Department of
Psychological and Brain Sciences

• Cognitive Science

• Computational models of memory and language
• Big data approaches to cognitive science
• Knowledge-based intelligent systems
• Computational synthesis of neuroimaging data
• Artificial intelligence; specifically swarm intelligence
• Statistical methodology for analyzing large-scale data

My research focuses on language learning, comprehension, and knowledge representation in humans and machines. I employ a combination of computational and experimental techniques to examine large-scale statistical structure of certain environments (such as language corpora) with the goal of understanding how this structure could be learned and represented with the mathematical capabilities of human learning and memory. This line of my research has applications in machine learning and intelligent systems. The overall premise of my work is that complex behavior often naturally emerges as a product of many simple processors working together at a large scale in response to statistical redundancies in a complex environment.

Under the same unified theme of large-scale statistical learning, I study human associative and recognition memory, categorization, decision making, and the role of attention in reading and perception. I am particularly interested in the temporal dynamics of learning in all these domains, and how to model the time course of knowledge acquisition. My secondary interests involve the application of these models to practical problems in text mining, intelligent search algorithms, and automated comprehension and scoring algorithms. 

Taler, V., Johns, B. T., Young, K., Sheppard, C., & Jones, M. N. (2013). A computational analysis of semantic structure in bilingual verbal fluency performance. Journal of Memory and Language, 69(4), 607-618.

McRae, K., & Jones, M. N. (2013). Semantic memory. In D. Reisberg (Ed.) The Oxford Handbook of Cognitive Psychology. Oxford University Press.

Johns, B. T., Jones, M. N., & Mewhort, D. J. K. (2012). A synchronization account of false recognition. Cognitive Psychology, 65, 486-518.

Johns, B. T., Gruenenfelder, T. M., Pisoni, D. B., & Jones, M. N. (2012). Effects of word frequency, contextual diversity, and semantic distinctiveness on spoken word recognition. Journal of the Acoustical Society of America, 132:2, EL74-EL80.

Hills, T. T., Jones, M. N., & Todd, P. T. (2012). Optimal foraging in semantic memory. Psychological Review, 119, 431-440.

Johns, B. T., & Jones, M. N. (2012). Perceptual inference from global lexical similarity. Topics in Cognitive Science, 4:1, 103-120.

Jones, M. N., Johns, B. T., Recchia, G. L. (2012). The role of semantic diversity in lexical organization. Canadian Journal of Experimental Psychology, 66, 121-132.

Johns, B. T., & Jones, M. N. (2010). Evaluating the random representation assumption of lexical semantics in cognitive models. Psychonomic Bulletin & Review, 17, 662-672.

Riordan, B., & Jones, M. N. (2010). Redundancy in linguistic and perceptual experience: Comparing distributional and feature-based models of semantic representation. Topics in Cognitive Science, 3:2, 303-345.

Hare, M., Jones, M. N., Thomson, C., Kelly, S., & McRae, K. (2009). Activating event knowledge. Cognition, 111 (2), 151-167.

Recchia, G. L., & Jones, M. N. (2009). More data trumps smarter algorithms: Comparing pointwise mutual information with latent semantic analysis. Behavior Research Methods, 41, 657-663.

Jones, M. N. & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1-37.

Jones, M. N., Kintsch, W., & Mewhort, D. J. K. (2006). High-dimensional semantic space accounts of priming. Journal of Memory and Language, 55, 534-552.