Dept. of Psychology, MC-716
833 Psychology Bldg.
603 E. Daniel St.
Champaign, IL 61820
Gary Dell's work deals with how people produce and understand sentences, and how these processes can be modelled using neural networks. For example, his research on language production attempts to understand production errors or "slips of the tongue." He has developed a neural net model that makes predictions about the qualitative and quantitative properties of speech errors. These predictions are tested using experimental procedures in which subjects produce words and sentences under controlled conditions. A particularly interesting aspect of the model is that it can be used to understand patterns of behavior resulting from brain damage. By changing the processing characteristics of the model, one can produce speech error patterns that are characteristic of certain types of aphasic patients.
Ph.D. from the University of Toronto
Cognitive Science Proseminar
Connectionist models in psychology
Middleton, E. L., Schwartz, M. F., Dell, G. S., & Brecher, A. (2022). Learning from errors: Exploration of the monitoring learning effect. Cognition, 224, . https://doi.org/10.1016/j.cognition.2022.105057
Dell, G. S., Kelley, A. C., Hwang, S., & Bian, Y. (2021). The adaptable speaker: A theory of implicit learning in language production. Psychological review, 128(3), 446-487. https://doi.org/10.1037/rev0000275
Bian, Y., & Dell, G. S. (2020). Novel stress phonotactics are learnable by English speakers: Novel tone phonotactics are not. Memory and Cognition, 48(2), 176-187. https://doi.org/10.3758/s13421-019-01000-9
Jacobs, C. L., Loucks, T. M., Watson, D. G., & Dell, G. S. (2020). Masking auditory feedback does not eliminate repetition reduction. Language, Cognition and Neuroscience, 35(4), 485-497. https://doi.org/10.1080/23273798.2019.1693051
Rommers, J., Dell, G. S., & Benjamin, A. S. (2020). Word predictability blurs the lines between production and comprehension: Evidence from the production effect in memory. Cognition, 198, . https://doi.org/10.1016/j.cognition.2020.104206