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Heiko Schütt
Heiko Schütt
PostDoc, NYU-CNS & Columbia-ZI
Verified email at nyu.edu
Title
Cited by
Cited by
Year
Generalisation in humans and deep neural networks
R Geirhos, CRM Temme, J Rauber, HH Schütt, M Bethge, FA Wichmann
Advances in neural information processing systems 31, 2018
504*2018
Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data
HH Schütt, S Harmeling, JH Macke, FA Wichmann
Vision research 122, 105-123, 2016
2302016
Likelihood-based parameter estimation and comparison of dynamical cognitive models.
HH Schütt, LOM Rothkegel, HA Trukenbrod, S Reich, FA Wichmann, ...
Psychological Review 124 (4), 505, 2017
332017
An image-computable psychophysical spatial vision model
HH Schütt, FA Wichmann
Journal of vision 17 (12), 12-12, 2017
312017
Temporal evolution of the central fixation bias in scene viewing
LOM Rothkegel, HA Trukenbrod, HH Schütt, FA Wichmann, R Engbert
Journal of vision 17 (13), 3-3, 2017
272017
Disentangling bottom-up versus top-down and low-level versus high-level influences on eye movements over time
HH Schütt, LOM Rothkegel, HA Trukenbrod, R Engbert, FA Wichmann
Journal of vision 19 (3), 1-1, 2019
232019
Influence of initial fixation position in scene viewing
LOM Rothkegel, HA Trukenbrod, HH Schütt, FA Wichmann, R Engbert
Vision research 129, 33-49, 2016
182016
Searchers adjust their eye-movement dynamics to target characteristics in natural scenes
LOM Rothkegel, HH Schütt, HA Trukenbrod, FA Wichmann, R Engbert
Scientific reports 9 (1), 1-12, 2019
142019
Comparing representational geometries using whitened unbiased-distance-matrix similarity
J Diedrichsen, E Berlot, M Mur, HH Schütt, M Shahbazi, N Kriegeskorte
arXiv preprint arXiv:2007.02789, 2020
13*2020
Comparing deep neural networks against humans: Object recognition when the signal gets weaker. arXiv 2017
R Geirhos, DHJ Janssen, HH Schütt, J Rauber, M Bethge, FA Wichmann
arXiv preprint arXiv:1706.06969, 2018
112018
Methods and measurements to compare men against machines
FA Wichmann, DHJ Janssen, R Geirhos, G Aguilar, HH Schütt, ...
Electronic Imaging 2017 (14), 36-45, 2017
112017
Comparing deep neural networks against humans: object recognition when the signal gets weaker. 2017
R Geirhos, DHJ Janssen, HH Schütt, J Rauber, M Bethge, FA Wichmann
arXiv preprint arXiv:1706.06969, 0
9
Perception of light source distance from shading patterns
HH Schütt, F Baier, RW Fleming
Journal of Vision 16 (3), 9-9, 2016
62016
Of human observers and deep neural networks: A detailed psychophysical comparison
R Geirhos, D Janssen, H Schütt, M Bethge, F Wichmann
Journal of Vision 17 (10), 806-806, 2017
22017
Point estimate observers: A new class of models for perceptual decision making
H Schütt, A Yoo, J Calder-Travis, WJ Ma
PsyArXiv, 2021
12021
Color constancy in deep neural networks
AC Flachot, HH Schuett, RW Fleming, F Wichmann, KR Gegenfurtner
Journal of Vision 19 (10), 298-298, 2019
12019
Deep neural models for color classification and color constancy
A Flachot, A Akbarinia, HH Schütt, RW Fleming, FA Wichmann, ...
Journal of Vision 22 (4), 17-17, 2022
2022
Statistical inference on representational geometries
HH Schütt, AD Kipnis, J Diedrichsen, N Kriegeskorte
arXiv preprint arXiv:2112.09200, 2021
2021
Deep Neural Models for color discrimination and color constancy
A Flachot, A Akbarinia, HH Schütt, RW Fleming, FA Wichmann, ...
arXiv preprint arXiv:2012.14402, 2020
2020
A divisive model of midget and parasol ganglion cells explains the contrast sensitivity function
HH Schütt, FA Wichmann
Journal of Vision 19 (10), 79a-79a, 2019
2019
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Articles 1–20