Mission
Professor Gary Cottrell's lab at the University of California, San Diego (UCSD) investigates the mechanisms that underlie cognition in animals and people using computational modeling. Some projects have also advanced the state-of-the-art in machine learning and computer vision. Our work is highly interdisciplinary and draws on findings in psychology, neuroscience, machine learning, computer vision, and behavioral economics.
Recent projects have included: the SUN model of stimulus-driven and task-driven visual and aural attention, sparse principal component analysis, modeling transsaccadic evidence accumulation for object classification, investigating interhemispheric connectivity, and seeking to understand the mechanisms that underlie face processing.
Professor Cottrell is the head of the NSF-sponsored Temporal Dynamics of Learning Center (TDLC). He also directs the Interdisciplinary Ph.D. program in Cognitive Science at UCSD, and he is one of the founders of the Perceptual Expertise Network (PEN).
Research Topics & Publications
- Our lab has developed The Model - a framework for object recognition that has been used to model more behavioral results in perceptual expertise and face recognition than any other model. The Model has repeatedly shown that there is nothing "special" about face perception in the ventral visual stream--all aspects of expertise are captured by experience.
Wang, P., Gauthier, I., and Cottrell, G.W. (2014) Experience matters: Modeling the relationship between face and object recognition. In Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Cottrell, G.W. and Hsiao, J.H. (2011) Neurocomputational Models of Face Processing. In A.J. Calder, G. Rhodes, M. Johnson, and J. Haxby (Eds.) The Oxford Handbook of Face Perception. Oxford, UK: Oxford University Press.
Palmeri, T. and Cottrell, G.W. (2009) Modeling Perceptual Expertise. In Isabel Gauthier, Michael J. Tarr, and Daniel Bub, (Eds.) Perceptual Expertise, Oxford: Oxford University Press.
Tong, M.H., Joyce, C.A., and Cottrell, G.W. (2008) Why is the fusiform face area recruited for novel categories of expertise? A neurocomputational investigation. Brain Research, 1202:14-24.
Dailey, Matthew N., Cottrell, Garrison W., Padgett, Curtis, and Ralph Adolphs (2002) EMPATH: A neural network that categorizes facial expressions. Journal of Cognitive Neuroscience 14(8):1158-1173.
- Neural networks with 5+ layers are now completely trainable, due to advances in hardware performance and dataset size. These networks produce state of the art performance in object categorization, speech recognition, and many other tasks.
P. Wang, V. Malave, B. Cipollini (accepted) Encoding Voxels with Deep Learning. Journal of Neuroscience.
M. Malmir, K. Sikka, D. Forster, J. Movellan and G. W. Cottrell. (2015) Deep Q-learning for Active Recognition of GERMS: Baseline performance on a standardized dataset for active learning. In Proceedings of the British Machine Vision Conference (BMVC), BMVA Press. 161.1-161.11.
Shan, H., Zhang, L., and Cottrell, G.W. (2007) Recursive ICA. In: Advances in Neural Information Processing Systems 20 (NIPS-2007).
Cottrell, Garrison W. (2006) New life for neural networks. Science. 313(5786):454-5.
- Primates subsample images continuously by making fixations, to utilize the fovea (the small high-resolution window of the retina) across the task-relevant portions of the image. We want to understand how this sampling behavior interacts with object recognition performance and perceptual expertise.
Kanan, C.M., Ray, N.A., Bseiso, D.N.F., Hsiao, J.H., Cottrell, G.W. (2014) Predicting an observer's task using Multi-Fixation Pattern Analysis. In Proceedings of the Annual Eye Tracking Research & Applications Symposium (ETRA 2014), March, 24-26, Safety Harbor, FL.
Tsuchida, Tomoki and Cottrell, Garrison W. (2012) Auditory saliency using natural statistics. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Kanan, C. and Cottrell, G.W. (2010) Robust classification of objects, faces, and flowers using natural image statistics. In: Proc IEEE Computer Vision and Pattern Recognition Conference (CVPR-2010).
Zhang, L., Tong, M.H., Marks, T.K., Shan, H., and Cottrell, G.W. (2008) SUN: A Bayesian Framework for Saliency Using Natural Statistics. Journal of Vision, 8(7):32, 1-20.
Barrington, L., Marks, T. K., Hui-wen Hsiao, J., and Cottrell, G.W. (2008) NIMBLE: A kernel density model of saccade-based visual memory. Journal of Vision, 8(14):17.
Hsiao, J. and Cottrell, G.W. (2008) Two fixations suffice in face recognition. Psychological Science. 19(10):998-1006.
- One fundamental aspect of visual perception is that the left and right sides of the brain are specialized to process different aspects of an image. We have explored potential anatomical and developmental origins of this effect, how it relates to face perception, and how interactions between the two hemispheres modulate this effect.
Cipollini, B. and Cottrell, G.W. (2014) A developmental model of hemispheric asymmetries of spatial frequencies. In Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. Winner of the 2014 Perception/Action Modeling Prize
Hsiao, Janet H., Cipollini, Ben, and Cottrell, Garrison W. (2013) Hemispheric asymmetry in perception: A differential encoding account. Journal of Cognitive Neuroscience 25(7):998-1007.
Cipollini, B. and Cottrell, G.W. (2013) Uniquely human developmental timing may drive cerebral lateralization and interhemispheric coupling. In Proceedings of the 35th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Cipollini, Benjamin, Hsiao, Janet H-W., and Cottrell, Garrison W. (2012) Connectivity asymmetry can explain visual hemispheric asymmetries in local/global, face, and spatial frequency processing. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Hsiao, J., Shieh, D. and Cottrell, G.W. (2008) Convergence of the visual field split: Hemispheric modeling of face and object recognition. Journal of Cognitive Neuroscience, 20(12):2298-2307.
- How do general principles of sensory coding explain the properties of neurons within the visual and auditory streams at multiple layers of processing? We have developed a mathematical approach that explains many features of these sensory streams.
Shan, Honghao, and Cottrell, G.W. (2014) Efficient visual coding: From retina to V2. In International Conference on Learning Representations (ICLR 2014).
Shan, H. and Cottrell, G.W. (2008) Looking around the back yard helps the recognition of faces and digits. In: Proc. IEEE Computer Vision and Pattern Recognition (CVPR-2008).
- The Cottrell lab welcomes students to propose and guide their own research topics. In the past, these have ranged from speech perception to explaining neuroimaging to optimal experimental design.
Nelson, J., McKenzie, C., Cottrell, G.W., and Sejnowski, T. (2010) Experience Matters: information acquisition optimizes probability gain. Psychological Science.
Cowell, R., Huber, D., and Cottrell, G.W. (2009) Virtual brain reading: A connectionist approach to understanding fMRI. In Proceedings of the 31st Annual Meeting of the Cognitive Science Society. Winner of the 2009 Perception/Action Modeling Prize
Tong M.H., Bickett A.D., Christiansen E.M., Cottrell G.W. (2007) Learning grammatical structure with Echo State Networks. Neural Networks, 20(3):424-432.
Guru & Principal Investigator
Dr. Garrison W. Cottrell
CogSci PhD Candidate
Vicente Malave
Machine Learning, Neuroimaging, Optimization, Theoretical Neuroscience
CSE PhD Student
Mohsen Malmir
Social Robotics, Vision, Motor Control
CSE Ph.D student
Yao Qin
Deep networks, computer vision
CSE Ph.D student
Yan Shu
Current projects:
Recurrent attention model of visual expertise (birds)
Cogsci Ph.D student
Amanda Song
Current projects:
facial attractiveness
intermediate representations of faces
CSE PhD Student
Tomoki Tsuchida
Modeling, Sound Perception, Auditory Attention, Decision Making
ECE PhD Student
Panqu Wang
Modeling, Face Recognition, Neural Networks
ECE PhD Student
Yufei Wang
Deep Networks
CSE MS Student
Amey Parulekar
Current project:
Recognizing sign language gestures in videos
CSE MS Student
Rishikesh Ghewari
Current project:
Action Recognition from Videos
ECE Undergraduate Student
Davis Liang
Vision, Object Recognition
Current project:
Modeling holistic face processing
CSE Undergrad Student
Vishaal Prasad
Current project:
Lateralization in vision
CSE Undergraduate Student
Chad Atalla
Vision, Object Recognition
Current project:
Modeling holistic face processing
Lab Opportunities
Dr. Cottrell is currently not accepting new students. However, students and post-docs with funding and/or who are collaborating with students and post-docs in our lab may be
considered, under special circumstances.
Please browse current members to see people's
research interests and current projects. Read about our lab's
research areas to learn more about our projects
and to browse relevant publications.
Contacting us:
For general interest in the lab, email our lab.
For interest in joining a current project, try contacting a relevant lab member.
For interest in proposing your own, new project, please contact Dr. Cottrell.
Coming soon: Past internships and undergraduate projects.
