Frank Jäkel

Frank Jäkel (Principal Investigator)

Frank's research interests range from visual perception to problem solving. However, most of the time he studies category learning in humans and machines. He also likes psychophysics, statistics, and machine learning.

Before joining the Centre for Cognitive Science at TU Darmstadt, Frank was an assistant professor at the Institute of Cognitive Science at the University of Osnabrück. He was a postdoctoral fellow at MIT working with Josh Tenebaum and he did his doctoral work under the supervision of Felix Wichmann and Bernhard Schölkopf at the MPI for Biological Cybernetics. He studied Cognitive Science, Neural & Behavioural Sciences, and Artificial Intelligence in Osnabrück, Tübingen, and Edinburgh.


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Number of items: 29.


Stoilova, V. V. ; Knauer, B. ; Berg, S. ; Rieber, E. ; Jäkel, F. ; Stüttgen, M. C. (2020):
Auditory cortex reflects goal-directed movement but is not necessary for behavioral adaptation in sound-cued reward tracking.
In: Journal of Neurophysiology, American Physiological Society, ISSN 0022-3077,
DOI: 10.1152/jn.00736.2019,


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]

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]

Depeweg, S. ; Rothkopf, C. A. ; Jäkel, F. (2018):
Solving Bongard Problems with a visual language and pragmatic reasoning.
In: arXiv, [Online-Edition:],


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,

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. ; 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,

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,


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]


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

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,

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

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


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. (2009):
Does Cognitive Science Need Kernels.
In: Trends in Cognitive Sciences, 13S. 381-388, DOI: 10.1016/j.tics.2009.06.002,


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/,

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,


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 Mon Sep 21 06:45:40 2020 CEST.