Van Rijn, prof. dr. Hedderik
Hedderik van Rijn is hoogleraar cognitie- en neurowetenschappen aan de Faculteit Gedrags- & Maatschappijwetenschappen. Van Rijn doet onderzoek naar de relatie tussen gedrag en tijd en probeert zo te begrijpen hoe cognitieve verwerking wordt beïnvloed door tijd. In 2022 hield Van Rijns onderzoeksgroep zich bezig met hoe wij tijd ervaren in ons brein. Dit onderzoek werd gepresenteerd in een interactieve tentoonstelling genaamd 'TIME WILL TELL' in het Universiteitsmuseum Groningen. Ook deed Van Rijn meermaals onderzoek naar waarom de tijd voor ons gevoel soms langzaam gaat en soms juist voorbij vliegt. Voor zijn onderzoeksvoorstel over het inschatten van tijd 'The stopwatch in our brains' ontving Van Rijn in 2017 een Vici-beurs.
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Publicaties
2024
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
Wilschut,
T., Sense, F., & van Rijn, H.
(2024). Modality Matters: Evidence for the Benefits of
Speech-Based Adaptive Retrieval Practice in Learners with
Dyslexia. Topics in Cognitive Science.
Advance online publication. https://doi.org/10.1111/tops.12769
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,
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