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Number of items at this level: 29.


Zednik, C. ; Jäkel, F. (2019):
Descending Marr’s levels: Standard observers are no panacea.
In: Behavioral and Brain Sciences, 41S. 43-44, [Article]


Hummel, P. A. ; Jäkel, F. ; Lange, S. ; Mertelsmann, R. Cox, M. ; Funk, P. ; Begum, S. (Hrsg.) (2018):
A Textual Recommender System for Clinical Data.
In: Lecture Notes in Computer Science, In: International Conference on Case-Based Reasoning 2018, S. 140-152, DOI: 10.1007/978-3-030-01081-2₁₀,
[Conference item]

Jäkel, Frank ; Worm, Oliver ; Lange, Sascha ; Mertelsmann, Roland (2018):
A stochastic model of myeloid cell lineages in hematopoiesis and pathway mutations in acute myeloid leukemia.
In: PloS one, 13 (10), S. 1-25, ISSN 1932-6203,
DOI: 10.1371/journal.pone.0204393,

Wichmann, F. A. ; Jäkel, F. Wixted, J. T. ; Wagenmakers, E. J. (Hrsg.) (2018):
Methods in Psychophysics.
In: Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, S. 265-306, [Online-Edition:],
[Book section]


Gershman, S. J. ; Tenenbaum, J. ; Jäkel, F. (2016):
Discovering hierarchical motion structure.
In: Vision Research, (126), S. 232-241, DOI: 10.1016/j.visres.2015.03.004,

Jäkel, F. ; Liu, M. (2016):
On interactivity in probabilistic pragmatics: yet another rational analysis of scalar implicatures.
In: Zeitschrift für Sprachwissenschaft, 35 (1), S. 69-87, DOI: 10.1515/zfs-2016-0005,

Jäkel, F. ; Singh, M. ; Wichmann, F. A. ; Herzog, M. H. (2016):
An overview of quantitative approaches in Gestalt perception.
In: Vision Research, (126), S. 3-8, DOI: 10.1016/j.visres.2016.06.004,

Zednik, C. ; Jäkel, F. (2016):
Bayesian reverse-engineering considered as a research strategy for cognitive science.
In: Synthese, S. 3951-3985, DOI: 10.1007/s11229-016-1180-3,


Collaboration, Open Science (2015):
Estimating the reproducibility of psychological science.
In: Science, 349S. 943, DOI: 10.1126/science.aac4716,

Schumacher, J. ; Wunderle, T. ; Fries, P. ; Jäkel, F. ; Pipa, G. (2015):
A Statistical Framework to Infer Delay and Direction of Information Flow from Measurements of Complex Systems.
In: Neural Computation, 27S. 1555-1608, DOI: 10.1162/NECOa₀₀₇₅₆,


Zednik, C. ; Jäkel, F. Bello, P. ; Guarini, M. ; McShane, M. ; Scasselati, B. (Hrsg.) (2014):
How does Bayesian reverse-engineering work?
Austin, TX, Cognitive Science Society, In: Proceedings of the 36th Annual Conference of the Cognitive Science Society, Austin, TX, S. 666-671, [Online-Edition:],
[Conference item]


Gershman, S. J. ; Jäkel, F. ; Tenenbaum, J. B. Knauff, M. ; Pauen, M. ; Sebanz, N. ; Wachsmuth, I. (Hrsg.) (2013):
Bayesian Vector Analysis and the Perception of Hierarchical Motion.
Austin, TX, Cognitive Science Society, In: Proceedings of the 35th Annual Conference of the Cognitive Science Society, Austin, TX, [Online-Edition:],
[Conference item]

Jäkel, F. ; Meyer, U. Stephan, A. ; Walter, S. (Hrsg.) (2013):
Kategorisierung und Begriffe.
In: Handbuch Kognitionswissenschaft, Stuttgart, Metzler, [Book section]

Jäkel, F. ; Schreiber, C. (2013):
Introspection in Problem Solving.
In: Journal of Problem Solving, 6 (1), S. 20-33, DOI: 10.7771/1932-6246.1131,

León-Villagrá, P. ; Jäkel, F. Knauff, M. ; Pauen, M. ; Sebanz, N. ; Wachsmuth, I. (Hrsg.) (2013):
Categorization and Abstract Similarity in Chess.
Austin, TX, In: Annual Conference of the Cognitive Science Society 2013, Austin, TX, [Online-Edition:],
[Conference item]

Stüttgen, M. C. ; Kasties, N. ; Lengersdorf, D. ; Starosta, S. ; Güntürkün, O. ; Jäkel, F. (2013):
Suboptimal criterion setting in a perceptual choice task with asymmetric reinforcement.
In: Behavioral Processes, 96S. 59-70, DOI: 10.1016/j.beproc.2013.02.014,


Fleming, R. ; Jäkel, F. ; Maloney, L. T. (2011):
Visual Perception of Thick Transparent Materials.
In: Psychological Science, 22S. 812-820, DOI: 10.1177/0956797611408734,

Stüttgen, M. C. ; Schwarz, C. ; Jäkel, F. (2011):
Mapping spikes to sensations.
In: Frontiers in Neuroscience, 5 (125), S. 1-17, DOI: 10.3389/fnins.2011.00125,


Jäkel, F. ; Schölkopf, B. ; Wichmann, F. A. (2009):
Does Cognitive Science Need Kernels.
In: Trends in Cognitive Sciences, 13S. 381-388, DOI: 10.1016/j.tics.2009.06.002,

Savova, V. ; Jäkel, F. ; Tenenbaum, J. B. Taatgen, N. ; van Rijn, H. (Hrsg.) (2009):
Grammar-based object representations in a scene parsing task.
Austin, TX, Cognitive Science Society, In: Proceedings of the 31st Annual Meeting of the Cognitive Science Society, Austin, TX, S. 857-862, [Online-Edition:],
[Conference item]


Jäkel, F. ; Schölkopf, B. ; Wichmann, F. A. (2008):
Generalization and Similarity in Exemplar Models of Categorization: Insights from Machine Learning.
In: Psychonomic Bulletin & Review, 15 (2), S. 256-271, DOI: 10.3758/PBR.15.2.256,

Jäkel, F. ; Schölkopf, B. ; Wichmann, F. A. (2008):
Similarity, Kernels and the Triangle Inequality.
In: Journal of Mathematical Psychology, 52 (5), S. 297-303, DOI: 10.1016/,


Cooke, T. ; Jäkel, F. ; Wallraven, C. ; Bülthoff, H. (2007):
Multimodal Similarity and Categorization of Novel, Three-Dimensional Objects.
In: Neuropsychologia, 45S. 484-495, DOI: 10.1016/j.neuropsychologia.2006.02.009,

Jäkel, F. ; Schölkopf, B. ; Wichmann, F. A. (2007):
A Tutorial on Kernel Methods for Categorization.
In: Journal of Mathematical Psychology, 51S. 343-358, DOI: 10.1016/,


Görür, D. ; Jäkel, F. ; Rasmussen, C. E. (2006):
A Choice Model with Infinitely Many Latent Features.
Pittsburgh, PA, In: Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, S. 8-15, [Online-Edition:],
[Conference item]

Jäkel, F. ; Wichmann, F. A. (2006):
Spatial four-alternative forced-choice method is the preferred psychophysical method for naïve observers.
In: Journal of Vision, 6 (11), S. 1307-1322, DOI: 10.1167/6.11.13,


Kuss, M. ; Jäkel, F. ; Wichmann, F. A. (2005):
Bayesian Inference for Psychometric Functions.
In: Journal of Vision, 5S. 478-492, DOI: 10.1167/5.5.8,


Jäkel, F. ; Ernst, M. O. Oakley, I. ; O'Modhrain, S. ; Newell, F. (Hrsg.) (2003):
Learning to Combine Arbitrary Signals from Vision and Touch.
Trinity College Dublin and Media Lab Europe, In: Eurohaptics 2003 Conference Proceedings, S. 276-290, [Conference item]


Storck, J. ; Jäkel, F. ; Deco, G. (2001):
Temporal clustering with spiking neurons and dynamic synapses: towards technological applications.
In: Neural Networks, 14 (3), S. 275-285, DOI: 10.1016/S0893-6080(00)00101-5,

This list was generated on Wed Jun 3 02:07:34 2020 CEST.