42 citations · 130 across the 9 of their papers we have counts for
9 papers
Active Visual Semantics: A large-scale MEG and eye-tracking dataset for understanding visual intelligence in action
Philip Sulewski, Carmen Amme, Peter König +2
Here we present the Active Visual Semantics (AVS) dataset, a large-scale collection of magnetoencephalography (MEG) and eye-tracking data recorded while five participants freely ex…
Characterizing Universal Object Representations Across Vision Models
Florian P. Mahner, Johannes Roth, Ka Chun Lam +3
Deep neural networks trained with different architectures, objectives, and datasets have been reported to converge on similar visual representations. However, what remains unknown…
CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience
Marie St-Laurent, Basile Pinsard, Oliver Contier +6
Data-hungry neuro-AI modelling requires ever larger neuroimaging datasets. CNeuroMod-THINGS meets this need by capturing neural representations for a wide set of semantic concepts…
Dimensions underlying the representational alignment of deep neural networks with humans
Florian P. Mahner, Lukas Muttenthaler, Umut Güçlü +1
Determining the similarities and differences between humans and artificial intelligence (AI) is an important goal both in computational cognitive neuroscience and machine learning,…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…
VICE: Variational Interpretable Concept Embeddings
Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure +3
A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Conce…