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This list is the updated (but surely not complete!) literature collection by Anna Wolff and Martin Hebart (ViCCo Group).
| Title | Authors | Year | Journal | Link | Keywords |
|---|---|---|---|---|---|
| Sharpening of Hierarchical Visual Feature Representations of Blurred Images | Abdelhack, Mohamed; Kamitani, Yukiyasu | 2018 | eNeuro | 10.1523/ENEURO.0443-17.2018 | brain_imaging; fMRI; visual |
| Conflicting Bottom-up and Top-down Signals during Misrecognition of Visual Objects | Abdelhack, Mohamed; Kamitani, Yukiyasu | 2019 | BioRxiv | 10.1101/521252 | brain_imaging; fMRI; human; visual |
| Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes | Antolík, Ján; Hofer, Sonja B.; Bednar, James A.; Mrsic-Flogel, Thomas D. | 2016 | PLoS computational biology | 10.1371/journal.pcbi.1004927 | brain_imaging; electrophysiology; rodent; visual |
| What deep learning can tell us about higher cognitive functions like mindreading? | Aru, Jaan; Vicente, Raul | 2018 | arXiv | 1803.10470v2 | review |
| Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision | Avberšek, Lev Kiar; Zeman, Astrid A.; Op de Beeck, Hans | 2021 | BioRxiv | 10.1101/2021.01.24.427905 | brain_imaging; human; visual |
| Deep convolutional networks do not classify based on global object shape | Baker, Nicholas; Lu, Hongjing; Erlikhman, Gennady; Kellman, Philip J. | 2018 | PLoS computational biology | 10.1371/journal.pcbi.1006613 | behavior; human; visual |
| The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning | Bakhtiari, Shahab; Mineault, Patrick; Lillicrap, Tim; Pack, Christopher C.; Richards, Blake A. | 2021 | BioRxiv | 10.1101/2021.06.18.448989v2 | brain_imaging; rodent; visual; electrophysiology |
| Vector-based navigation using grid-like representations in artificial agents | Banino, Andrea; Barry, Caswell; Uria, Benigno; Blundell, Charles; Lillicrap, Timothy P.; Mirowski, Piotr; Pritzel, Alexander; Chadwick, Martin J.; Degris, Thomas; Modayil, Joseph; Wayne, Greg; Soyer, Hubert; Viola, Fabio; Zhang, Brian; Goroshin, Ross; Rabinowitz, Neil; Pascanu, Razvan; Beattie, Charlie; Petersen, Stig; Sadik, Amir; Gaffney, Stephen; King, Helen; Kavukcuoglu, Koray; Hassabis, Demis; Hadsell, Raia; Kumaran, Dharshan | 2018 | Nature | 10.1038/s41586-018-0102-6 | brain_imaging; electrophysiology; rodent; visual |
| The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks | Bankson, B. B.; Hebart, Martin N.; Groen, Iris I. A.; Baker, Chris I. | 2018 | NeuroImage | 10.1016/j.neuroimage.2018.05.037 | brain_imaging; MEG; semantic; visual |
| Analyzing biological and artificial neural networks: challenges with opportunities for synergy? | Barrett, David G. T.; Morcos, Ari S.; Macke, Jakob H. | 2018 | Current Opinion in Neurobiology | 1810.13373v1 | review |
| Neural Population Control via Deep ANN Image Synthesis | Bashivan, Pouya; Kar, Kohitij; DiCarlo, James J. | 2018 | Cognitive Computational Neuroscience | brain_imaging; electrophysiology; monkey; visual | |
| Neural population control via deep image synthesis | Bashivan, Pouya; Kar, Kohitij; DiCarlo, James J. | 2019 | Science | 10.1126/science.aav9436 | brain_imaging; electrophysiology; monkey; visual |
| Modeling Human Categorization of Natural Images Using Deep Feature Representations | Battleday, Ruairidh M.; Peterson, Joshua C.; Griffiths, Thomas L. | 2017 | arXiv | 1711.04855v1 | behavior; human; visual |
| Capturing human categorization of natural images by combining deep networks and cognitive models | Battleday, Ruairidh M.; Peterson, Joshua C.; Griffiths, Thomas L. | 2020 | Nature Communications | 10.1038/s41467-020-18946-z | behavior; human; visual |
| From convolutional neural networks to models of higher-level cognition (and back again) | Battleday, Ruairidh M.; Peterson, Joshua C.; Griffiths, Thomas L. | 2021 | Annals of the New York Academy of Sciences | 10.1111/nyas.14593 | human; review; visual |
| Minimal videos: Trade-off between spatial and temporal information in human and machine vision | Ben-Yosef, Guy; Kreiman, Gabriel; Ullman, Shimon | 2020 | Cognition | 10.1016/j.cognition.2020.104263 | behavior; human; visual |
| Computational mechanisms underlying cortical responses to the affordance properties of visual scenes | Bonner, Michael F.; Epstein, Russell A. | 2018 | PLoS computational biology | 10.1371/journal.pcbi.1006111 | brain_imaging; fMRI; visual |
| Reinforcement Learning, Fast and Slow | Botvinick, Matthew M.; Ritter, Sam; Wang, Jane X.; Kurth-Nelson, Zeb; Blundell, Charles; Hassabis, Demis | 2019 | Trends in Cognitive Sciences | 10.1016/j.tics.2019.02.006 | learning; review |
| Object-scene conceptual regularities reveal fundamental differences between 3 biological and artificial object vision | Bracci, Stefania; Mraz, Jakob; Zeman, Astrid A.; Leys, Gaëlle; Op de Beeck, Hans | 2021 | BioRxiv | 10.1101/2021.08.13.456197 | brain_imaging; fMRI; human; visual |
| The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks | Bracci, Stefania; Ritchie, J. Brendan; Kalfas, Ioannis; Op de Beeck, Hans | 2019 | Journal of Neuroscience | 10.1523/JNEUROSCI.1714-18.2019 | brain_imaging; fMRI; human; visual |
| Learning divisive normalization in primary visual cortex | Burg, Max F.; Cadena, Santiago A.; Denfield, George H.; Walker, Edgar Y.; Tolias, Andreas S.; Bethge, Matthias; Ecker, Alexander S. | 2021 | PLoS computational biology | 10.1371/journal.pcbi.1009028 | brain_imaging; electrophysiology; monkey; visual |
| Deep convolutional models improve predictions of macaque V1 responses to natural images | Cadena, Santiago A.; Denfield, George H.; Walker, Edgar Y.; Gatys, Leon A.; Tolias, Andreas S.; Bethge, Matthias; Ecker, Alexander S. | 2019 | PLoS computational biology | 10.1371/journal.pcbi.1006897 | brain_imaging; electrophysiology; monkey; visual |
| How well do deep neural networks trained on object recognition characterize the mouse visual system? | Cadena, Santiago A.; Sinz, Fabian H.; Muhammad, Taliah; Froudarakis, Emmanouil; Cobos, Erick; Walker, Edgar Y.; Reimer, Jake; Bethge, Matthias; Tolias, Andreas S.; Ecker, Alexander S. | 2019 | NeurIPS workshop Neuro-AI | brain_imaging; electrophysiology; rodent; visual | |
| Deep neural networks rival the representation of primate IT cortex for core visual object recognition | Cadieu, Charles F.; Hong, Ha; Yamins, Daniel L. K.; Pinto, Nicolas; Ardila, Diego; Solomon, Ethan A.; Majaj, Najib J.; DiCarlo, James J. | 2014 | PLoS computational biology | 10.1371/journal.pcbi.1003963 | brain_imaging; electrophysiology; monkey; visual |
| BOLD5000, a public fMRI dataset while viewing 5000 visual images | Chang, Nadine; Pyles, John A.; Marcus, Austin; Gupta, Abhinav; Tarr, Michael J.; Aminoff, Elissa M. | 2019 | Scientific Data | 10.1038/s41597-019-0052-3 | brain_imaging; fMRI; human; semantic; visual |
| The Roles of Statistics in Human Neuroscience | Chén, Oliver Y. | 2019 | Brain Sciences | 10.3390/brainsci9080194 | review |
| DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains | Chen, Xiayu; Zhou, Ming; Gong, Zhengxin; Xu, Wei; Liu, Xingyu; Huang, Taicheng; Zhen, Zonglei; Liu, Jia | 2020 | Frontiers in Computational Neuroscience | 10.3389/fncom.2020.580632 | brain_imaging; fMRI; human; visual |
| Models of primate ventral stream that categorize and visualize images | Christensen, Elijah D.; Zylberberg, Joel | 2020 | BioRxiv | 10.1101/2020.02.21.958488 | brain_imaging; electrophysiology; monkey; visual |
| Deep Neural Networks as Scientific Models | Cichy, Radoslaw M.; Kaiser, Daniel | 2019 | Trends in Cognitive Sciences | 10.1016/j.tics.2019.01.009 | review |
| Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence | Cichy, Radoslaw M.; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude | 2016 | Scientific Reports | 10.1038/srep27755 | brain_imaging; fMRI; human; MEG; visual |
| Neural dynamics of real-world object vision that guide behaviour | Cichy, Radoslaw M.; Kriegeskorte, Nikolaus; Jozwik, Kamila M.; van den Bosch, Jasper J. F.; Charest, Ian | 2017 | BioRxiv | 10.1101/147298 | brain_imaging; fMRI; human; MEG; semantic; visual |
| Human perception in computer vision | Dekel, Ron | 2017 | arXiv | 1701.04674v1 | behavior; human; visual |
| Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway | Devereux, Barry J.; Clarke, Alex; Tyler, Lorraine K. | 2018 | Scientific Reports | 10.1038/s41598-018-28865-1 | brain_imaging; fMRI; semantic; visual |
| Disentangled behavioral representations | Dezfouli, A., Ashtiani, H., Ghattas, O., Nock, R., Dayan, P., & Ong, C. S. | 2019 | BioRxiv | 10.1101/658252 | behavior; human; visual |
| Models that learn how humans learn: The case of decision-making and its disorders | Dezfouli, Amir; Griffiths, Kristi; Ramos, Fabio; Dayan, Peter; Balleine, Bernard W. | 2019 | PLoS computational biology | 10.1371/journal.pcbi.1006903 | behavior; human; visual |
| Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models | Dezfouli, Amir; Morris, Richard; Ramos, Fabio; Dayan, Peter; Balleine, Bernard W. | 2018 | BioRxiv | 10.1101/328849 | behavior; brain_imaging; fMRI; human; visual |
| Human and DNN Classification Performance on Images With Quality Distortions: A Comparative Study | Dodge, Samuel; Karam, Lina | 2019 | Association for Computing Machinery | 10.1145/3306241 | behavior; human; visual |
| Crowding reveals fundamental differences in local vs. global processing in humans and machines | Doerig, Adrien; Bornet, A.; Choung, O. H.; Herzog, Michael H. | 2020 | Vision Research | 10.1016/j.visres.2019.12.006 | behavior; human; visual |
| Capsule Networks as Recurrent Models ofGrouping and Segmentation | Doerig, Adrien; Schmittwilken, Lynn; Sayim, Bilge; Manassi, Mauro; Herzog, Michael H. | 2020 | BioRxiv | 10.1101/747394 | behavior; human; visual |
| What do adversarial images tell us about human vision? | Dujmović, Marin; Malhotra, Gaurav; Bowers, Jeffrey S. | 2020 | eLife | 10.7554/eLife.55978 | behavior; human; visual |
| Unveiling functions of the visual cortex using task-specific deep neural networks | Dwivedi, Kshitij; Bonner, Michael F.; Cichy, Radoslaw M.; Roig, Gemma | 2021 | BioRxiv | 10.1101/2020.11.27.401380 | brain_imaging; fMRI; human; semantic; visual |
| Unraveling Representations in Scene-selective Brain Regions Using Scene-Parsing Deep Neural Networks | Dwivedi, Kshitij; Cichy, Radoslaw M.; Roig, Gemma | 2020 | Journal of Cognitive Neuroscience | 10.1162/jocn\_a\_01624 | brain_imaging; fMRI; human; visual |
| Task-specific vision models explain task-specific areas of visual cortex | Dwivedi, Kshitij; Roig, Gemma | 2018 | BioRxiv | 10.1101/402735 | brain_imaging; visual |
| How Deep is the Feature Analysis underlying Rapid Visual Categorization? | Eberhardt, Sven; Cader, Jonah; Serre, Thomas | 2016 | Advances in Neural Information Processing Systems | 1606.01167v1 | behavior; human; visual |
| A rotation-equivariant convolutional neural network model of primary visual cortex | Ecker, Alexander S.; Sinz, Fabian H.; Froudarakis, Emmanouil; Fahey, Paul G.; Cadena, Santiago A.; Walker, Edgar Y.; Cobos, Erick; Reimer, Jacob; Tolias, Andreas S.; Bethge, Matthias | 2018 | arXiv | 1809.10504v1 | brain_imaging; electrophysiology; rodent; visual |
| Seeing it all: Convolutional network layers map the function of the human visual system | Eickenberg, Michael; Gramfort, Alexandre; Varoquaux, Gaël; Thirion, Bertrand | 2017 | NeuroImage | 10.1016/j.neuroimage.2016.10.001 | brain_imaging; fMRI; human; visual |
| Adversarial Examples that Fool both Computer Vision and Time-Limited Humans | Elsayed, Gamaleldin F.; Shankar, Shreya; Cheung, Brian; Papernot, Nicolas; Kurakin, Alex; Goodfellow, Ian; Sohl-Dickstein, Jascha | 2018 | arXiv | 1802.08195v3 | behavior; human; visual |
| Relating Visual Production and Recognition of Objects in Human Visual Cortex | Fan, Judith E.; Wammes, Jeffrey D.; Gunn, Jordan B.; Yamins, Daniel L. K.; Norman, Kenneth A.; Turk-Browne, Nicholas B. | 2020 | Journal of Neuroscience | 10.1523/JNEUROSCI.1843-19.2019 | brain_imaging; fMRI; human; visual |
| Common Object Representations for Visual Production and Recognition | Fan, Judith E.; Yamins, Daniel L. K.; Turk-Browne, Nicholas B. | 2018 | Cognitive Science | 10.1111/cogs.12676 | behavior; human; visual |
| A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans | Farzmahdi, Amirhossein; Rajaei, Karim; Ghodrati, Masoud; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi | 2016 | Scientific Reports | 10.1038/srep25025 | behavior; human; monkey; visual |
| Training neural networks to mimic the brain improves object recognition performance | Federer, Callie; Xu, Haoyan; Fyshe, Alona; Zylberberg, Joel | 2020 | arXiv | 1905.10679v2 | brain_imaging; electrophysiology; human; monkey; visual |
| Comparing continual task learning in minds and machines | Flesch, Timo; Balaguer, Jan; Dekker, Ronald; Nili, Hamed; Summerfield, Christopher | 2018 | Proceedings of the National Academy of Sciences | 10.1073/pnas.1800755115 | human; learning; visual |
| Using human brain activity to guide machine learning | Fong, Ruth C.; Scheirer, Walter J.; Cox, David D. | 2018 | Scientific Reports | 10.1038/s41598-018-23618-6 | brain_imaging; fMRI; human; visual |
| Constrained sampling from deep generative image models reveals mechanisms of human target detection | Fruend, Ingo | 2020 | Journal of Vision | 10.1167/jov.20.7.32 | behavior; human; visual |
| Human sensitivity to perturbations constrained by a model of the natural image manifold | Fruend, Ingo; Stalker, Elee | 2018 | Journal of Vision | 10.1167/18.11.20 | behavior; human; visual |
| Five points to check when comparing visual perception in humans and machines | Funke, Christina M.; Borowski, Judy; Stosio, Karolina; Brendel, Wieland; Wallis, Thomas S. A.; Bethge, Matthias | 2021 | Journal of Vision | 10.1167/jov.21.3.16 | behavior; human; visual |
| Do Primates and Deep Artificial Neural Networks Perform Object Categorization in a Similar Manner? | Gangopadhyay, Prabaha; Das, Jhilik | 2019 | Journal of Neuroscience | 10.1523/JNEUROSCI.2458-18.2018 | behavior; electrophysiology; fMRI; human; monkey; visual |
| Visual Object Recognition: Do We (Finally) Know More Now Than We Did? | Gauthier, Isabel; Tarr, Michael J. | 2016 | Annual Review of Vision Science | 10.1146/annurev-vision-111815-114621 | review; visual |
| Comparing deep neural networks against humans: object recognition when the signal gets weaker | Geirhos, Robert; Janssen, David H. J.; Schütt, Heiko H.; Rauber, Jonas; Bethge, Matthias; Wichmann, Felix A. | 2017 | arXiv | 1706.06969v2 | behavior; human; visual |
| Generalisation in humans and deep neural networks | Geirhos, Robert; Medina Temme, Carlos R.; Rauber, Jonas; Schütt, Heiko H.; Bethge, Matthias; Wichmann, Felix A. | 2018 | arXiv | 1808.08750v3 | behavior; human; visual |
| Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency | Geirhos, Robert; Meding, Kristof; Wichmann, Felix A. | 2020 | arXiv | 2006.16736v3 | behavior; human; visual |
| Partial success in closing the gap between human and machine vision | Geirhos, Robert; Narayanappa, Kantharaju; Mitzkus, Benjamin; Thieringer, Tizian; Bethge, Matthias; Wichmann, Felix A.; Brendel, Wieland | 2021 | arXiv | 2106.07411v1 | brain_imaging; human; visual |
| ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness | Geirhos, Robert; Rubisch, Patricia; Michaelis, Claudio; Bethge, Matthias; Wichmann, Felix A.; Brendel, Wieland | 2018 | arXiv | 1811.12231v2 | behavior; human; visual |
| Feedforward object-vision models only tolerate small image variations compared to human | Ghodrati, Masoud; Farzmahdi, Amirhossein; Rajaei, Karim; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi | 2014 | Frontiers in Computational Neuroscience | 10.3389/fncom.2014.00074 | brain_imaging; electrophysiology; monkey; visual |
| The Roles of Supervised Machine Learning in Systems Neuroscience | Glaser, Joshua I.; Benjamin, Ari S.; Farhoodi, Roozbeh; Kording, Konrad P. | 2018 | arXiv | 1805.08239v2 | review |
| Controversial stimuli: Pitting neural networks against each other as models of human cognition | Golan, Tal; Raju, Prashant C.; Kriegeskorte, Nikolaus | 2020 | Proceedings of the National Academy of Sciences of the United States of America | 10.1073/pnas.1912334117 | behavior; human; review; visual |
| Visual scenes are categorized by function | Greene, Michelle R.; Baldassano, Christopher; Esteva, Andre; Beck, Diane M.; Fei-Fei, Li | 2016 | Journal of Experimental Psychology | 10.1037/xge0000129 | behavior; human; visual |
| Shared spatiotemporal category representations in biological and artificial deep neural networks | Greene, Michelle R.; Hansen, Bruce C. | 2018 | PLoS computational biology | 10.1371/journal.pcbi.1006327 | brain_imaging; EEG; human; visual |
| Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior | Groen, Iris I. A.; Greene, Michelle R.; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M.; Baker, Chris I. | 2018 | eLife | 10.7554/eLife.32962 | brain_imaging; fMRI; human; visual |
| Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks | Grossman, Shany; Gaziv, Guy; Yeagle, Erin M.; Harel, Michal; Mégevand, Pierre; Groppe, David M.; Khuvis, Simon; Herrero, Jose L.; Irani, Michal; Mehta, Ashesh D.; Malach, Rafael | 2019 | Nature Communications | 10.1038/s41467-019-12623-6 | behavior; human; visual |
| Perceptual Dominance in Brief Presentations of Mixed Images: Human Perception vs. Deep Neural Networks | Gruber, Liron Z.; Haruvi, Aia; Basri, Ronen; Irani, Michal | 2018 | Frontiers in Computational Neuroscience | 10.3389/fncom.2018.00057 | behavior; human; visual |
| Brains on Beats | Güçlü, Umut; Thielen, Jordy; Hanke, Michael; van Gerven, Marcel A. J. | 2016 | Advances in Neural Information Processing Systems | brain_imaging; fMRI; human; visual | |
| Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream | Güçlü, Umut; van Gerven, Marcel A. J. | 2015 | Journal of Neuroscience | 10.1523/JNEUROSCI.5023-14.2015 | brain_imaging; fMRI; human; visual |
| Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks | Güçlü, Umut; van Gerven, Marcel A. J. | 2017 | Frontiers in Computational Neuroscience | 10.3389/fncom.2017.00007 | brain_imaging; fMRI; human |
| Increasingly complex representations of natural movies across the dorsal stream are shared between subjects | Güçlü, Umut; van Gerven, Marcel A. J. | 2017 | NeuroImage | 10.1016/j.neuroimage.2015.12.036 | brain_imaging; fMRI; human; visual |
| Reconstructing perceived faces from brain activations with deep adversarial neural decoding | Güçlütürk, Yagmur; Güçlü, Umut; Seeliger, Katja; Bosch, Sander; van Lier, Rob; van Gerven, Marcel A. J. | 2017 | Advances in Neural Information Processing Systems 30 (NIPS 2017) Pre-Proceedings | brain_imaging; fMRI; human; visual | |
| Levels of Representation in a Deep Learning Model of Categorization | Guest, Olivia; Love, Bradley C. | 2019 | BioRxiv | 10.1101/626374 | behavior; human; visual |
| Performance-optimized hierarchical models only partially predict neural responses during perceptual decision making | Gwilliams, Laura; King, Jean-Rémi | 2017 | BioRxiv | 10.1101/221630 | brain_imaging; human; MEG; visual |
| Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex | Han, Kuan; Wen, Haiguang; Shi, Junxing; Lu, Kun-Han; Zhang, Yizhen; Fu, Di; Liu, Zhongming | 2019 | NeuroImage | 10.1016/j.neuroimage.2019.05.039 | brain_imaging; fMRI; human; visual |
| Neuroscience-Inspired Artificial Intelligence | Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher; Botvinick, Matthew M. | 2017 | Neuron | 10.1016/j.neuron.2017.06.011 | review |
| Explicit information for category-orthogonal object properties increases along the ventral stream | Hong, Ha; Yamins, Daniel L. K.; Majaj, Najib J.; DiCarlo, James J. | 2016 | Nature Neuroscience | 10.1038/nn.4247 | brain_imaging; electrophysiology; monkey; visual |
| Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features | Horikawa, Tomoyasu; Kamitani, Yukiyasu | 2017 | Frontiers in Computational Neuroscience | 10.3389/fncom.2017.00004 | brain_imaging; fMRI; human |
| Generic decoding of seen and imagined objects using hierarchical visual features | Horikawa, Tomoyasu; Kamitani, Yukiyasu | 2017 | Nature Communications | 10.1038/ncomms15037 | brain_imaging; fMRI; human; visual |
| The visual and semantic features that predict object memory: Concept property norms for 1,000 object images | Hovhannisyan, Mariam; Clarke, Alex; Geib, Benjamin R.; Cicchinelli, Rosalie; Monge, Zachary; Worth, Tory; Szymanski, Amanda; Cabeza, Roberto; Davis, Simon W. | 2021 | Memory & Cognition | 10.3758/s13421-020-01130-5 | behavior; human; semantic; visual |
| Comparing the Visual Representations and Performance of Humans and Deep Neural Networks | Jacobs, Robert A.; Bates, Christopher J. | 2019 | Current Directions in Psychological Science | 10.1177/0963721418801342 | human; learning; review; visual |
| Noise-robust recognition of objects by humans and deep neural networks | Jang, Hojin; McCormack, Devin; Tong, Frank | 2021 | BioRxiv | 10.1101/2020.08.03.234625 | behavior; fMRI; human; visual |
| Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing | Jang, Hojin; Tong, Frank | 2021 | BioRxiv | 10.1101/2021.05.25.444835 | behavior; human; visual |
| General object-based features account for letter perception better than specialized letter features | Janini, Daniel; Hamblin, Chris; Deza, Arturo; Konkle, Talia | 2021 | BioRxiv | 10.1101/2021.04.21.440772 | behavior; human; semantic; visual |
| Relating Simple Sentence Representations in Deep Neural Networks and the Brain | Jat, Sharmistha; Tang, Hao; Talukdar, Partha; Mitchell, Tom | 2019 | Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics | 10.18653/v1/P19-1507 | brain_imaging; human; MEG; semantic |
| Extracting low-dimensional psychological representations from convolutional neural networks | Jha, Aditi; Peterson, Joshua C.; Griffiths, Thomas L. | 2020 | arXiv | 2005.14363v1 | behavior; human; review; visual |
| Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments | Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Storrs, Katherine R.; Mur, Marieke | 2017 | Frontiers in Psychology | 10.3389/fpsyg.2017.01726 | behavior; human; visual |
| Face dissimilarity judgements are predicted by representational distance in deep neural networks and principal-component face space | Jozwik, Kamila M.; O’Keeffe, Jonathan; Storrs, Katherine R.; Kriegeskorte, Nikolaus | 2021 | BioRxiv | 10.1101/2021.04.09.438859 | behavior; human; visual |
| To find better neural network models of human vision, find better neural network models of primate vision | Jozwik, Kamila M.; Schrimpf, Martin; Kanwisher, Nancy; DiCarlo, James J. | 2019 | BioRxiv | 10.1101/688390 | brain_imaging; electrophysiology; human; monkey; visual |
| Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons | Kalfas, Ioannis; Kumar, Satwant; Vogels, Rufin | 2017 | eNeuro | 10.1523/ENEURO.0113-17.2017 | brain_imaging; electrophysiology; monkey; visual |
| Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior | Kar, Kohitij; Kubilius, Jonas; Schmidt, Kailyn; Issa, Elias B.; DiCarlo, James J. | 2019 | Nature Neuroscience | 10.1038/s41593-019-0392-5 | brain_imaging; electrophysiology; monkey; visual |
| Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition | Karimi-Rouzbahani, Hamid; Bagheri, Nasour; Ebrahimpour, Reza | 2017 | Neuroscience | 10.1016/j.neuroscience.2017.02.050 | brain_imaging; EEG; human; visual |
| Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models | Karimi-Rouzbahani, Hamid; Bagheri, Nasour; Ebrahimpour, Reza | 2017 | Scientific Reports | 10.1038/s41598-017-13756-8 | behavior; human; visual |
| How do targets, nontargets, and scene context influence real-world object detection? | Katti, Harish; Peelen, Marius V.; Arun, Sripati P. | 2017 | Attention, Perception & Psychophysics | 10.3758/s13414-017-1359-9 | behavior; human; visual |
| Machine vision benefits from human contextual expectations | Katti, Harish; Peelen, Marius V.; Arun, Sripati P. | 2019 | Scientific Reports | 10.1038/s41598-018-38427-0 | behavior; brain_imaging; human; visual |
| Principles for models of neural information processing | Kay, Kendrick N. | 2018 | NeuroImage | 10.1016/j.neuroimage.2017.08.016 | review |
| Deep neural network models of sensory systems: windows onto the role of task constraints | Kell, Alexander J. E.; McDermott, Josh H. | 2019 | Current Opinion in Neurobiology | 10.1016/j.conb.2019.02.003 | brain_imaging; fMRI; human; review |
| A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy | Kell, Alexander J. E.; Yamins, Daniel L. K.; Shook, Erica N.; Norman-Haignere, Sam V.; McDermott, Josh H. | 2018 | Neuron | 10.1016/j.neuron.2018.03.044 | brain_imaging; fMRI; human |
| Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models | Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick N.; Kriegeskorte, Nikolaus | 2017 | Journal of Mathematical Psychology | 10.1016/j.jmp.2016.10.007 | backpropagation; brain_imaging; fMRI; human |
| Deep supervised, but not unsupervised, models may explain IT cortical representation | Khaligh-Razavi, Seyed-Mahdi; Kriegeskorte, Nikolaus | 2014 | PLoS computational biology | 10.1371/journal.pcbi.1003915 | brain_imaging; electrophysiology; monkey; visual |
| Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder | Kheradpisheh, Saeed R.; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée | 2016 | Frontiers in Computational Neuroscience | 10.3389/fncom.2016.00092 | behavior; human; visual |
| Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition | Kheradpisheh, Saeed R.; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée | 2016 | Scientific Reports | 10.1038/srep32672 | behavior; human; visual |
| Deep neural networks in computational neuroscience | Kietzmann, Tim C.; McClure, Patrick; Kriegeskorte, Nikolaus | 2019 | Oxford Research Encyclopaedia of Neuroscience | 10.1093/acrefore/9780190264086.013.46 | review |
| Recurrence is required to capture the representational dynamics of the human visual system | Kietzmann, Tim C.; Spoerer, Courtney J.; Sörensen, Lynn K. A.; Cichy, Radoslaw M.; Hauk, Olaf; Kriegeskorte, Nikolaus | 2019 | Proceedings of the National Academy of Sciences of the United States of America | 10.1073/pnas.1905544116 | brain_imaging; human; MEG; visual |
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