553 citations · 758 across the 11 of their papers we have counts for
11 papers
Perceived face similarity reflects inverse-generative and naturalistic discriminative objectives
Wenxuan Guo, Heiko H. Schütt, Kamila Maria Jozwik +3
The perceptual representations supporting our ability to recognize faces remain a computational mystery. Deep neural networks offer mechanistic hypotheses for human face perception…
Model-Behavior Alignment under Flexible Evaluation: When the Best-Fitting Model Isn't the Right One
Itamar Avitan, Tal Golan
Linearly transforming stimulus representations of deep neural networks yields high-performing models of behavioral and neural responses to complex stimuli. But does the test accura…
Core Knowledge Deficits in Multi-Modal Language Models
Yijiang Li, Qingying Gao, Tianwei Zhao +8
While Multi-modal Large Language Models (MLLMs) demonstrate impressive abilities over high-level perception and reasoning, their robustness in the wild remains limited, often falli…
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…
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments
Tal Golan, Wenxuan Guo, Heiko H. Schütt +1
Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitativel…
Testing the limits of natural language models for predicting human language judgments
Tal Golan, Matthew Siegelman, Nikolaus Kriegeskorte +1
Neural network language models can serve as computational hypotheses about how humans process language. We compared the model-human consistency of diverse language models using a n…
Controversial stimuli: pitting neural networks against each other as models of human recognition
Tal Golan, Prashant C. Raju, Nikolaus Kriegeskorte
Distinct scientific theories can make similar predictions. To adjudicate between theories, we must design experiments for which the theories make distinct predictions. Here we cons…
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Roman Beliy, Guy Gaziv, Assaf Hoogi +3
Reconstructing observed images from fMRI brain recordings is challenging. Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i.e., images with their correspondin…
Neural network models and deep learning - a primer for biologists
Nikolaus Kriegeskorte, Tal Golan
Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence, where they are used to approximate func…