Publication
] Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents’ Action PlansAbstract. Open Mind 6, 211 - 231 (2022).
Continuous representations of action efficiency in infancy. CEU Conference on Cognitive Development (BCCCD16) (2016).
Origins of the concepts cause, cost, and goal in prereaching infants. Cognitive Development Society (2019).
liu_etal_lumi_cds2019_final.pdf (22.95 MB)
liu_etal_lumi_cds2019_final.pdf (22.95 MB) Cortex Is Cortex: Ubiquitous Principles Drive Face-Domain Development. Trends in Cognitive Sciences (2018). doi:10.1016/j.tics.2018.10.009 1-s2.0-S1364661318302572-main.pdf (260.4 KB)
1-s2.0-S1364661318302572-main.pdf (260.4 KB) UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS. (2015). CBMM Memo 040_rev1.pdf (1.92 MB)
CBMM Memo 040_rev1.pdf (1.92 MB) A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception. ( arXiv | Cornell University, 2018). at <https://arxiv.org/pdf/1805.10734.pdf> 1805.10734.pdf (9.59 MB)
1805.10734.pdf (9.59 MB) A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Learning (2020). 1805.10734.pdf (9.59 MB)
1805.10734.pdf (9.59 MB) Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017). 1605.08104.pdf (2.9 MB)
1605.08104.pdf (2.9 MB) Unsupervised Learning of Visual Structure using Predictive Generative Networks. International Conference on Learning Representations (ICLR) (2016). at <http://arxiv.org/pdf/1511.06380v2.pdf>
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. (2017). CBMM-Memo-064.pdf (3 MB)
CBMM-Memo-064.pdf (3 MB) A neural network trained for prediction mimics diverse features of biological neurons and perception. Nature Machine Intelligence 2, 210 - 219 (2020).
PredNet - "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning" [code]. (2016).
Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency. (2014). CBMM-Memo-018_opt.pdf (5.02 MB)
CBMM-Memo-018_opt.pdf (5.02 MB) Foveation-based Mechanisms Alleviate Adversarial Examples. (2016). cbmm_memo_044.pdf (11.48 MB)
cbmm_memo_044.pdf (11.48 MB) On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. (2020). CBMM-Memo-111.pdf (9.76 MB)
CBMM-Memo-111.pdf (9.76 MB) Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 (2024).
When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations. Nature Machine Intelligence 4, 146 - 153 (2022).
Decrease in gamma-band activity tracks sequence learning. Frontiers in Systems Neuroscience 8, (2015). fnsys-08-00222.pdf (5.62 MB)
fnsys-08-00222.pdf (5.62 MB) Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex 1–17, (2018).
Quit while you’re ahead: Preschoolers’ persistence and willingness to accept challenges are affected by social comparison. Annual Meeting of the Cognitive Science Society (CogSci) (2015). 15_Cogsci_Magid&Schulz.pdf (513.72 KB)
15_Cogsci_Magid&Schulz.pdf (513.72 KB) Changing minds: Children’s inferences about third party belief revision. Developmental Science e12553 (2017). doi:10.1111/desc.12553 Changing Minds_MagidYanSiegelTenenbaumSchulz_in press.pdf (915.8 KB)
Changing Minds_MagidYanSiegelTenenbaumSchulz_in press.pdf (915.8 KB) Imagination and the generation of new ideas. Cognitive Development 34, 99–110 (2015). Imagination and the generation of new ideas (266.63 KB)
Imagination and the generation of new ideas (266.63 KB) Moral alchemy: How love changes norms. Cognition 167, 135 -150 (2017). Moral Alchemy_Magid&Schulz.pdf (627.46 KB)
Moral Alchemy_Magid&Schulz.pdf (627.46 KB) Dissociating language and thought in large language models. Trends in Cognitive Sciences 28, 517 - 540 (2024).