U., Matzke, D., Gretton, M., Castro, S., Cooper, J., Skinner, M., Strayer, D., & Heathcote, A. (2021). Correction to: Real-time prediction of short-timescale fluctuations in cognitive workload. Cognitive Research: Principles and Implications, 6, Article 62. https://doi.org/10.1186/s41235-021-00328-8
2018
Ly, A., Böhm, U., Heathcote, A., Turner, B. M., Forstmann, B., Marsman, M., & Matzke, D. (2018). A flexible and efficient hierarchical Bayesian approach to the exploration of individual differences in cognitive-model-based neuroscience. In A. A. Moustafa (Ed.), Computational Models of Brain and Behavior (pp. 467-480). Wiley Blackwell. https://doi.org/10.1002/9781119159193.ch34[details]
Prijs / subsidie
Matzke, D., Heathcote, A., Blanken, T. & Tanis, C. (2021). Small-Scale Initiatives in Software Performance Optimization.
Spreker
Matzke, D. (invited speaker) & Heathcote, A. (invited speaker) (15-5-2018). Dynamic Models of Choice (DMC) workshop, Heidelberg University.
Andere
Forstmann, B. (organiser), Matzke, D. (organiser), Heathcote, A. (organiser), Innes, R. (organiser) & Stevenson, N. (organiser) (1-8-2022 - 5-8-2022). Model-based Neuroscience Summer School (organising a conference, workshop, ...).
Forstmann, B. (organiser), Matzke, D. (organiser), Heathcote, A. (organiser), Miletić, S. (organiser) & Bazin, P.-L. (organiser) (5-8-2019 - 14-8-2019). Model-based Neuroscience Summer School and Brainhack (organising a conference, workshop, ...).
Forstmann, B. U. (organiser), Matzke, D. (organiser), van Maanen, L. (organiser) & Heathcote, A. (organiser) (30-7-2018 - 3-8-2018). Model-based Neuroscience Summer School (organising a conference, workshop, ...).
Forstmann, B. U. (organiser), Matzke, D. (organiser), Heathcote, A. (organiser), Hawkins, G. E. (organiser), van Maanen, L. (organiser) & de Hollander, G. (organiser) (31-7-2017 - 4-8-2017). Model-based Neuroscience Summer School (organising a conference, workshop, ...). http://(http://www.modelbasedneuroscience.c om/index.html
Heathcote, A. (organiser), Brown, S. (organiser), Matzke, D. (organiser), Turner, B. (organiser), Hawkins, G. E. (organiser) & Bushmakin, M. (organiser) (2016). Bayesian Estimation of Evidence Accumulation Architectures in Neuroscience and Cognition, Boston (organising a conference, workshop, ...).
2023
Isherwood, S., Bazin, P., Miletić, S., Stevenson, N., Trutti, A., H. Y. Tse, D., Heathcote, A., Matzke, D., Habli, S., Sokołowski, D. R., Alkemade, A., Håberg, A., Forstmann, B. & Innes, R. (8-6-2023). Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI data. Universiteit van Amsterdam. https://doi.org/10.21942/uva.22240393.v1
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