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Links to open access publications are provided. Articles behind paywalls: Please klick "get PDF via e-mail" to obtain the article from us.

Ahlert, J., Klein, T., Wichmann, F. A., and Geirhos, R. (2024). How aligned are different alignment metrics? International Conference on Learning Representations (ICLR), ReAlign Workshop, 2024.

Bertalmío, M., Durán Vizcaíno, A., Malo, J., and Wichmann, F. A. (2024). Plaid masking explained with input-dependent dendritic nonlinearities. Scientific Reports, 14(1), 1–7. 

Huber, L. S., Mast, F. W., and Wichmann, F. A. (2024). Immediate generalisation in humans but a generalisation lag in deep neural networks—evidence for representational divergence? International Conference on Learning Representations (ICLR), ReAlign Workshop, 2024.

Künstle, D.-E., and von Luxburg, U. (2024). cblearn: Comparison-based Machine Learning in Python. Journal of Open Source Software, 9(98), 6139.

Sauer, Y., Künstle, D.-E., Wichmann, F. A., and Wahl, S. (2024). An objective measurement approach to quantify the perceived distortions of spectacle lenses. Scientific Reports, 14:3967, 1-10.

Schmittwilken, L., Wichmann, F. A., and Maertens, M. (2024). Standard models of spatial vision mispredict edge sensitivity at low spatial frequencies. Vision Research, 222(108450), 1-13.

Huber, L. S., Geirhos, R., and Wichmann, F. A. (2023). The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks. Journal of Vision, 23(7):4, 1-30.

Wichmann, F. A., and Geirhos, R. (2023). Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception? Annual Review of Vision Science, 9:7.1–7.24.

Wichmann, F. A., Kornblith, S., and Geirhos, R. (2023). Neither hype nor gloom do DNNs justice. Behavioral and Brain Sciences, 46, 63-64.[get PDF via e-mail].

Zimmermann, R., Klein, T., and Brendel, W. (2023). Scale Alone does not Improve Mechanistic Interpretability in Vision Models.  37th Conference on Neural Information Processing Systems (NeurIPS), 2023.

Flachot, A., Akbarinia, A., Schütt, H. H.,  Fleming, R. W.,  Wichmann, F. A., and Gegenfurtner, K. R. (2022). Deep neural models for color classification and color constancy. Journal of Vision, 22(4):17, 1-24.

Künstle, D.-E., von Luxburg, U., and Wichmann, F. A. (2022). Estimating the perceived dimension of psychophysical stimuli using triplet accuracy and hypothesis testing. Journal of Vision, 22(13), 5, 1-14.

Meding, K., Schulze Buschoff, L. M., Geirhos, R., and Wichmann, F. A. (2022). Trivial or Impossible — dichotomous data difficulty masks model differences (on ImageNet and beyond). International Conference on Learning Representations (ICLR), virtual meeting.

Borowski, J., Zimmermann, R. S., Schepers, J.,  Geirhos, R., Wallis, T. S. A., Bethge, M. and Brendel, W. (2021). Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization. International Conference on Learning Representations (ICLR), 2021.

Geirhos, R., Narayanappa, K., Mitzkus, B., Thieringer, T., Bethge, M., Wichmann, F. A. and Brendel, W. (2021). Partial success in closing the gap between human and machine vision. 35th Conference on Neural Information Processing Systems (NeurIPS), 2021.

Hagendorff, T. and Meding, K. (2021). Ethical considerations and statistical analysis of industry involvement in machine learning research. AI & SOCIETY, 2021.

Huber, L. S., Geirhos, R. and Wichmann, F. A. (2021). Out-of-distribution robustness: Limited image exposure of a four-year-old is enough to outperform ResNet-50. NeurIPS Workshop on Shared Visual Representations in Human & Machine Intelligence, 2021. 

Meding, K., Schulze Buschoff, L. M., Geirhos, R. and Wichmann, F. A. (2021). ImageNet suffers from dichotomous data difficulty. NeurIPS Workshop on ImageNet: past, present, and future, 2021.

Zimmermann, R. S., Borowski, J., Geirhos, R., Bethge, M., Wallis, T. S. A. and Brendel, W. (2021). How Well do Feature Visualizations Support Causal Understanding of CNN Activations? Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI (ICML), 2021.

Zimmermann, R. S., Borowski, J., Geirhos, R., Bethge, M., Wallis, T. S. A. and Brendel, W. (2021). How Well do Feature Visualizations Support Causal Understanding of CNN Activations? 35th Conference on Neural Information Processing Systems (NeurIPS), 2021.

Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R.,  Brendel, W.,  Bethge, M., and Wichmann, F. A. (2020). Shortcut learning in deep neural networks. Nature Machine Intelligence, 2, 665-673.

Geirhos, R., Meding, K. and Wichmann, F. A. (2020). Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.

Geirhos, R., Narayanappa, K., Mitzkus, B., Bethge, M., Wichmann, F. A., and Brendel, W. (2020). On the surprising similarities between supervised and self-supervised models. NeurIPS Workshop on Shared Visual Representations in Human & Machine Intelligence, 2020.

Haghiri, S., Wichmann, F. A., and von Luxburg, U. (2020). Estimation of perceptual scales using ordinal embedding. Journal of Vision, 20(9):14, 1-20.

Hénaff, O. J., Boundy-Singer, Z. M., Meding, K.,  Ziemba, C. M., Robbe L. T. Goris, R. L. T. (2020). Representation of visual uncertainty through neural gain variability. Nature Communications, 11:2513, 1-12.

Joos, D. and Meding,K. (2020). Künstliche Intelligenz und Datenschutz im Human Resource Management. Computer und Recht, 36(12), 834-840.

Meding,K.,  Bruijns, S. A., Schölkopf, B.,  Berens, P. and Wichmann, F. A. (2020). Phenomenal Causality and Sensory Realism. i-Perception, 11(3), 1–16.

Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A. and Brendel, W. (2019).  ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. International Conference on Learning Representations (ICLR), 2019. 

Meding, K., Janzing, D., Schölkopf, B. and Wichmann, F. A. (2019).   Perceiving the arrow of time in autoregressive motion. In Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E.  and Garnett, R., editors, Advances in Neural Information Processing Systems (NeurIPS) 32. 

Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A.S., Bethge, M. and Brendel, W. (2019). Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming. NeurIPS Workshop on Machine Learning for Autonomous Driving.

Özdenizci, O., Meyer, T., Wichmann, F. A., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M. (2019). Neural Signatures of Motor Skill in the Resting Brain. IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 4387-4394 ). [get PDF via e-mail].

Rothkegel, L., Schütt, H. H., Trukenbrod, H. A., Wichmann, F. A. and Engbert, R. (2019). Searchers adjust their eye-movement dynamics to target characteristics in natural scenes.  Scientific Reports, 9:1635.

Schütt, H. H., Rothkegel, L. O. M., Trukenbrod, H. A., Engbert, R. and Wichmann, F. A. (2019). Disentangling bottom-up vs. top-down and low-level vs. high-level influences on eye movements over time. Journal of Vision, 19(3):1, 1-23.

Trukenbrod, H. A., Barthelmé, S., Wichmann, F. A. and Engbert, R. (2019). Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing. Journal of Vision, 19(6):5, 1-19.

Wallis, T. S. A., Funke, C. M., Ecker, A. S., Gatys, L. A., Wichmann, F. A. and Bethge, M. (2019). Image content is more important than Bouma’s Law for scene metamers. eLife, 8:e42512, 1-43.

Geirhos, R., Temme,  C. R. M., Rauber, J., Schütt, H. H., Bethge, M. and Wichmann, F. A. (2018). Generalisation in humans and deep neural networks. In Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R., editors, Advances in Neural Information Processing Systems (NeurIPS) 31, pages 7549–7561.

Wichmann, F. A. and Jäkel, F. (2018). Methods in Psychophysics. Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Fourth Edition, Volume 5. Methodology get PDF via e-mail].

Aguilar, G., Wichmann, F. A. and Maertens, M. (2017). Comparing sensitivity estimates from MLDS and forced-choice methods in a slant-from-texture experiment. Journal of Vision, 17(1):37, 1–18.

Rothkegel, L. O. M., Trukenbrod, H. A., Schütt, H. H., Wichmann, F. A. and Engbert, R. (2017). Temporal evolution of the central fixation bias in scene viewing. Journal of Vision,17(13):3, 1–18.

Schütt, H. H., Rothkegel, L., Trukenbrod, H. A., Reich, S., Wichmann, F. A. and Engbert, R. (2017). Likelihood-Based Parameter Estimation and Comparison of Dynamical Cognitive Models. Psychological Review, 124(4), 505–524. [get PDF via e-mail].

Schütt, H. H. and Wichmann, F. A. (2017). An image-computable psychophysical spatial vision model. Journal of Vision, 17(12):12, 1–35.

Wallis, T. S. A., Funke, C. M., Ecker, A. S., Gatys, L. A., Wichmann, F. A. and Bethge, M. (2017). A parametric texture model based on deep convolutional features closely matches texture appearance for humans. Journal of Vision, 17(12):5, 1–29.

Wallis, T. S. A., Tobias, S., Bethge, M. and Wichmann, F. A. (2017). Detecting distortions of peripherally presented letter stimuli under crowded conditions. Attention, Perception and Psychophysics, doi:10.3758/s13414-016-1245-x.

Wichmann, F. A., Janssen, D. H. J., Geirhos, R., Aguilar, G., Schütt, H. H., Maertens, M., and Bethge, M. (2017).Methods and measurements to compare men against machines. Electronic Imaging, Human Vision and Electronic Imaging 2017, pp. 36-45(10).

Jäkel, F., Singh, M., Wichmann, F. A. and Herzog, M. H. (2016). An Overview of Quantitative Approaches in Gestalt Perception. Vision Research 126, 3-8.

Mohr, J., Seyfarth, J., Lüschow, A., Weber, J. E., Wichmann, F. A., and Obermayer, K. (2016). BOiS - Berlin object in scene database: Controlled photographic images for visual search experiments with quantified contextual priors. Frontiers in Psychology, doi: 10.3389/fpsyg.2016.00749.

Rothkegel, L., Trukenbrod, H., Schütt, H., Wichmann, F. A. and Engbert, R. (2016). Influence of initial fixation position in scene viewing. Vision Research 129, 33-49.

Schütt, H. H., Harmeling, S., Macke, J. H. and Wichmann, F. A. (2016). Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research 122, 105-123.

Schütt, H. H., Baier, F. and Fleming, R. W. (2016). Perception of light source distance from shading patterns. Journal of Vision, 16(3):9, 1–20.

Wallis, T. S. A., Bethge, M. and Wichmann, F. A. (2016). Testing models of peripheral encoding using metamerism in an oddity paradigm. Journal of Vision, 16(2):4, 1–30.

Betz, T., Shapley, R. M., Wichmann, F. A. and Maertens, M. (2015). Noise masking of White’s illusion exposes the weakness of current spatial filtering models of lightness perception. Journal of Vision, 15(14):1, 1-17.

Betz, T., Shapley, R. M., Wichmann, F. A. and Maertens, M. (2015). Testing the role of luminance edges in White’s illusion with contour adaptation. Journal of Vision, 15(11):14, 1-16.

Engbert, R., Trukenbrod, H. A., Barthelmé, S. and Wichmann, F. A. (2015). Spatial statistics and attentional dynamics in scene viewing. Journal of Vision, 15(1):14, 1-17.

Maertens, M., Wichmann, F. A. and Shapley, R. M. (2015). Context affects lightness at the level of surfaces. Journal of Vision, 15(1):15, 1-15.

Fründ, I., Wichmann, F. A. and Macke, J. H. (2014). Quantifying the effect of intertrial dependence on perceptual decisions. Journal of Vision, 14(7):9, 1-16.

Barthelmé, S., Trukenbrod, H. A., Engbert, R., and Wichmann, F. A. (2013). Modeling fixation locations using spatial point processes. Journal of Vision, 13(12):1, 1–34.

Gerhard, H. E., Wichmann, F. A. and Bethge, M. (2013). How sensitive is the human visual system to the local statistics of natural images?. PLoS Computational Biology, 9(1):e1002873. doi:10.1371/journal.pcbi.1002873.

Goris, R. L. T., Putzeys, T., Wagemans, J. and Wichmann, F. A. (2013). A neural population model for visual pattern detection. Psychological Review, 120(3):472–496. [get PDF via e-mail].

Maertens, M. and Wichmann, F. A. (2013). When luminance increment thresholds depend on apparent lightness. Journal of Vision, 13(6):21, 1–11.

Schönfelder, V. H. and Wichmann, F. A. (2013). Identification of stimulus cues in narrow-band tone-in-noise detection using sparse observer models. Journal of the Acoustical Society of America, 134(1):447–463.

Trukenbrod, H. A., Barthelmé, S., Wichmann, F. A. and Engbert, R. (2013). Using spatial statistics to investigate within-trial correlations of human gaze positions. In T. Pfeifer & K. Essig. Proceedings of the First International Workshop on Solutions for Automatic Gaze-Data Analysis 2013 (SAGA 2013). Paper presented at SAGA 2013: Bielefeld, Germany (pp. 19–22). [get PDF via e-mail].

Putzeys, T., Bethge, M., Wichmann, F. A., Wagemans, J. and Goris, R. L. T. (2012). A new perceptual bias reveals suboptimal population decoding of sensory responses. PLoS Computational Biology, 8(4), e1002453. doi:10.1371/journal.pcbi.1002453.

Schönfelder, V. H. and Wichmann, F. A. (2012). Sparse regularized regression identifies behaviorally-relevant stimulus features from psychophysical data. Journal of the Acoustical Society of America, 131(5), 3953-3969.

Fründ, I., Haenel, N. V. and Wichmann, F.A. (2011). Inference for psychometric functions in the presence of nonstationary behavior. Journal of Vision, 11(6:16), 1-19.

Rosas, P. and Wichmann, F. A. (2011). Cue Combination: Beyond Optimality. In J. Trommershäuser, K. Körding and M. S. Landy (Eds.), Sensory Cue Integration (pp. 144-152). Oxford: Oxford University Press. [get PDF via e-mail].

Macke, J. H. and Wichmann, F. A. (2010). Estimating predictive stimulus features from psychophysical data: the decision image technique applied to human faces. Journal of Vision, 10(5:22), 1-24.

Wichmann, F. A., Drewes, J., Rosas, P. and Gegenfurtner, K. R. (2010). Animal detection in natural scenes: Critical features revisited. Journal of Vision, 10(4), 1-27,6.

Goris, R. L. T., Wichmann, F. A. and Henning, G. B. (2009). A neurophysiologically plausible population-code model for human contrast discrimination. Journal of Vision, 9(7:15), 1-22.

Jäkel, F., Schölkopf, B. and Wichmann, F. A. (2009). Does cognitive science need kernels?. Trends in Cognitive Sciences, 13(9), 381-388.

Kienzle, W., Franz, M. O., Schölkopf, B. and Wichmann, F. A. (2009). Center-surround patterns emerge as optimal predictors for human saccade targets. Journal of Vision, 9(5:7), 1-15,7.

Thielscher, A. and Wichmann, F. A. (2009). Determining the cortical target of transcranial magnetic stimulation.  NeuroImage, 47, 1319-1330.

Goris, R. L. T., Wagemans, J. and Wichmann, F. A. (2008). Modelling contrast discrimination data suggests both the pedestal effect and stochastic resonance to be caused by the same mechanism. Journal of Vision, 8(15:17), 1-21.

Jäkel, F., Schölkopf, B. and Wichmann, F. A. (2008). Similarity, kernels and the triangle inequality. Journal of Mathematical Psychology, 52, 297-303. [get PDF via e-mail].

Jäkel, F., Schölkopf, B. and Wichmann, F. A. (2008). Generalization and similarity in exemplar models of categorization: Insights from machine learning. Psychonomic Bulletin and Review, 15(2), 256-271. [get PDF via e-mail].

Bethge, M., Wiecki, T. V. and Wichmann, F. A. (2007). The Independent Components of Natural Images are Perceptually Dependent. Proceedings of the SPIE-ISandT. Human Vision and Electronic Imaging, 6492, A1-A12. [get PDF via e-mail].

Henning, G. B. and Wichmann, F. A. (2007). Some observations on the pedestal effect.  Journal of Vision, 7(1:3), 1-15.

Jäkel, F., Schölkopf, B. and Wichmann, F. A. (2007). A tutorial on kernel methods for categorization. Journal of Mathematical Psychology, 51(6), 343-358. [get PDF via e-mail].

Kienzle, W., Schölkopf, B., Wichmann, F. A. and Franz, M. O. (2007). How to find interesting locations in video: a spatiotemporal interest point detector learned from human eye movements. In F. A. Hamprecht, C. Schnörr and B. Jähne (Eds.), DAGM 2007 (Vol. 4713, pp. 405-414). [get PDF via e-mail].

Kienzle, W., Wichmann, F. A., Schölkopf, B. and Franz, M. O. (2007). A Nonparametric Approach to Bottom-Up Visual Saliency. In B. Schölkopf, J. Platt and T. Hoffman (Eds.), Advances in Neural Information Processing Systems 19 (pp. 689-696). Cambridge, MA: MIT Press. [get PDF via e-mail].

Laub, J., Macke, J. H., Müller, K.-R. and Wichmann, F. A. (2007). Inducing Metric Violations in Human Similarity Judgements. In B. Schölkopf, J. Platt and T. Hoffman (Eds.), (pp. 777–784). Presented at the Advances in Neural Information Processing Systems 19, Cambridge, MA: MIT Press. [get PDF via e-mail].

Rosas, P., Wichmann, F. A. and Wagemans, J. (2007). Texture and object motion in slant discrimination: Failure of reliability-based weighting of cues may be evidence for strong fusion. Journal of Vision, 7(6:3), 1-21.

Graf, A. B. A., Wichmann, F. A., Bülthoff, H. H. and Schölkopf, B. (2006). Classification of faces in man and machine.  Neural Computation, 18, 143-165. [get PDF via e-mail].

Jäkel, F. and Wichmann, F. A. (2006). Spatial four-alternative forced-choice method is the preferred psychophysical method for naive observers. Journal of Vision, 6(11), 1307-1322.

Kienzle, W., Wichmann, F. A., Schölkopf, B. and Franz, M. O. (2006). Learning an interest operator from human eye movements IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). [get PDF via e-mail].

Wichmann, F. A., Braun, D. I. and Gegenfurtner, K. R. (2006). Phase noise and the classification of natural images.  Vision Research, 46, 1520-1529. [get PDF via e-mail].

Kammer, T., Puls, K., Strasburger, H., Hill, N. J. and Wichmann, F. A. (2005). Transcranial magnetic stimulation in the visual system. I. The psychophysics of visual suppression. Experimental Brain Research, 160(1), 118-128. [get PDF via e-mail].

Kuss, M., Jäkel, F. and Wichmann, F. A. (2005). Bayesian inference for psychometric functions. Journal of Vision, 5, 478-492.

Rosas, P., Ernst, M. O., Wagemans, J. and Wichmann, F. A. (2005). Texture and haptic cues in slant discrimination: Reliability-based cue weighting without statistically optimal cue combination. Journal of the Optical Society of America A, 22(5), 801-809. [get PDF via e-mail].

Wagemans, J., Wichmann, F. A. and Op de Beeck, H. (2005). Visual Perception I: Basic Principles In K. Lamberts and R. L. Goldstone (Eds.), Handbook of Cognition (pp. 3-47). London: Sage Publications. [get PDF via e-mail]

Wichmann, F. A., Graf, A. B. A., Simoncelli, E. P., Bülthoff, H. H. and Schölkopf, B. (2005). Machine learning applied to perception: decision-images for gender classification In L. K. Saul, Y. Weiss and L. Bottou (Eds.), Advances in Neural Information Processing Systems 17 (pp. 1489-1496). Cambridge, MA: MIT Press. [get PDF via e-mail]

Graf, A. B. A. and Wichmann, F. A. (2004). Insights from Machine Learning Applied to Human Visual Classification. In S. Thrun, L. Saul and B. Schölkopf (Eds.), Advances in Neural Information Processing Systems 16 (pp. 905-912). Cambridge, MA: MIT Press. [get PDF via e-mail]

Rosas, P., Wichmann, F. A. and Wagemans, J. (2004).
Some observations on the effects of slant and texture type on slant-from-texture. Vision Research, 44(13), 1511-1535.

Bird, C. M., Henning, G. B. and Wichmann, F. A. (2002). Contrast discrimination with sinusoidal gratings of different spatial frequency. Journal of the Optical Society of America A, 19(7), 1267-1273. [get PDF via e-mail].

Graf, A. B. A. and Wichmann, F. A. (2002). Gender Classification of Human Faces. In H H Bülthoff, S.-W. Lee, T. A. Poggio and C. Wallraven (Eds.), Biologically Motivated Computer Vision (Vol. 2525, pp. 491-501). Heidelberg: Springer Verlag. [get PDF via e-mail].

Henning, G. B., Bird, C. M. and Wichmann, F. A. (2002). Contrast discrimination with pulse trains in pink noise. Journal of the Optical Society of America A, 19(7), 1259-1266. [get PDF via e-mail].

Wichmann, F. A., Sharpe, L. T. and Gegenfurtner, K. R. (2002). The contributions of color to recognition memory for natural scenes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(3), 509-520. [get PDF via e-mail].

Wichmann, F. A. and Hill, N. J. (2001). The psychometric function: I. Fitting, sampling and goodness-of-fit. Perception and Psychophysics, 63(8), 1293-1313.

Wichmann, F. A. and Hill, N. J. (2001). The psychometric function: II. Bootstrap-based confidence intervals and sampling. Perception and Psychophysics, 63(8), 1314-1329.

Gegenfurtner, K. R., Wichmann, F. A. and Sharpe, L. T. (1998). The contribution of color to visual memory in x-chromosome-linked dichromats. Vision Research, 38(7), 1041-1045.

Wichmann, F. A. and Henning, G. B. (1998). No role for motion blur in either motion detection or motion based image segmentation. Journal of the Optical Society of America A, 15(2), 297-306. [get PDF via e-mail].