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.

Cox, C. R., & Rogers, T. T. (2020). Finding distributed needles in neural haystacks. Journal of Neuroscience.

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.

Cohen, A. S., Cox, C. R., Le, T. P., Cowan, T., Masucci, M. D., Strauss, G. P., & Kirkpatrick, B. (2020). Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia. NPJ schizophrenia, 6(1), 1-9.

Cohen, A. S., Cox, C. R., Masucci, M. D., Le, T. P., Cowan, T., Coghill, L. M., . . . Elvevåg, B. (2020). Digital Phenotyping Using Multimodal Data. Current Behavioral Neuroscience Reports, 1-9.

Cohen, A. S., Schwartz, E., Le, T., Cowan, T., Cox, C. R., Tucker, R., . . . Elvevåg, B. (2020). Validating digital phenotyping technologies for clinical use: the critical importance of “resolution”. World Psychiatry, 19(1), 114.

Zhou, S., Li, W., Cox, C. R., & Lu, H. (2020). Side Information Dependence as a Regularizer for Analyzing Human Brain Conditions across Cognitive Experiments. Paper presented at the The Thirty-Fourth AAAI Conference on Artificial Intelligence, New York: USA.


Rogers, T. T., Cox, C.R., Lu, Q., Shimotake, A., Kikuch, T., Kunieda, T., . . . Lambon Ralph, M. A. (2019). Evidence for a deep, distributed and dynamic semantic code in human ventral anterior temporal cortex. bioRxiv, 695049. doi:10.1101/695049

Cox, C. R., Cooper Borkenhagen, M., & Seidenberg, M. S. (2019). Efficiency of Learning in Experience-Limited Domains: Generalization Beyond the WUG Test. Paper presented at the Proceedings of the 41st Annual Conference of the Cognitive Science Society, Montreal, QB: Cognitive Science Society.

Zhou, S., Li, W., Cox, C. R., & Lu, H. (2019). Domain Independent SVM for Transfer Learning in Brain Decoding. arXiv preprint(arXiv:1903.11020).

Cox, C. R., & Rogers, T. T. (2018). Connecting natural and artificial neural networks in functional brain imaging using structured sparsity. bioRxiv, 390534. doi:10.1101/390534

Kattner, F., Cochrane, A., Cox, C. R., Gorman, T. E., & Green, C. S. (2017). Perceptual Learning Generalization from Sequential Perceptual Training as a Change in Learning Rate. Curr Biol, 27(6), 840-846. doi:10.1016/j.cub.2017.01.046

Kattner, F., Cox, C. R., & Green, C. S. (2016). Transfer in Rule-Based Category Learning Depends on the Training Task. PLoS One, 11(10), e0165260. doi:10.1371/journal.pone.0165260

Oswal, U., Cox, C. R., Lambon Ralph, M. A., Rogers, T. T., & Nowak, R. (2016). Representational similarity learning with application to brain networks. Paper presented at the ICML'16 Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, USA.

Rao, N., Nowak, R., Cox, C. R., & Rogers, T. (2016). Classification With the Sparse Group Lasso. IEEE Transactions on Signal Processing, 64(2), 448-463. doi:10.1109/tsp.2015.2488586

Rogers, T. T., & Cox, C. R. (2015). The Neural Basis of Conceptual Knowledge: Revisiting a Golden Age Hypothesis in the Era of Cognitive Neuroscience. In D. R. Addis, M. Barense, & A. Duarte (Eds.), The Wiley Handbook on the Cognitive Neuroscience of Memory: Wiley.

Cox, C. R., Seidenberg, M. S., & Rogers, T. T. (2014). Connecting functional brain imaging and Parallel Distributed Processing. Language, Cognition and Neuroscience, 30(4), 380-394. doi:10.1080/23273798.2014.994010

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.