2.1k citations · 3.4k across the 13 of their papers we have counts for
16 papers
Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception?
Felix A. Wichmann, Robert Geirhos
Deep neural networks (DNNs) are machine learning algorithms that have revolutionised computer vision due to their remarkable successes in tasks like object classification and segme…
The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks
Lukas S. Huber, Robert Geirhos, Felix A. Wichmann
In laboratory object recognition tasks based on undistorted photographs, both adult humans and Deep Neural Networks (DNNs) perform close to ceiling. Unlike adults', whose object re…
Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)
Kristof Meding, Luca M. Schulze Buschoff, Robert Geirhos +1
"The power of a generalization system follows directly from its biases" (Mitchell 1980). Today, CNNs are incredibly powerful generalisation systems -- but to what degree have we un…
On the surprising similarities between supervised and self-supervised models
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus +3
How do humans learn to acquire a powerful, flexible and robust representation of objects? While much of this process remains unknown, it is clear that humans do not require million…
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
Robert Geirhos, Kristof Meding, Felix A. Wichmann
A central problem in cognitive science and behavioural neuroscience as well as in machine learning and artificial intelligence research is to ascertain whether two or more decision…
Shortcut Learning in Deep Neural Networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis +4
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today's machine intelligence. Numerous success stories have rapidly spread all over…
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis +3
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more…
Generalisation in humans and deep neural networks
Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber +3
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using thr…
Disentangling top-down vs. bottom-up and low-level vs. high-level influences on eye movements over time
Heiko H. Schütt, Lars O. M. Rothkegel, Hans A. Trukenbrod +2
Bottom-up and top-down, as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how…
Searchers adjust their eye movement dynamics to the target characteristics in natural scenes
Lars Oliver Martin Rothkegel, Heiko Herbert Schütt, Hans Arne Trukenbrod +2
When searching a target in a natural scene, both the target's visual properties and similarity to the background influence whether (and how fast) humans are able to find it. Howeve…
Comparing deep neural networks against humans: object recognition when the signal gets weaker
Robert Geirhos, David H. J. Janssen, Heiko H. Schütt +3
Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual…
The temporal evolution of the central fixation bias in scene viewing
Lars Oliver Martin Rothkegel, Hans Arne Trukenbrod, Heiko Herbert Schütt +2
When watching the image of a natural scene on a computer screen, observers initially move their eyes towards the center of the image --- a reliable experimental finding termed cent…
Influence of initial fixation position in scene viewing
Lars O. M. Rothkegel, Hans A. Trukenbrod, Heiko H. Schütt +2
During scene perception our eyes generate complex sequences of fixations. Predictors of fixation locations are bottom-up factors like luminance contrast, top-down factors like view…
Likelihood-based Parameter Estimation and Comparison of Dynamical Cognitive Models
Heiko H. Schütt, Lars Rothkegel, Hans A. Trukenbrod +3
Dynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis an…
Spatial statistics and attentional dynamics in scene viewing
Ralf Engbert, Hans A. Trukenbrod, Simon Barthelmé +1
In humans and in foveated animals visual acuity is highly concentrated at the center of gaze, so that choosing where to look next is an important example of online, rapid decision…
Modelling fixation locations using spatial point processes
Simon Barthelmé, Hans Trukenbrod, Ralf Engbert +1
Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate, and why they fixated where they fixated. To a first approximation, a set o…