The availability of large textual datasets in recent years has enabled researchers to investigate the statistical structure of language on a scale not possible in previous decades. By combining tools from cognitive science with computational and statistical techniques for analyzing large databases of text, researchers have developed computational methods for automatically finding clusters of words that are related in meaning. Using these advances as a starting point, my research at CRASSH aims to develop novel computational methods that can be used to investigate historical cultural concepts, to track how they change over time, and to characterize their properties. I have been especially focused on the development of simple, interpretable algorithms and tools that permit a clear understanding of the pathway from the underlying texts to the conceptual relationships unearthed by a combination of close reading and computational analysis.


Formerly Dr Gabriel Recchia was a Research Associate with the Concept Lab, part of the Cambridge Centre for Digital Knowledge at CRASSH and a postdoctoral fellow at the Institute for Intelligent Systems at the University of Memphis. He received his PhD in Cognitive Science from Indiana University, where he developed computational models of lexical semantics as part of the Cognitive Computing Laboratory. He has articles published or forthcoming in Behavior Research Methods, The Quarterly Journal of Experimental Psychology, and Frontiers in Human Neuroscience, among other venues.


Jones, M. N., Gruenenfelder, T., & Recchia, G. (in press). In defense of spatial models of semantic representation. New Ideas in Psychology.

Recchia, G. & Nulty, P. (2017). Improving a fundamental measure of lexical association. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Recchia, G., Jones, E., Nulty, P., Regan, J., & de Bolla, P. (2016). Tracing shifting conceptual vocabularies through time. In: Proc. of Detection, Representation and Management of Concept Drift in Linked Open Data (Drift-a-LOD), Bologna, Italy, 20 November 2016 (pp. 2-9).

Gruenenfelder, T. M., Recchia, G., Rubin, T., & Jones, M. N. (2016). Graph-theoretic properties of networks based on word association norms: Implications for models of lexical semantic memory. Cognitive Science, 40(6), 1460-95. doi: 10.1111/cogs.12299.

Hladish, T.J., Pearson, C.A.B, Chao, D.L., Rojas, D.P., Recchia, G.L., Gómez-Dantés, H., Halloran, M.E., Pulliam, J.R.C., & Longini, I.M. (2016). Projected impact of dengue vaccination in Yucatán, Mexico. PLoS Neglected Tropical Diseases. DOI: 10.1371/journal.pntd.0004661

Recchia, G. & Louwerse, M. (2015). Archaeology through computational linguistics: Inscription statistics predict excavation sites of Indus Valley artifacts. Cognitive Science. DOI: 10.1111/cogs.12311

Recchia, G., Sahlgren, M., Kanerva, P., & Jones, M. N. (2015). Encoding sequential information in semantic space models: Comparing holographic reduced representation and random permutation. Computational Intelligence and Neuroscience. doi: 10.1155/2015/986574

Recchia, G., & Louwerse, M. M. (2015). Reproducing affective norms with lexical co-occurrence statistics: Predicting valence, arousal, and dominance. The Quarterly Journal of Experimental Psychology, 68(8), 1584-1598. doi: 10.1080/17470218.2014.941296

Louwerse, M. M., Hutchinson, S., Tillman, R., & Recchia, G. (2015). Effect size matters: the role of language statistics and perceptual simulation in conceptual processing. Language, Cognition and Neuroscience, 30(4), 430-447. doi: 10.1080/23273798.2014.981552

Recchia, G., Slater, A. L., & Louwerse, M. (2014). Predicting the good guy and the bad guy: Attitudes are encoded in language statistics. Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 1264-1269).

Recchia, G., & Louwerse, M. (2014). Grounding the ungrounded: Estimating locations of unknown place names from linguistic associations and grounded representations. Proceedings of the 36thAnnual Conference of the Cognitive Science Society (pp. 1270-1275).

Recchia, G. L., & Louwerse, M. M. (2013). A comparison of string similarity measures for toponym matching. Proceedings of ACM SIGSPATIAL CoMP ’13. Orlando, FL: ACM.

Recchia, G. L., & Jones, M. N. (2012). The semantic richness of abstract concepts. Frontiers in Human Neuroscience, 6(315). doi: 10.3389/fnhum.2012.00315

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.

Hard, B., Recchia, G., & Tversky, B. (2011). The shape of action. Journal of Experimental Psychology: General, 140(4), 586-604.

Cox, G., Kachergis, G., Recchia, G., & Jones, M. N. (2011). Towards a scalable holographic representation of word form. Behavior Research Methods, 43(3), 602-615.

Jones, M. N., Gruenenfelder, T. M., & Recchia, G. (2011). In defense of spatial models of lexical semantics. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 3444-3449).

Kachergis, G., Recchia, G., & Shiffrin, R. M. (2011). Adaptive magnitude and valence biases in a dynamic memory task. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 819-824).

Recchia, G. L., Jones, M. N., Sahlgren, M., & Kanerva, P. (2010). Encoding sequential information in vector space models of semantics: Comparing holographic reduced representation and random permutation. In S. Ohlsson and R. Catrambone (Eds.), Proceedings of the 32nd Cognitive Science Society (pp. 865-870).

Jones, M. N., & Recchia, G. L. (2010). You can’t wear a coat rack: A binding framework to avoid illusory feature migrations in perceptually grounded semantic models. In S. Ohlsson and R. Catrambone (Eds.), Proceedings of the 32nd Annual Cognitive Science Society (pp. 877-882).

Recchia, G., & Jones, M. N. (2009). More data trumps smarter algorithms: Training computational models of semantics on very large corpora. Behavior Research Methods, 41(3), 647-656.

Recchia, G., Johns, B. T., & Jones, M. N. (2008). Context repetition benefits are dependent on context redundancy. In V. Sloutsky, K. McRae, & B. Love (Eds.), Proceedings of the 30th Cognitive Science Society (pp. 267-272).



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