Journal publications:
- Jozwik, K.M., Kietzmann, T.C., Cichy, RM., Kriegeskorte, N., Mur, M. (2023)
"Deep neural networks and semantic models explain complementary components of human ventral-stream representational dynamics" Journal of Neuroscience
- Jozwik, K.M.*, O'Keeffe*, J., Storrs*, K., Guo, W., Golan, T., Kriegeskorte, N. (2022) (*contributed equally)
"Face dissimilarity judgements are predicted by representational distance in morphable and image-computable models" Proceedings of the National Academy of Sciences 119(27)e2115047119
- Jozwik, K.M., Najarro, E., van den Bosch, JJF., Charest, I., Cichy*, RM., Kriegeskorte*, N. (2022) (*contributed equally)
"Disentangling five dimensions of animacy in human brain and behaviour" Communications Biology 5, 1247
- Jozwik, K.M. (2021)
"What AI can learn from the biological brain" Science 372 (6544), 798-798
- Adhya, D., Swarup, V., Nagy, R., Dutan, L., Shum, C., Valencia-Alarcón, E.P., Jozwik, K.M., Mendez, M.A., Horder, J., Loth, E., Nowosiad, P., Lee, I., Skuse, D., Flinter, F.A., Murphy, D., McAlonan, G., Geschwind, D.H., Price, J., Carroll, J., Srivastava, D.P., Baron-Cohen, S. (2021)
"Atypical neurogenesis in induced pluripotent stem cell (iPSC) from autistic individuals" Biological Psychiatry 89 (5), 486-496
- Cichy, R. M., Kriegeskorte, N., Jozwik, K.M., van den Bosch, J.J.F. , Charest, I. (2019)
"The spatiotemporal neural dynamics underlying perceived similarity for real-world objects" Neuroimage, 194, 12-24
- Jozwik, K.M., Kriegeskorte, N., Storrs, K. R., Mur, M. (2017)
“Deep convolutional neural networks outperform feature-based but not categorical models in explaining object similarity judgments” Frontiers in Psychology 8(10):1726
- Jozwik, K.M., Kriegeskorte N, Mur M (2016)
“Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares” Neuropsychologia 83:201-26
- Jozwik, K.M., Chernukhin I, Serandour AA, Nagarajan S, Carroll JS (2016)
“FOXA1 directs H3K4 monomethylation at enhancers via recruitment of the methyltransferase MLL3” Cell Reports 17(10):2715-2723
- Jozwik, K.M., Carroll JS (2012)
“Pioneer factors in hormone dependent cancers” Nature Reviews Cancer 12(6):381-5
Peer-reviewed conference publications:
- Jozwik, K.M., Jozwik, K.M., Cichy*, RM. and Kriegeskorte*, N. (2025)
"Five animacy dimensions and the CLIP model explain complementary components of visual representational dynamics and similarity judgments" Conference on Cognitive Computational Neuroscience
- Jozwik, K.M., Lee, H., Kanwisher, N. and DiCarlo, J.J. (2023)
”First steps in using topographic deep artificial neural network models to generate hypotheses about not-yet-detected functional neural aggregates in the ventral stream” Conference on Cognitive Computational Neuroscience
- Jozwik, K.M., Lee, H., Kanwisher, N. and DiCarlo, J.J. (2019)
"Are topographic deep convolutional neural networks better models of the ventral visual stream?" Conference on Cognitive Computational Neuroscience
- Jozwik, K.M., Kriegeskorte, N., Cichy, R. M., Mur, M. (2018)
“Deep convolutional neural networks, features, and categories perform similarly at explaining primate high-level visual representations” Conference on Cognitive Computational Neuroscience
- Jozwik, K.M., Charest I., Kriegeskorte, N. Cichy, R. M., (2017)
“Animacy dimensions ratings andapproach for decorrelating stimuli dimensions” Conference on Cognitive Computational Neuroscience
Preprints:
- Lee, H., Margalit, E.,Jozwik, K.M., Cohen, M.A., Kanwisher, N., Yamins, D.L.K, DiCarlo, J.J. (2020)
”Topographic deep artificial neural networks reproduce the hallmarks of the primate inferior temporal cortex face processing network” (performed analyses on wiring cost and neural fits). bioRxiv
- Jozwik, K.M., Schrimpf, M., Kanwisher, N. and DiCarlo, J.J. (2019)
"To find better neural network models of human vision, find better neural network models of primate vision" bioRxiv
- Jozwik, K.M., Lee, M., Marques, T., Schrimpf, M., Bashivan, P. (2019)
"Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge" bioRxiv
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