Publication

Found 904 results
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Spokes, A. C. Infants’ Reasoning about Affiliation and Caregiving. Cognitive Development Society (CDS) Biennial Meeting (2015).
Spokes, A. C. & Spelke, E. S. Early reasoning about affiliation and kinship. (2015).
Spokes, A. C. & Spelke, E. S. Four-year-old children favor kin when the stakes are higher. Cognitive Development Society (CDS) (2017). at <https://cogdevsoc.org/wp-content/uploads/2017/10/CDS2017AbstractBook.pdf>
Spokes, A. C. & Spelke, E. S. Children’s Expectations and Understanding of Kinship as a Social Category. Frontiers in Psychology 7, 1664-1078 (2016).
Spokes, A. C., Howard, R., Mehr, S. A. & Krasnow, M. M. Welfare-tradeoff ratios in children. Human Behavior and Evolution Society (2016).
Spokes, A. C. & Spelke, E. S. At 4.5 but not 5.5 years, children favor kin when the stakes are moderately high. PLOS ONE 13, (2018).
Spokes, A. C., Venkatesan, T. & Spelke, E. S. Five-month-old infants attend to responsive caregivers. Cognitive Development Society (CDS) (2017). at <https://cogdevsoc.org/wp-content/uploads/2017/10/CDS2017AbstractBook.pdf>
Spokes, A. C. & Spelke, E. S. Early Reasoning about Affiliation and Social Networks. International Conference on Infant Studies (ICIS) (2016).
Spokes, A. C. & Spelke, E. S. Early Reasoning about Affiliation and Caregiving. Cognitive Development Society (CDS) (2015).
Spokes, A. C., Howard, R., Mehr, S. A. & Krasnow, M. M. Like Adults, children make consistent welfare tradeoff allocations. Budapest CEU Conference on Cognitive Development (2017).
Spokes, A. C. Infants’ Reasoning about Affiliation and Caregiving. Cognitive Development Society (CDS) | More on Development workshop (2015).
Spokes, A. C. & Spelke, E. S. The cradle of social knowledge: Infants' reasoning about caregiving and affiliation. Cognition 159, 102-116 (2017).
Spokes, A. C. How Infants Reason About Affective States and Social Interactions. International Conference on Infant Studies (ICIS) (2016).
Srinivasan, R. Francesco et al. Forward learning with top-down feedback: empirical and analytical characterization. arXiv (2023). at <https://arxiv.org/abs/2302.05440>
Srivastava, S., Ben-Yosef, G. & Boix, X. Minimal images in deep neural networks: Fragile Object Recognition in Natural Images. International Conference on Learning Representations (ICLR) (2019). at <https://arxiv.org/pdf/1902.03227.pdf>
Stemmann, H. & Freiwald, W. A. Evidence for an attentional priority map in inferotemporal cortex. Proceedings of the National Academy of Sciences 116, 23797 - 23805 (2019).
Stephan, S., Willemsen, P. & Gerstenberg, T. Marbles in inaction: Counterfactual simulation and causation by omission. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).PDF icon Marbles in Inaction Counterfactual Simulation and Causation by Omission, Stephan, Willemsen, Gerstenberg, 2017.pdf (1.46 MB)
Stephenson, C. et al. Untangling in Invariant Speech Recognition. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9583-untangling-in-invariant-speech-recognition.pdf (2.09 MB)
Stern, M., Sompolinsky, H. & Abbott, L. F. Dynamics of random neural networks with bistable units. Phys Rev E Stat Nonlin Soft Matter Phys 90, (2014).
Subirana, B., Bagiati, A. & Sarma, S. On the Forgetting of College Academics: at "Ebbinghaus Speed"?. (2017).PDF icon CBMM Memo 068-On Forgetting - June 18th 2017 v2.pdf (713.7 KB)
Subramaniam, V. et al. Using Multimodal DNNs to Study Vision-Language Integration in the Brain. ICLR 2023 (2023). at <https://openreview.net/pdf?id=OQQ1p0pFP4>
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Tacchetti, A., Isik, L. & Poggio, T. Invariant representations for action recognition in the visual system. Vision Sciences Society 15, (2015).
Tacchetti, A., Isik, L. & Poggio, T. Invariant recognition drives neural representations of action sequences. PLoS Comp. Bio (2017).
Tacchetti, A., Voinea, S. & Evangelopoulos, G. Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets. (2017).PDF icon CBMM-Memo-062.pdf (9.37 MB)
Tacchetti, A., Voinea, S. & Evangelopoulos, G. Trading robust representations for sample complexity through self-supervised visual experience. Advances in Neural Information Processing Systems 31 (Bengio, S. et al.) 9640–9650 (Curran Associates, Inc., 2018). at <http://papers.nips.cc/paper/8170-trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf>PDF icon trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB)PDF icon NeurIPS2018_Poster.pdf (6.12 MB)

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