426 citations · 594 across the 10 of their papers we have counts for
13 papers
Bayesian Comparisons Between Representations
Heiko H. Schütt
Which neural networks are similar is a fundamental question for both machine learning and neuroscience. Here, it is proposed to base comparisons on the predictive distributions of…
Neither hype nor gloom do DNNs justice
Felix A. Wichmann, Simon Kornblith, Robert Geirhos
Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNN…
Fixing the problems of deep neural networks will require better training data and learning algorithms
Drew Linsley, Thomas Serre
Bowers and colleagues argue that DNNs are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those…
Beyond the limitations of any imaginable mechanism: large language models and psycholinguistics
Conor Houghton, Nina Kazanina, Priyanka Sukumaran
Large language models are not detailed models of human linguistic processing. They are, however, extremely successful at their primary task: providing a model for language. For thi…
Unsupervised learning of features and object boundaries from local prediction
Heiko H. Schütt, Wei Ji Ma
A visual system has to learn both which features to extract from images and how to group locations into (proto-)objects. Those two aspects are usually dealt with separately, althou…
Statistical inference on representational geometries
Heiko H. Schütt, Alexander D. Kipnis, Jörn Diedrichsen +1
Neuroscience has recently made much progress, expanding the complexity of both neural-activity measurements and brain-computational models. However, we lack robust methods for conn…
Comparing representational geometries using whitened unbiased-distance-matrix similarity
Jörn Diedrichsen, Eva Berlot, Marieke Mur +3
Representational similarity analysis (RSA) tests models of brain computation by investigating how neural activity patterns reflect experimental conditions. Instead of predicting ac…
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…
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…