Export 652 results:
A Causal Relationship Between Face-Patch Activity and Face-Detection Behavior. eLife (2017). doi:https://doi.org/10.7554/eLife.18558.001
Changing minds: Children’s inferences about third party belief revision. Developmental Science e12553 (2017). doi:10.1111/desc.12553
Character-building stories. Advances in Cognitive Systems (2017).
Children understand that agents maximize expected utilities. Journal of Experimental Psychology: General 146, 1574 - 1585 (2017).
Comparing human and monkey neural circuits for processing social scenes. Society for Neuroscience's Annual Meeting - SfN 2017 (2017).
Compositional inductive biases in function learning. Cogn Psychol 99, 44-79 (2017).
Compression of Deep Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1701.04923>
Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems. Psychol Sci 28, 1321-1333 (2017).
The cradle of social knowledge: Infants' reasoning about caregiving and affiliation. Cognition 159, 102-116 (2017).
Critical Cues in Early Physical Reasoning. SRCD (2017).
A Data Science approach to analyzing neural data. Joint Statistical Meetings (2017).
Deciphering neural codes of memory during sleep. Trends in Neurosciences (2017).
A Dedicated Network for Social Interaction Processing in the Primate Brain. Science Vol. 356, pp. 745-749 (2017).
Design of the Artificial: lessons from the biological roots of general intelligence. (2017). at <https://arxiv.org/pdf/1703.02245>
Detecting Semantic Parts on Partially Occluded Objects. British Machine Vision Conference (BMVC) (2017). at <https://bmvc2017.london/proceedings/>
Differences in dynamic and static coding within different subdivision of the prefrontal cortex. Society for Neuroscience's Annual Meeting - SfN 2017 (2017). at <http://www.abstractsonline.com/pp8/#!/4376/presentation/4782>
Differential Processing of Isolated Object and Multi-item Pop-Out Displays in LIP and PFC. Cerebral Cortex (2017). doi:10.1093/cercor/bhx243
Eccentricity Dependent Deep Neural Networks for Modeling Human Vision. Vision Sciences Society (2017).
Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15360>