Christopher R. Cox

Office: 228 Audubon Hall
Department of Psychology
Louisiana State University
Baton Rouge, LA 70803
Office Phone: 225-578-1051
Email: [email protected]

Dr. Cox is accepting new students for Fall 2025.

View Dr. Cox's Google Scholar page. 

Research Interests

Our knowledge of the world shapes our path through it: the decisions we make, the things we attend to and ignore, and what we think we know about an unfamiliar environment when we enter it for the first time. How this knowledge is acquired, and a brain-based account of what that knowledge is and how it is physically represented in the brain, are critical questions for cognitive psychology and cognitive neuroscience. My research applies a combination of behavioral research, computational modelling, machine learning, and neuroimaging to investigate the representational format of semantic knowledge.

Some active lines of research include:

• Applying experimental machine learning tools to discover complex brain networks in neuroimaging data that are associated with semantic knowledge.
• Exploring the context-sensitivity of semantic knowledge, both to experimental manipulation and with respect to individual differences
• Building computational models of reading that study in the interplay of written and spoken language with semantic knowledge.
• Studying computational models as simulated students that can be taught by “machine teachers” that explore the training space to build effective (and possibly counterintuitive) curricula.

Selected publications:

Cox, C. R., Rogers, T. T., Shimotake, A., . . . Lambon Ralph, M. A. (preprint). Representational similarity learning reveals a graded multi-dimensional semantic space in the human anterior temporal cortex. BioRxiv. 

Frisby, S. L., Halai, A. D. Cox, C. R., Lambon Ralph, M. A., & Rogers, T. T. (2023). Decoding semantic representations in mind and brain. Trends in Cognitive Sciences, 27(3), 258 – 281. 

Cox, C. R. & Haebig, E. K. (2022). Child-Oriented Word Associations Improve Models of Early Word Learning. Behavior Research Methods. 

Rogers, T. T., Cox, C. R., Lu, Q., . . . Lambon Ralph, M. A. (2021). Evidence for a deep, distributed and dynamic code for animacy in human ventral anterior temporal cortex. eLife, 10, e66276. 

Cohen, A. S., Cox, C. R., Cowan, T., Masucci, M. D., Le, T. P., Docherty, A. R., & Bedwell, J. S. (2021). High predictive accuracy of negative schizotypy with acoustic measures. Clinical Psychological Science, 21677026211017835. 

Cox, C. R., & Rogers, T. T. (2021). Finding distributed needles in neural haystacks. The Journal of Neuroscience, 41(5), 1019-1032. 

Cox, C. R., Moscardini, E. H., Cohen, A. S., & Tucker, R. P. (2020). Machine learning for suicidology: A practical review of exploratory and hypothesis-driven approaches. Clinical Psychology Review, 101940. 

Haebig, E., Jiménez, E., Cox, C. R., & Hills, T. T. (2020). Characterizing the early vocabulary profiles of preverbal and minimally verbal children with autism spectrum disorder. Autism, 1362361320973799. 

Oswal, U., Cox, C. R., Lambon Ralph, M. A., Rogers, T. T., & Nowak, R. (2016). Representational similarity learning with application to brain networks. Proceedings of the 33rd International Conference on Machine Learning, 1041 – 1049. 

Cox, C. R., Seidenberg, M. S., & Rogers, T. T. (2015). Connecting functional brain imaging and parallel distributed processing. Language, Cognition and Neuroscience, 30(4), 380–394. 

Rao, N. S., Cox, C. R., Nowak, R. D., & Rogers, T. T. (2013). Sparse Overlapping Sets Lasso for multitask learning and its application to fMRI analysis. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Weinberger (Eds.), Advances in neural information processing systems, 26, 2202–2210. Curran Associates, Inc.