Jay McClelland (website)
Over the years, my research has addressed a broad range of cognitive neuroscience issues in learning, memory, language and cognitive development. I view cognitive functions as emerging from the parallel, distributed processing activity of neural populations, with learning occurring through the adaptation of connections among participating neurons, as discussed in Parallel Distributed Processing (Rumelhart, McClelland, and the PDP Research Group, 1986). Research in the lab revolves around efforts to develop explicit computational models based on these ideas; to test, refine, and extend the principles embodied in the models; and then to apply the models to substantive research questions through behavioral experiment, computer simulation, functional brain imaging, and mathematical analysis.
Recently I have begun to focus on mathematical cognition, and the lab is actively recruiting students and collaborators on this topic. Please see the statement about this on my website.
Ph.D., Mechanical and Aerospace Engineering, Princeton University, 2007. As a graduate student Juan Gao's research interests spanned a wide range of topics in dynamics and its application in neuroscience. Her current interests focus on dynamics of decision-making, especially the effects of reward conditions, viewing time and previous sequences. Together with Prof. McClelland Juan is responsible for the theoretical and computational aspect of the decision-making research in the lab.
J. Gao, K. Wong, P. Holmes and J. D. Cohen. Integrated models for sequential effects in two-choice forced-choice tasks serial reaction-time tasks (Under review).
J. Gao and P. Holmes. On Dynamics of Electrically-coupled Neurons with Inhibitory Synapses. J. Comp. Neurosci., 22 (1):39-61, 2007.
I'm interested in models and experimental approaches related to the visual perception of multiple objects. The model I've been developing with Dr. McClelland explores potential complementary contributions of the dorsal and ventral visual pathways to perception when more than one object is present.
I am interested in conceptual (aka semantic) knowledge. In my view, semantic representations are influenced not only by what we sense and interact with in our experience but also by language. My work with Jay McClelland has focused on developing neural network models of deficits in semantic dementia, as well as cross-linguistic behavioral data from normal individuals. Our approach emphasizes learning / experience and individual differences as major factors in explaining the variability among individuals and between groups.
Dilkina, K., McClelland, J. L., & Plaut, D. C. (2008). A single-system account of semantic and lexical deficits in five semantic dementia patients, Cognitive Neuropsychology, 25(2), 136-164.
Dilkina, K., McClelland, J. L., & Boroditsky, L. (2007). How language affects thought in a connectionist model. 29th Annual Meeting of the Cognitive Science Society, 215-220.
I'm interested in statistical learning and computational models. Right now, I'm working on behavioral experiments in word segmentation, trying to distinguish between some competing models. I'm also interested in connectionist and probabilistic models more broadly, and I have a few side projects applying these to other domains, including memory for counterintuitive concepts.
I’m expecting my B.S. and M.S. in Symbolic Systems in 2009. My research in the lab has focused on perceptual category learning. For my Master's thesis, I am investigating how people learn categories from a combination of labeled and unlabeled stimuli.
Lake, B. M., Vallabha, G, K., and McClelland, J. L. (2008). Modeling unsupervised perceptual category learning. In Proceedings of the 7th International Conference on Development and Learning.
Lake, B. M. and Cottrell, G. W. (2005). Age of acquisition in facial identification: A connectionist approach. In Proceedings of the 27th Annual Cognitive Science Conference. Mahwah, NJ: Lawrence Erlbaum.
Sharareh holds B.Sc. and M.Sc. degrees from the Electrical Engineering (EE) department of the University of Minnesota. In 2007, she joined Prof. McClelland's lab where she works on understanding the neural basis of decision making. She is currently a graduate student at the EE department at Stanford University.
Sharareh Noorbaloochi, Jose F. Barbe, Ahmed H. Tewfik: Probabilistic Modeling of Multi-level Genetic Regulatory Logic, Workshop on Genomics Signal Processing and Statistics (GENSIPS), 2006.
I'm an undergraduate, majoring in Symbolic Systems. My work in the lab has been in two different areas: modeling children’s development in performance on the balance scale task and modeling lateralization of semantic knowledge in the anterior temporal lobes.
Schapiro, A. C. & McClelland, J. L. (submitted). A Connectionist Model of a Continuous Developmental Transition in the Balance Scale Task.Stanford University, CA.
Thomas, M. S. C., McClelland, J. L., Richardson, F. M., Schapiro, A. C., & Baughman, F. (in press). Dynamical and Connectionist Approaches to Development: Toward a Future of Mutually Beneficial Co-evolution. In J.P. Spencer, M. S. C. Thomas, & J. L. McClelland, (Eds). Toward a New Grand Theory of Development: Connectionism and Dynamic Systems Theory Re-Considered. Oxford University Press.
Ph.D., Neurobiology, Weizmann Institute of Science. M.Sc., Physiology - Faculty of Medicine, Tel Aviv University. B.Sc., Biology - Faculty of Life Sciences, Tel Aviv University. I’m interested in visual perception and in the neural population dynamics that give rise to it. I have studied spatiotemporal neural response patterns in both cat, using voltage-sensitive dye imaging, and human, using combined MEG/EEG/MRI. My current work in human is focused on feature-based attention and perceptual decision-making.
Sharon, D & Grinvald, A (2002). Dynamics and Constancy in Cortical Spatiotemporal Patterns of Orientation Processing. Science 295: 512-515.
Sharon D, Hämäläinen MS, Halgren E, Tootell RBH, Belliveau JW (2007). The advantage of combining MEG and EEG: Comparison to fMRI in focally-stimulated visual cortex. NeuroImage 36 (4): 1225-1235.
I'm interested in how people make predictions and inferences about the causes and outcomes of events, and how to ground this type of reasoning in the framework of domain-general learning mechanisms. Currently I have been focusing on a series of experiments that investigate how causal framing and the learning task (e.g., prediction vs. reaction) affect participants' inferences about simple cue-outcome relationships. I am also interested in how to relate this work to computational models of learning and memory.
Sternberg, D.A., & McClelland, J.L. (2009). When should we expect indirect effects in human contingency learning? In N.A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society, 206-211.
Sternberg, D.A., & McClelland, J.L. (2009). How do we get from propositions to behavior? Commentary on a target article by Mitchell, De Houwer and Lovibond. Behavioral and Brain Sciences, 32, 226-227.