Matthew Kelly Ph.D., M.Sc., B.Sc. | College of Information Sciences and Technology
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Matthew Kelly, Ph.D., M.Sc., B.Sc.

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Dr. Matthew A. Kelly
Title(s):
  • Postdoctoral Scholar

Contact:
E370 Westgate Building
University Park, PA 16802
Education:
  • Ph.D. in Cognitive Science from Carleton University

  • M.Sc. in Computer Science from Queen's University

  • B.Sc. in Computer Science (Specialization: Cognitive Science) from Queen's University

Research Interests:

Human memory and language and computational models thereof.

Publications:

Ororbia II, A. G., Mali, A., Kelly, M. A., & Reitter, D. (2019). Like a baby: Visually situated neural language acquisition. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 5127-5136). Florence, Italy. https://www.aclweb.org/anthology/P19-1506

Vertolli, M. O., Kelly, M. A., & Davies, J. (2018). Coherence in the visual imagination. Cognitive Science, 42(3), 885-917. doi: 10.1111/cogs.12569

Arora, N., West, R., Brook, A., & Kelly, M. A. (2018). Why the common model of cognition needs holographic a-priori categories. In J. Laird, C. Lebiere, & P. S. Rosenbloom (Eds.), Papers on the Common Model of Cognition, Procedia Computer Science, 145, 680-690. doi: 10.1016/j.procs.2018.11.060

Kelly, M. A., & Reitter, D. (2018). How language processing can shape a common model of cognition. In J. Laird, C. Lebiere, & P. S. Rosenbloom (Eds.), Papers on the Common Model of Cognition, Procedia Computer Science, 145, 724-729. doi: 10.1016/j.procs.2018.11.047

Kelly, M. A., & West, R. L. (2017). A framework for computational models of human memory. In J. Laird, C. Lebiere, & P. S. Rosenbloom (Organizers), The 2017 AAAI Fall Symposium Series: Technical Reports (pp. 376-381). Palo Alto, California: The AAAI Press. https://aaai.org/ocs/index.php/FSS/FSS17/paper/view/15952

Kelly, M. A., & Reitter, D. (2017). Holographic Declarative Memory: Using distributional semantics within ACT-R. In J. Laird, C. Lebiere, & P. S. Rosenbloom (Organizers), The 2017 AAAI Fall Symposium Series: Technical Reports (pp. 382-387). Palo Alto, California: The AAAI Press. https://aaai.org/ocs/index.php/FSS/FSS17/paper/view/16001

 Kelly, M. A., Reitter, D., & West, R. L. (2017). Degrees of separation in semantic and syntactic relationships. In M. K. van Vugt, A. P. Banks, & W. G. Kennedy (Eds.), Proceedings of the 15th International Conference on Cognitive Modeling (pp. 199-204). Coventry, United Kingdom: University of Warwick. https://iccm-conference.neocities.org/2017/ICCMprogram_files/paper_42.pdf

Kelly, M. A. & West, R. L. (2017). Argument complexity: Teaching undergrads to make better arguments. Psychology Teaching Review, 23(2), 20-31. https://shop.bps.org.uk/publications/publications-by-subject/psychology-teaching-review-vol-23-no-2-2017.html

Kelly M. A., Mewhort, D. J. K., & West, R. L. (2017). The Memory Tesseract: Mathematical Equivalence Between Composite and Separate Storage Memory Models. Journal of Mathematical Psychology, 77, 142-155. doi: 10.1016/j.jmp.2016.10.006

Rutledge-Taylor, M. F., Kelly M. A., West, R. L., & Pyke, A. A. (2014). Dynamically structured holographic memory. Biologically Inspired Cognitive Architectures, 9, 9-32. doi: 10.1016/j.bica.2014.06.001

Kelly, M. A., Blostein, D., & Mewhort, D. J. K. (2013). Encoding structure in holographic reduced representations. Canadian Journal of Experimental Psychology, 67, 79-93. doi: 10.1037/a0030301

Kelly, M. A. & West, R. L. (2012). From vectors to symbols to cognition: The symbolic and sub-symbolic aspects of vector-symbolic cognitive models. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 1768-1773). Austin, TX: Cognitive Science Society. http://mindmodeling.org/cogsci2012/papers/0311/

Mewhort, D. J. K., Johns, B. T., & Kelly, M. (2010). Applying the permutation test to factorial designs. Behavior Research Methods, 42, 366-372. doi: 10.3758/BRM.42.2.366

Mewhort, D. J. K., Kelly, M., & Johns, B. T. (2009). Randomization tests and the unequal-N/unequal-variance problem. Behavior Research Methods, 41, 664-667. doi: 10.3758/BRM.41.3.664