Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway
暂无分享,去创建一个
[1] Jeroen Geertzen,et al. Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing , 2015, Cogn. Sci..
[2] T. Rogers,et al. Where do you know what you know? The representation of semantic knowledge in the human brain , 2007, Nature Reviews Neuroscience.
[3] Antonio Torralba,et al. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.
[4] J. Rodd,et al. Distinctiveness and correlation in conceptual structure: behavioral and computational studies. , 2004, Journal of experimental psychology. Learning, memory, and cognition.
[5] Elizabeth Jefferies,et al. Semantic Processing in the Anterior Temporal Lobes: A Meta-analysis of the Functional Neuroimaging Literature , 2010, Journal of Cognitive Neuroscience.
[6] Antonio Torralba,et al. Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition , 2016, ArXiv.
[7] James L. McClelland,et al. Understanding normal and impaired word reading: computational principles in quasi-regular domains. , 1996, Psychological review.
[8] Rainer Goebel,et al. Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[9] Alex Clarke,et al. Dynamic information processing states revealed through neurocognitive models of object semantics , 2014, Language, cognition and neuroscience.
[10] Elizabeth Jefferies,et al. Fractionating the anterior temporal lobe: MVPA reveals differential responses to input and conceptual modality , 2017, NeuroImage.
[11] N. Kriegeskorte,et al. Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .
[12] Billi Randall,et al. The perirhinal cortex and conceptual processing: Effects of feature-based statistics following damage to the anterior temporal lobes , 2015, Neuropsychologia.
[13] J. Rodd,et al. Anteromedial temporal cortex supports fine-grained differentiation among objects. , 2005, Cerebral cortex.
[14] L. Tyler,et al. Contrasting effects of feature-based statistics on the categorisation and basic-level identification of visual objects , 2012, Cognition.
[15] Alfonso Caramazza,et al. The multiple semantics hypothesis: Multiple confusions? , 1990 .
[16] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[17] Joseph P. Levy,et al. The distinctiveness of form and function in category structure: A connectionist model , 1997 .
[18] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[19] A. Ishai,et al. Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.
[20] K. McRae,et al. Shared Features Dominate Semantic Richness Effects for Concrete Concepts. , 2009, Journal of memory and language.
[21] M. Masson. A distributed memory model of semantic priming. , 1995 .
[22] Alex Clarke,et al. The Evolution of Meaning: Spatio-temporal Dynamics of Visual Object Recognition , 2011, Journal of Cognitive Neuroscience.
[23] L. Tyler,et al. Object-Specific Semantic Coding in Human Perirhinal Cortex , 2014, The Journal of Neuroscience.
[24] Uta Noppeney,et al. Temporal lobe lesions and semantic impairment: a comparison of herpes simplex virus encephalitis and semantic dementia. , 2006, Brain : a journal of neurology.
[25] Paul Wright,et al. Objects and Categories: Feature Statistics and Object Processing in the Ventral Stream , 2013, Journal of Cognitive Neuroscience.
[26] Chris McNorgan,et al. An attractor model of lexical conceptual processing: simulating semantic priming , 1999, Cogn. Sci..
[27] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[28] C. Price,et al. Integrating Visual and Tactile Information in the Perirhinal Cortex , 2009, Cerebral cortex.
[29] Li Su,et al. A Toolbox for Representational Similarity Analysis , 2014, PLoS Comput. Biol..
[30] William W. Graves,et al. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. , 2009, Cerebral cortex.
[31] Mark S. Seidenberg,et al. Semantic feature production norms for a large set of living and nonliving things , 2005, Behavior research methods.
[32] L. Tyler,et al. Predicting the Time Course of Individual Objects with MEG , 2014, Cerebral cortex.
[33] Mark S. Seidenberg,et al. On the nature and scope of featural representations of word meaning. , 1997, Journal of experimental psychology. General.
[34] Jeroen Geertzen,et al. The Centre for Speech, Language and the Brain (CSLB) concept property norms , 2013, Behavior research methods.
[35] Timothy T. Rogers,et al. Connecting functional brain imaging and Parallel Distributed Processing , 2015 .
[36] George S. Cree,et al. Distinctive features hold a privileged status in the computation of word meaning: Implications for theories of semantic memory. , 2006, Journal of experimental psychology. Learning, memory, and cognition.
[37] Thomas E. Nichols,et al. Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.
[38] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Ken McRae,et al. Category - Specific semantic deficits , 2008 .
[40] Marcel A. J. van Gerven,et al. Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , 2014, The Journal of Neuroscience.
[41] Billi Randall,et al. From perception to conception: how meaningful objects are processed over time. , 2013, Cerebral cortex.
[42] Matthew A. Lambon Ralph,et al. Neurocognitive insights on conceptual knowledge and its breakdown , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.
[43] D. Mirman,et al. Dynamics of activation of semantically similar concepts during spoken word recognition , 2009, Memory & cognition.
[44] Nikolaus Kriegeskorte,et al. Deep neural networks: a new framework for modelling biological vision and brain information processing , 2015, bioRxiv.
[45] L. Tyler,et al. Representational Similarity Analysis Reveals Commonalities and Differences in the Semantic Processing of Words and Objects , 2013, The Journal of Neuroscience.
[46] L. Tyler,et al. Towards a distributed account of conceptual knowledge , 2001, Trends in Cognitive Sciences.
[47] L. Tyler,et al. Understanding What We See: How We Derive Meaning From Vision , 2015, Trends in Cognitive Sciences.
[48] P. Brockhoff,et al. lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package) , 2014 .
[49] K I Taylor,et al. Conceptual structure: Towards an integrated neurocognitive account , 2011, Language and cognitive processes.
[50] Daniel L. K. Yamins,et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..
[51] Nikolaus Kriegeskorte,et al. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..