Van Rijn, Prof. Hedderik

Hedderik van Rijn is Professor of Cognitive Science and Neuroscience at the Faculty of Behavioural and Social Sciences. Van Rijn conducts research into the relationship between behaviour and time. In doing so, he hopes to understand how cognitive processing is influenced by time. In 2022, Van Rijn’s research group focused on how our brains experience time. This research was presented in the interactive exhibition ‘TIME WILL TELL’ at the University Museum in Groningen. Van Rijn has also repeatedly conducted research into why we sometimes feel that time is going slowly, while it flies by at other times. In 2017, Van Rijn received a Vici grant for his research proposal on estimating time, entitled ‘The stopwatch in our brains’.

How can a 'digital twin' help us learn better?

Hedderik van Rijn: why are people more agressive during summer?

Hedderik van Rijn about 'time as a peripheral system'

Vici Grant for Hedderik van Rijn

Hedderik van Rijn at the 'Universiteit van Nederland'

Previously in the news (mostly in Dutch)

Contact and more information

See profile page

Publications

2025

Zotos, L., van Rijn, H., & Nissim, M. (2025). Can Model Uncertainty Function as a Proxy for Multiple-Choice Question Item Difficulty? In O. Rambow, L. Wanner, M. Apidianaki, H. Al-Khalifa, B. Di Eugenio, & S. Schockaert (Eds.), Proceedings of the 31st International Conference on Computational Linguistics (pp. 11304–11316). Association for Computational Linguistics (ACL).

2024

Wilschut, T., Sense, F., & van Rijn, H. (2025). Modality Matters: Evidence for the Benefits of Speech-Based Adaptive Retrieval Practice in Learners with Dyslexia. Topics in Cognitive Science, 17(1), 57-72. https://doi.org/10.1111/tops.12769

Wilschut, T., Polsterer, K., Tienkamp, T., van Rijn, H., Sibert, C., & Abur, D. (2024). Association between Speech Motor Learning and Model-based Estimates of Memory Retrieval. Poster session presented at 13th International Seminar on Speech Production, Autrans, France.

de Jong, J., Akyürek, E., & van Rijn, H. (Accepted/In press). A Unified Neurocomputational Model of Prospective and Retrospective Timing. Psychological Review. https://doi.org/10.1037/rev0000519

van der Velde, M., Sense, F., Borst, J. P., & Rijn, H. V. (2024). Large-scale evaluation of cold-start mitigation in adaptive fact learning: Knowing “what” matters more than knowing “who”. User Modeling and User-Adapted Interaction, 34, 1467–1491. https://doi.org/10.1007/s11257-024-09401-5

Riemer, M., Wolbers, T., & van Rijn, H. (2024). Memory traces of duration and location in the right intraparietal sulcus. Neuroimage, 297, Article 120706. https://doi.org/10.1016/j.neuroimage.2024.120706

2023

Wilschut, T., Sense, F., & van Rijn, H. (2024). Speaking to remember: Model-based adaptive vocabulary learning using automatic speech recognition. Computer Speech and Language, 84, Article 101578. https://doi.org/10.1016/j.csl.2023.101578

Jong, J. D., Rijn, H. V., & Akyürek, E. G. (2023). Adaptive Encoding Speed in Working Memory. Psychological Science, 34(7), 822-833. https://doi.org/10.1177/09567976231173902

Hake, H. S., Leonard, B., Ulibarri, S., Grabowski, T., Rijn, H. V., & Stocco, A. (2023). Breaking New Ground in Computational Psychiatry: Model-Based Characterization of Forgetting in Healthy Aging and Mild Cognitive Impairment. MedRxiv. https://doi.org/10.1101/2023.05.13.23289941

Wilschut, T., Sense, F., Scharenborg, O., & van Rijn, H. (2023). Improving Adaptive Learning Models Using Prosodic Speech Features. In N. Wang, G. Rebolledo-Mendez, N. Matsuda, O. C. Santos, & V. Dimitrova (Eds.), Artificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings (pp. 255-266). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13916 LNAI). Springer. https://doi.org/10.1007/978-3-031-36272-9_21

Wilschut, T., Velde, M. V. D., Sense, F., & Rijn, H. V. (2023). Prior Knowledge Norms for Naming Country Outlines: An Open Stimulus Set. Journal of Cognition, 6(1), Article 14. https://doi.org/10.5334/joc.260

2022

Salet, J. M., Schlichting, N., Kruijne, W., & van Rijn, H. (2022). Addendum: Implicit learning of temporal behavior in complex dynamic environments. Psychonomic Bulletin and Review, 29(6), 2325-2329. https://doi.org/10.3758/s13423-022-02194-x

van der Mijn, R., & van Rijn, H. (2022). Attentional Effects on the Subjective Passage of Time While Driving. Poster session presented at 18th Winter conference on Brain and Cognition, Egmond aan Zee, Netherlands.

van der Velde, M., Sense, F., Borst, J. P., van Maanen, L., & van Rijn, H. (2022). Capturing Dynamic Performance in a Cognitive Model: Estimating ACT-R Memory Parameters With the Linear Ballistic Accumulator. Topics in Cognitive Science, 14(4), 889-903. https://doi.org/10.1111/tops.12614

Salet, J. M., Kruijne, W., van Rijn, H., Los, S. A., & Meeter, M. (2022). FMTP: A unifying computational framework of temporal preparation across time scales. Psychological Review, 129(5), 911-948. https://doi.org/10.1037/rev0000356

Riemer, M., Vieweg, P., van Rijn, H., & Wolbers, T. (2022). Reducing the tendency for chronometric counting in duration discrimination tasks. Attention, Perception, and Psychophysics, 84, 2641–2654. https://doi.org/10.3758/s13414-022-02523-1

Salet, J. M., de Jong, J., & van Rijn, H. (2022). Still Stuck With the Stopwatch. Behavioral Neuroscience, 136(5), 453-466. https://doi.org/10.1037/bne0000527

2021

Maass, S., Wolbers, T., van Rijn, H., & Riemer, M. (2022). Temporal context effects are associated with cognitive status in advanced age. Psychological research-Psychologische forschung, 86, 512-521. https://doi.org/10.1007/s00426-021-01502-9

de Jong, J., Akyurek, E. G., & van Rijn, H. (2021). A common dynamic prior for time in duration discrimination. Psychonomic Bulletin & Review, 28, 1183–1190. https://doi.org/10.3758/s13423-021-01887-z

Riemer, M., Wolbers, T., & van Rijn, H. (2021). Age-related changes in time perception: The impact of naturalistic environments and retrospective judgements on timing performance. Quarterly Journal of Experimental Psychology, 74(11), 2002-2012. https://doi.org/10.1177/17470218211023362

van der Velde, M., Sense, F., Borst, J., & van Rijn, H. (2021). Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates. Computational Brain and Behavior, 4(2), 231-249. https://doi.org/10.1007/s42113-021-00101-6

Wilschut, T., Sense, F., van der Velde, M., Fountas, Z., Maaß, S. C., & van Rijn, H. (2021). Benefits of Adaptive Learning Transfer From Typing-Based Learning to Speech-Based Learning. Frontiers in Artificial Intelligence, 4, Article 780131. https://doi.org/10.3389/frai.2021.780131

van der Velde, M., Sense, F., Borst, J., & van Rijn, H. (2021). Capturing dynamic performance in a cognitive model: Estimating ACT-R memory parameters with the linear ballistic accumulator. Paper presented at International Conference on Cognitive Modeling 2021.

Kruijne, W., & van Rijn, H. (2021). Change biases identify the features that drive time perception. Journal of Experimental Psychology : Human Perception and Performance, 47(9), 1192-1208. https://doi.org/10.1037/xhp0000934

Maaß, S. C., de Jong, J., van Maanen, L., & van Rijn, H. (2021). Conceptually plausible Bayesian inference in interval timing. Royal Society Open Science, 8(8), Article 201844. https://doi.org/10.1098/rsos.201844

Berberyan, H. S., van Rijn, H., & Borst, J. P. (2021). Discovering the brain stages of lexical decision: Behavioral effects originate from a single neural decision process. Brain and Cognition, 153, Article 105786. https://doi.org/10.1016/j.bandc.2021.105786

Berberyan, H. S., van Maanen, L., van Rijn, H., & Borst, J. (2021). EEG-based Identification of Evidence Accumulation Stages in Decision-Making. Journal of Cognitive Neuroscience, 33(3), 510-527. https://doi.org/10.1162/jocn_a_01663

Damsma, A., Schlichting, N., van Rijn, H., & Roseboom, W. (2021). Estimating Time: Comparing the Accuracy of Estimation Methods for Interval Timing. Collabra: Psychology, 7(1). https://doi.org/10.1525/collabra.21422

Vogelzang, M., Guasti, M. T., van Rijn, H., & Hendriks, P. (2021). How children process reduced forms: A computational cognitive modeling approach to pronoun processing in discourse. Cognitive Science, 45(4), Article e12951. https://doi.org/10.1111/cogs.12951

Salet, J. M., Kruijne, W., & van Rijn, H. (2021). Implicit learning of temporal behavior in complex dynamic environments. Psychonomic Bulletin & Review, 28(4), 1270-1280. https://doi.org/10.3758/s13423-020-01873-x

van der Velde, M., Sense, F., Spijkers, R., Meeter, M., & van Rijn, H. (2021). Lockdown Learning: Changes in Online Foreign-Language Study Activity and Performance of Dutch Secondary School Students During the COVID-19 Pandemic. Frontiers in Education, 6, Article 712987. https://doi.org/10.3389/feduc.2021.712987

Kruijne, W., Olivers, C. N. L., & van Rijn, H. (2021). Memory for Stimulus Duration Is Not Bound to Spatial Information. Journal of Cognitive Neuroscience, 33(7), 1211-1229. https://doi.org/10.1162/jocn_a_01723

van der Velde, M., Sense, F., Borst, J., den Hartigh, R., Baatenburg de Jong, M., & van Rijn, H. (2021). Memory Performance in Special Forces: Speedier Responses Explain Improved Task Performance after Physical Exertion. Poster session presented at 43rd Annual Meeting of the Cognitive Science Society.

Kruijne, W., Olivers, C. N. L., & van Rijn, H. (2021). Neural Repetition Suppression Modulates Time Perception: Evidence From Electrophysiology and Pupillometry. Journal of Cognitive Neuroscience, 33(7), 1230-1252. https://doi.org/10.1101/2020.07.31.230508, https://doi.org/10.1162/jocn_a_01705

Sense, F., van der Velde, M., & van Rijn, H. (2021). Predicting University Students’ Exam Performance Using a Model-Based Adaptive Fact-Learning System. Journal of Learning Analytics, 8(3), 155-169. https://doi.org/10.18608/jla.2021.6590

Zhou, P., Sense, F., van Rijn, H., & Stocco, A. (2021). Reflections of idiographic long-term memory characteristics in resting-state neuroimaging data. Cognition, 212, Article 104660. https://doi.org/10.1016/j.cognition.2021.104660

Damsma, A., Schlichting, N., & van Rijn, H. (2021). Temporal context actively shapes EEG signatures of time perception. Journal of Neuroscience, 41(20), 4514-4523. https://doi.org/10.1523/JNEUROSCI.0628-20.2021

Kingma, B. R. M., Roijendijk, L. M. M., Van Maanen, L., Van Rijn, H., & Van Beurden, M. H. P. H. (2021). Time perception and timed decision task performance during passive heat stress. Temperature, 8(1), 53-63. https://doi.org/10.1080/23328940.2020.1776925

Wilschut, T., van der Velde, M., Sense, F., Fountas, Z., & van Rijn, H. (2021). Translating a Typing-Based Adaptive Learning Model to Speech-Based L2 Vocabulary Learning. 245-250. Paper presented at 29th ACM Conference on User Modeling, Adaptation and Personalization. https://doi.org/10.1145/3450613.3456825

2020

van der Mijn, R., & van Rijn, H. (2021). Attention Does Not Affect the Speed of Subjective Time, but Whether Temporal Information Guides Performance: A Large-Scale Study of Intrinsically Motivated Timers in a Real-Time Strategy Game. Cognitive Science, 45(3), Article 12939. https://doi.org/10.1111/cogs.12939

van der Mijn, R., Damsma, A., Taatgen, N., & van Rijn, H. (2021). Individual optimization of risky decisions in duration and distance estimations. Attention, Perception, & Psychophysics, 83, 1897–1906. https://doi.org/10.3758/s13414-020-02225-6

van der Velde, M., Sense, F., Borst, J., & van Rijn, H. (2020). Kickstarting adaptive fact learning using hierarchical Bayesian modelling. In T. C. Stewart (Ed.), Proceedings of ICCM 2019 - 17th International Conference on Cognitive Modeling (pp. 275-276). Applied Cognitive Science Lab, Penn State.

Kononowicz, T. W., Sander, T., van Rijn, H., & van Wassenhove, V. (2020). Precision Timing with alpha-beta Oscillatory Coupling: Stopwatch or Motor Control? Journal of Cognitive Neuroscience, 32(9), 1624-1636. https://doi.org/10.1162/jocn_a_01570

2019

Damsma, A., Taatgen, N., de Jong, R., & van Rijn, H. (2020). No evidence for an attentional bias towards implicit temporal regularities. Attention, Perception & Psychophysics, 82(3), 1136-1149. https://doi.org/10.3758/s13414-019-01851-z

Vogelzang, M., Foppolo, F., Guasti, M. T., van Rijn, H., & Hendriks, P. (2020). Reasoning about alternative forms is costly: The processing of null and overt pronouns in Italian using pupillary responses. Discourse Processes, 57(2), 158-183. https://doi.org/10.1080/0163853X.2019.1591127

Velde, van der, M., Sense, F., Borst, J., & Rijn, van, H. (2019). Alleviating the Cold-Start Problem in Adaptive Fact Learning Using Bayesian Modelling. Abstract from 17th NVP Winter Conference on Brain & Cognition, Egmond aan Zee, Netherlands.

van Rij, J., Hendriks, P., van Rijn, H., Baayen, R. H., & Wood, S. N. (2019). Analyzing the Time Course of Pupillometric Data. Trends in hearing, 23, 1-22. https://doi.org/10.1177/2331216519832483

Maaß, S., Van Maanen, L., & Rijn, van, H. (2019). An Improved Bayesian Observer Model to Explain Temporal Context Effects in Clinical and Healthy Aged Populations. Paper presented at 2nd Annual Conference of the Timing Research Forum, Queretaro, Mexico.

Sense, F., Jastrzembski, T., Krusmark, M., Martinez, S., & van Rijn, H. (2019). An Integrated Trial-Level Performance Measure: Combining Accuracy and RT to Express Performance During Learning. In Proceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019 (pp. 1029-1034). The Cognitive Science Society.

Berberyan, H., Rijn, van, H., & Borst, J. (2019). Can we directly observe stages of cognitive processing? HsMM-EEG analysis of a Visual Discrimination Task. Poster session presented at NVP winterconference on Brain & Cognition, Egmond aan Zee, Netherlands. https://doi.org/10.13140/RG.2.2.28411.05924

van Maanen, L., van der Mijn, R., van Beurden, M. H. P. H., Roijendijk, L. M. M., Kingma, B. R. M., Miletić, S., & van Rijn, H. (2019). Core body temperature speeds up temporal processing and choice behavior under deadlines. Scientific Reports, 9, Article 10053. https://doi.org/10.1038/s41598-019-46073-3

van den Broek, G. S. E., Segers, E., van Rijn, H., Takashima, A., & Verhoeven, L. (2019). Effects of Elaborate Feedback During Practice Tests: Costs and Benefits of Retrieval Prompts. Journal of experimental psychology-Applied, 25(4), 588-601. https://doi.org/10.1037/xap0000212

Maass, S. C., Schlichting, N., & van Rijn, H. (2019). Eliciting contextual temporal calibration: The effect of bottom-up and top-down information in reproduction tasks. Acta Psychologica, 199, Article 102898. https://doi.org/10.1016/j.actpsy.2019.102898

de Jong, J., Voelker, A., van Rijn, H., Stewart, T., & Eliasmith, C. (2019). Flexible timing with delay networks–The scalar property and neural scaling. In T. C. Stewart (Ed.), Proceedings of ICCM 2019 - 17th International Conference on Cognitive Modeling (pp. 77-82). (Proceedings of ICCM 2019 - 17th International Conference on Cognitive Modeling). Applied Cognitive Science Lab, Penn State.

Salet, J., Kruijne, W., & Rijn, van, H. (2019). fMTP: A Unifying Computational Framework of Temporal Preparation Across Time Scales.

Maaß, S., Sense, F., Gluck, K., & van Rijn, H. (2019). Keeping Bystanders Active: Resuscitating Resuscitation Skills. Frontiers in Public Health, 7, Article 177. https://doi.org/10.3389/fpubh.2019.00177

van der Velde, M., Sense, F., Borst, J., & van Rijn, H. (2019). Kickstarting Adaptive Fact Learning Using Bayesian Modelling. Poster session presented at 17th International Conference on Cognitive Modeling, Montreal, Quebec, Canada.

van der Mijn, R., & van Rijn, H. (2019). Motivated Timing in a Real-Time Strategy Game: Starcraft2. Poster session presented at 2nd Annual Conference of the Timing Research Forum, Queretaro, Mexico.

Sense, F., Jastrzembski, T. S., Mozer, M. C., Krusmark, M., & Rijn, van, H. (2019). Perspectives on Computational Models of Learning and Forgetting. In T. C. Stewart (Ed.), Proceedings of ICCM 2019: 17th International Conference on Cognitive Modelling (pp. 216-221). Applied Cognitive Science Lab.

Gluck, K., Collins, M. G., Krusmark, M., Sense, F., Maaß, S., & Rijn, van, H. (2019). Predicting Performance in Cardiopulmonary Resuscitation. In T. C. Stewart (Ed.), Proceedings of ICCM 2019: 17th International Conference on Cognitive Modelling (pp. 53-58). Applied Cognitive Science Lab.

Collins, M. W., Jastrzembski, T. S., Krusmark, M., Sense, F., Rijn, van, H., Harris, J., Gaines, A., Haubert, A., & Martinez, S. (2019). Predictive Validity of a Cognitive Model in a Naturalistic Language Learning Task. In T. C. Stewart (Ed.), Proceedings of ICCM 2019: 17th International Conference on Cognitive Modelling Applied Cognitive Science Lab.

van Maanen, L., & van Rijn, H. (2019). The observed locus of semantic interference may not coincide with the functional locus of semantic interference: A commentary on Shitova et al. Cortex, 111, 327-332. https://doi.org/10.1016/j.cortex.2018.10.025

Maaß, S. C., Riemer, M., Wolbers, T., & van Rijn, H. (2019). Timing deficiencies in amnestic Mild Cognitive Impairment: Disentangling clock and memory processes. Behavioral Brain Research, 373, Article 112110. https://doi.org/10.1016/j.bbr.2019.112110

Salet, J., Kruijne, W., & Rijn, van, H. (2019). Whac-A-Mole: Implicit Adaptation to Temporal Regularities.

Sense, F., Maaß, S., Gluck, K., & van Rijn, H. (2019). Within-Subject Performance on a Real-Life, Complex Task and Traditional Lab Experiments: Measures of Word Learning, Raven Matrices, Tapping, and CPR. Journal of Cognition, 2(1). https://doi.org/10.5334/joc.65

2018

Schlichting, N., de Jong, R., & van Rijn, H. (2020). Performance-informed EEG analysis reveals mixed evidence for EEG signatures unique to the processing of time. Psychological Research : An International Journal of Perception, Attention, Memory, and Action, 84(2), 352-369. https://doi.org/10.1007/s00426-018-1039-y

Hallez, Q., Damsma, A., Rhodes, D., van Rijn, H., & Droit-Volet, S. (2019). The dynamic effect of context on interval timing in children and adults. Acta Psychologica, 192, 87-93. https://doi.org/10.1016/j.actpsy.2018.10.004

Maass, S. C., & van Rijn, H. (2018). 1-s Productions: A Validation of an Efficient Measure of Clock Variability. Frontiers in Human Neuroscience, 12, Article 519. https://doi.org/10.3389/fnhum.2018.00519

Soto-Padilla, A., Ruijsink, R., Span, M., van Rijn, H., & Billeter, J.-C. (2018). An automated method to determine the performance of Drosophila in response to temperature changes in space and time. Journal of Visualized Experiments, 2018(140), Article e58350. https://doi.org/10.3791/58350

Damsma, A., Schlichting, N., Eike, R., & van Rijn, D. (2018). Decoding the influence of context on time perception. Poster session presented at Annual meeting of the society for Neuroscience, San Diego, United States.

Sense, F., van der Velde, M., & van Rijn, H. (2018). Deploying a Model-based Adaptive Fact-Learning System in University Courses. Poster session presented at 16th International Conference on Cognitive Modeling. https://iccm-conference.neocities.org/2018/proceedings/ICCM%202018%20Proceedings.pdf

Sense, F., Meijer, R. R., & van Rijn, H. (2018). Exploration of the Rate of Forgetting as a Domain-Specific Individual Differences Measure. Frontiers in Education, 3, Article 112. https://doi.org/10.3389/feduc.2018.00112

Sense, F., & van Rijn, H. (2018). Probabilistic Motor Sequence Learning in a Virtual Reality Serial Reaction Time Task. PLoS ONE, 13(6), Article e0198759. https://doi.org/10.1371/journal.pone.0198759

Schlichting, N., Damsma, A., Ziegler, M. P., de Jong, R., & van Rijn, D. (2018). Quantifying attention and its effect on interval timing. Poster session presented at Annual meeting of the society for Neuroscience, San Diego, United States.

Schlichting, N., de Jong, R., & van Rijn, H. (2018). Robustness of individual differences in temporal interference effects. PLoS ONE, 13(8), Article e0202345. https://doi.org/10.1371/journal.pone.0202345

Schlichting, N., de Jong, R., & van Rijn, D. (2018). Robustness of individual differences in temporal interference effects. Abstract from International Meeting of the Psychonomic Society, Amsterdam, Netherlands.

Schlichting, N., Damsma, A., Aksoy, E. E., Wächter, M., Asfour, T., & van Rijn, D. (2018). Temporal context influences the perceived duration of everyday actions: Assessing the ecological validity of lab-based timing phenomena. Journal of Cognition, 2(1), Article 4. https://doi.org/10.5334/joc.4

Soto-Padilla, A., Ruijsink, R., Sibon, O. C. M., van Rijn, H., & Billeter, J.-C. (2018). Thermosensory perception regulates speed of movement in response to temperature changes in Drosophila melanogaster. Journal of Experimental Biology, 221(10), Article 74151. https://doi.org/10.1242/jeb.174151

Kononowicz, T. W., van Rijn, H., & Meck, W. H. (2018). Timing and Time Perception: A Critical Review of Neural Timing Signatures Before, During, and After the To‐Be‐Timed Interval . In Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience (Vol. 1). JOHN WILEY & SONS INC. https://doi.org/10.1002/9781119170174.epcn114

van Rijn, H. (2018). Towards Ecologically Valid Interval Timing. Trends in Cognitive Sciences, 22(10), 850-852. https://doi.org/10.1016/j.tics.2018.07.008

2017

Damsma, A., van der Mijn, R., & van Rijn, H. (2018). Neural markers of memory consolidation do not predict temporal estimates of encoded items. Neuropsychologia, 117, 36-45. Article 5. https://doi.org/10.1016/j.neuropsychologia.2018.04.039

Maaß, S., Sense, F., Walsh, M. M., Gluck, K., & van Rijn, D. (2017). Cognitive modeling of cardiopulmonary resuscitation knowledge and skill spanning months to years. Poster session presented at 15th International Conference on Cognitive Modeling, Coventry, United Kingdom.

Maaß, S., & van Rijn, D. (2017). Instantaneous modulations of perceived time: A review and empirical data. Poster session presented at 1st Conference of The Timing Research Forum, Strasbourg, France.

van der Mijn, R., Damsma, A., & van Rijn, D. (2017). Neural markers of memory consolidation do not predict temporal estimates of encoded items. Poster session presented at Winter Conference Nederlandse Vereniging voor Psychonomie 2017, Egmond aan Zee, Netherlands.

Damsma, A., van der Mijn, R., & van Rijn, D. (2017). Neural markers of memory consolidation do not predict temporal estimates of encoded items. Abstract from TEX2017 - Trieste Encounters in Cognitive Sciences, Trieste, Italy.

Damsma, A., de Jong, R., Taatgen, N., & van Rijn, D. (2017). No Evidence For Improved Attention Towards Implicit Temporal Regularities. Poster session presented at 1st Conference of The Timing Research Forum, Strasbourg, France.

Damsma, A., de Jong, R., Taatgen, N., & van Rijn, D. (2017). No Evidence For Improved Attention Towards Implicit Temporal Regularities. Poster session presented at Winter Conference Nederlandse Vereniging voor Psychonomie 2017, Egmond aan Zee, Netherlands.

Damsma, A., & van Rijn, D. (2017). Pupil dilation indexes the metrical hierarchy of unattended rhythmic violations. Abstract from Conference on Music & Eye-Tracking, Frankfurt, Germany.

Damsma, A., & van Rijn, D. (2017). Pupil dilation indexes the metrical hierarchy of unattended rhythmic violations. Abstract from 19th European Conference on Eye Movements, Wuppertal, Germany.

Damsma, A., & van Rijn, H. (2017). Pupillary response indexes the metrical hierarchy of unattended rhythmic violations. Brain and Cognition, 111, 95-103. https://doi.org/10.1016/j.bandc.2016.10.004

Schlichting, N., de Jong, R., & van Rijn, D. (2017). Robustness of individual differences in temporal interference effects. Poster session presented at 1st Conference of The Timing Research Forum, Strasbourg, France.

Schlichting, N., de Jong, R., & van Rijn, D. (2017). Robustness of individual differences in temporal interference effects. Abstract from TEX2017 - Trieste Encounters in Cognitive Sciences, Trieste, Italy.

Vogelzang, M., Mills, A. C., Reitter, D., van Rij, J., Hendriks, P., & van Rijn, H. (2017). Toward cognitively constrained models of language processing: A review. Frontiers in Communication, 2(11), 1-19. https://doi.org/10.3389/fcomm.2017.00011

Akyürek, E. G., Kappelmann, N., Volkert, M., & van Rijn, H. (2017). What you see is what you remember: Visual chunking by temporal integration enhances working memory. Journal of Cognitive Neuroscience, 29(12), 2025-2036. https://doi.org/10.1162/jocn_a_01175

2016

van Rijn, H. (2016). Accounting for memory mechanisms in interval timing: a review. Current Opinion in Behavioral Sciences, 8, 245-249. https://doi.org/10.1016/j.cobeha.2016.02.016

Halbertsma, H. N., & Van Rijn, H. (2016). An Evaluation of the Effect of Auditory Emotional Stimuli on Interval Timing. Timing and Time Perception, 4(1), 48-62. https://doi.org/10.1163/22134468-00002061

Sense, F., Behrens, F., Meijer, R. R., & van Rijn, H. (2016). An Individual's Rate of Forgetting Is Stable Over Time but Differs Across Materials. Topics in Cognitive Science, 8(1), 305-321. https://doi.org/10.1111/tops.12183

van Rijn, H., & Taatgen, N. A. (2016). An integrative account of psychological time. In S. E. F. Chipman (Ed.), The Oxford Handbook of Cognitive Science (pp. 151-168). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199842193.013.18

Nijboer, M., Borst, J., van Rijn, H., & Taatgen, N. (2016). Contrasting single and multi-component working-memory systems in dual tasking. Cognitive psychology, 86, 1-26. https://doi.org/10.1016/j.cogpsych.2016.01.003

Schlichting, N., de Jong, R., & van Rijn, D. (2016). Disentangling temporal & numerical magnitude processing. Poster session presented at Time in Tokyo, Tokyo, Japan.

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19 May 2025 09.18 a.m.

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