2023
Jeffrey S. Bowers, Gaurav Malhotra, Federico Adolfi, Marin Dujmović, Milton Montero, Valerio Biscione, Rachel Heaton (2023). On the Importance of Severely Testing Deep Learning Models of Cognition. Cognitive Systems Research. doi:10.1016/j.cogsys.2023.101158
Jeffrey Bowers, Gaurav Malhotra, Marin Dujmović, Milton Montero, Chris Tsvetkov, Valerio Biscione, and Ryan Blything (2023). Clarifying the Status of DNNs as Models of Vision. Behavioral and Brain Sciences Response to commentaries. Published by Cambridge University Press, Behavioral and Brain Sciences, 46:e415. https://doi.org/10.1017/S0140525X23002777
Don Yin, Valerio Biscione, Jeffrey S. Bowers (2023). Convolutional Neural Networks Trained to Identify Words Provide a Good Account of Visual Form Priming Effects. Computational Brain & Behavior 6, 457–472. doi:10.1007/s42113-023-00172-7
Valerio Biscione and Jeffrey S. Bowers (2023). Mixed Evidence for Gestalt Grouping in Deep Neural Networks. Computational Brain & Behavior 6, 438–456. doi:10.1007/s42113-023-00169-2
Christian Tsvetkov, Gaurav Malhotra, Benjamin D. Evans, Jeffrey S. Bowers (2023). The Role of Capacity Constraints in Convolutional Neural Networks for Learning Random Versus Natural Data, Neural Networks, 161:515-524. doi:10.1016/j.neunet.2023.01.011
Federico Adolfi, Jeffrey S. Bowers, David Poeppel (2023). Successes and critical failures of neural networks in capturing human-like speech recognition. Neural Network, 162:199-211. doi:10.1016/j.neunet.2023.02.032
Jeffrey S. Bowers, Gaurav Malhotra, Marin Dujmović, Milton Llera Montero, Christian Tsvetkov, Biscione, Guillermo Puebla, Federico Adolfi, John E. Hummel, Rachel F. Heaton, Benjamin D. Evans, Jeffrey Mitchell, Ryan Blything (2023). Deep Problems with Neural Network Models of Human Vision. Published by Cambridge University Press, Behavioral and Brain Sciences, 46:e385. https://doi.org/10.1017/S0140525X22002813
2022
Guillermo PueblaJeffrey S. Bowers (2022). Can deep convolutional neural networks support relational reasoning in the same-different task?Journal of Vision, 22 (10)11, 1-18
doi: https://doi.org/10.1167/jov.22.10.11
Milton Montero, Jeffrey S. Bowers, Rui Ponte Costa, Casimir J. Ludwig, Gaurav Malhotra (2022). Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation. In Advances in Neural Information Processing Systems, 35. Link to open review
Michele Gubian, Ryan Blything, Colin J. Davis, and Jeffrey S. Bowers (2022). Does that sound right? A Novel Method of Evaluating Models of Reading Aloud. Behavior Research Methods.
doi:10.3758/s13428-022-01794-8
Marin Dujmović, Jeffrey S. Bowers, Federico Adolfi, Gaurav Malhotra (2022). The Pitfalls of Measuring Representational Similarity Using Representational Similarity Analysis.
doi:10.1101/2022.04.05.487135
Gaurav Malhotra, Marin Dujmović, Jeffrey S. Bowers (2022). Feature Blindness: A Challenge for Understanding and Modelling Visual Object Recognition. PLoS Computational Biology, doi:10.1371/journal.pcbi.1009572
Valerio Biscione, Jeffrey S. Bowers (2022). Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training. Neural Networks, 150:222-236 doi:10.1016/j.neunet.2022.02.017
Benjamin D. Evans, Gaurav Malhotra, Jeffrey S. Bowers (2022). Biological convolutions improve DNN robustness to noise and generalisation. Neural Networks, 148:96-110. doi:10.1016/j.neunet.2021.12.005
2021
Gaurav Malhotra, Marin Dujmović, , John Hummel, Jeffrey S. Bowers (2021). The Contrasting Shape Representations that Support Object Recognition in Humans and CNNs (preprint)
https://doi.org/10.1101/2021.12.14.472546
Valerio Biscione, Jeffrey S. Bowers (2021). Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be. Journal of Machine Learning Research. 22(229):1−28, 2021.
Guillermo Puebla, Jeffrey S. Bowers (2021). Can deep convolutional neural networks support relational reasoning in the same-different task? Journal of Vision https://doi.10.1167/jov.22.10.11
Jeff Mitchell, Jeffrey S. Bowers (2021). Generalisation in Neural Networks Does not Require Feature Overlap (preprint) https://arxiv.org/abs/2107.06872
Guillermo Puebla, Jeffrey S. Bowers (2021). Can Deep Convolutional Neural Networks Learn Same-Different Relations? (preprint) https://doi.org/10.1101/2021.04.06.438551
Ryan Blything, Valerio Biscione, Ivan I. Vankov, Casimir J.H. Ludwig, Jeffrey S. Bowers (2021). The human visual system and CNNs can both support robust online translation tolerance following extreme displacements. Journal of Vision, Vol.21(2):9, 1-16. https://doi.org/10.1167/jov.21.2.9
Milton Llera Montero, Casimir J. Ludwig, Rui Ponte Costa, Gaurav Malhotra, Jeffrey S. Bowers (2021). The Role of Disentanglement in Generalisation. International Conference on Learning Representations, May 2021. [code]
2020
Ryan Blything, Valerio Biscione, Jeffrey S. Bowers (2020). A case for robust translation tolerance in humans and CNNs. A commentary on Han et al. (preprint) arXiv:2012.05950.
Valerio Biscione and Jeffrey S. Bowers (2020). Learning Translation Invariance in CNNs. 2nd Workshop on Shared Visual Representations in Human and Machine Intelligence (SVRHM), NeurIPS 2020. https://arxiv.org/abs/2011.11757
Jeff Mitchell & Jeffrey S. Bowers (2020). Priorless Recurrent Networks Learn Curiously. In the Proceedings of the 28th International Conference on Computational Linguistics. http://dx.doi.org/10.18653/v1/2020.coling-main.451
MarinDujmović, GauravMalhotra, Jeffrey S.Bowers, (2020). What do Adversarial Images Tell us about Human Vision? eLife, https://doi.org/10.7554/eLife.55978
Christian Tsvetkov, Gaurav Malhotra, Benjamin D. Evans, Jeffrey S. Bowers, (2020) Adding Biological Constraints to Deep Neural Networks Reduces their Capacity to Learn Unstructured Data. In Proceedings of the 42nd Annual Conference of the Cognitive Science Society 2020, Toronto, Canada.
Ella Gale, Nick D. Martin, Ryan Blything, Anh Tung Nguyen, Jeffrey S. Bowers, (2020) Are there any ‘Object Detectors’ in the Hidden Layers of CNNs Trained to Identify Objects or Scenes? Vision Research, 176, 60-71. https://doi.org/10.1016/j.visres.2020.06.007
Jeff Mitchell and Jeffrey S. Bowers, (2020). Harnessing the Symmetry of Convolutions for Systematic Generalisation, In Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK. https://doi.org/10.1109/IJCNN48605.2020.9207183
Gaurav Malhotra, Benjamin Evans and Jeffrey S. Bowers, (2020). Hiding a Plane with a Pixel: Examining Shape-bias in CNNs and the Benefit of Building in Biological Constraints. Vision Research, 174, 57-68. Special Issue on `What do Deep Neural Networks Tell us about Biological Vision’. https://doi.org/10.1016/j.visres.2020.04.013
2019
Ivan Vankov and Jeffrey S. Bowers (2019). Training neural networks to encode symbols enables combinatorial generalization. Philosophical Transactions of the Royal Society. https://doi.org/10.1098/rstb.2019.0309
Marin Dujmović, Gaurav Malhotra and Jeffrey S. Bowers, (2019). Humans Cannot Decipher Adversarial Images: Revisiting Zhou and Firestone. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://doi.org/10.32470/CCN.2019.1298-0
Gaurav Malhotra, Benjamin Evans and Jeff S. Bowers, (2019). Adding Biological Constraints to CNNs Makes Image Classification more Human-Like and Robust. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://doi.org/10.32470/CCN.2019.1212-0
Ryan Blything, Ivan I. Vankov, Casimir J. Ludwig and Jeffrey S. Bowers, (2019). Extreme Translation Tolerance in Humans and Machines. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://doi.org/10.32470/CCN.2019.1091-0
Milton Llera Montero, Gaurav Malhotra, Jeffrey S. Bowers and Rui Ponte Costa, (2019). Subtractive Gating Improves Generalization in Working Memory Tasks. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://doi.org/10.32470/CCN.2019.1352-0
Jeff Mitchell, Nina Kazanina, Conor Houghton and Jeffrey S. Bowers, (2019). Do LSTMs Know about Principle C? In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. https://doi.org/10.32470/CCN.2019.1241-0
Ella M. Gale, Ryan Blything, Nick D. Martin, Jeffrey S. Bowers, and Anh Nguyen, (2019). Selectivity Metrics Provide Misleading Estimates of the Selectivity of Single Units in Neural Networks. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society 2019, Montreal, Canada.
Ryan Blything, Ivan I. Vankov, Casimir J. Ludwig and Jeffrey S. Bowers, (2019). Translation Tolerance in Vision. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society 2019, Montreal, Canada.
Gaurav Malhotra and Jeffrey S. Bowers, (2019). The Contrasting Roles of Shape in Human Vision and Convolutional Neural Networks. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society 2019, Montreal, Canada.
Jeffrey S. Bowers, Nick D. Martin and Ella M. Gale, (2019). Researchers Keep Rejecting Grandmother Cells after Running the Wrong Experiments: The Issue Is How Familiar Stimuli Are Identified. BioEssays, 2019, 41(8), 1800248. https://doi.org/10.1002/bies.201800248
2017
Jeffrey S. Bowers, J.S. (2017). Parallel Distributed Processing Theory in the Age of Deep Networks. Trends in Cognitive Science, 21, 950-961.