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).

Dr. Cottrell discusses the Temporal Dynamics of Learning Center (TDLC)


Research Topics & Publications

Perceptual expertise & face recognition - 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.
  • Deep learning - 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.
  • Object saliency & eye movements - 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.
  • Hemispheric lateralization - 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.
  • Theoretical approach to sensory coding - 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).
  • Other topics - 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.

    Lab Members

    Guru & Principal Investigator

    Dr. Garrison W. Cottrell

    Current projects:
  • TDLC Director
  • Mentoring students
  • Physics PhD Candidate

    William Fedus

    Deep Learning

    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 Alumni & Visitors By... Lab Role or Current Job


    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.