February 18, 2022
Anne Marie Crinnion and I wrote this chapter back in 2020, and it’s finally out in print! We try to take a slightly novel approach to reviewing spoken word recognition.
Magnuson, J. S. & Crinnion, A. M. (2022). Spoken word recognition. In A. Papafragou, J. C. Trueswell, & L. R. Gleitman (Eds.), The Oxford Handbook of the Mental Lexicon, pp. 461-490. DOI: 10.1093/oxfordhb/9780198845003.013.23 [PREPRINT PDF]
May 6, 2021
This was an interesting learning experience. Thanks to Sahil and Dave for leading the way!
Luthra, S., Saltzman, D., Myers, E.B. & Magnuson, J.S. (2021). Listener expectations and the perceptual accommodation of talker variability: A pre-registered replication. Attention, Perception, & Psychophysics. https://doi.org/10.3758/s13414-021-02317-x. [PREPRINT PDF]
February 9, 2021
I set out to do something that seemed like it shouldn’t be too hard in R. I had a dataframe with RTs for a bunch of subjects, and I wanted to convert the RTs to z-scores relative to each subject’s own mean. To do this relative to the global mean is super easy:
data$global_zRT <- scale(data$RT)
However, getting it scaled by subject mean (which could be useful for visual inspection of data, or for some analyses) turns out not to be trivial, and I was unable to find relevant posts via google search. Before posting to stackoverflow myself, I tried our lab Slack channel. Dave Saltzman and Anne Marie Crinnion produced a solution quickly with dplyr. However, my dplyr calls were getting blocked by plyr. Anne Marie pointed out how to make the command bullet proof. Note that ‘subject’ here is a column in the dataframe, not a keyword of some sort.
data <- data %>% dplyr::group_by(subject) %>% dplyr::mutate(zRT = scale(RT))
— Jim Magnuson
In this article led by Sahil Luthra, we introduce a new model of print-to-(over-time) speech.
Luthra, S., You, H., Rueckl, J. G., & Magnuson, J. S. (2020). Friends in low‐entropy places: Orthographic neighbor effects on visual word identification differ across letter positions. Cognitive Science, 44(12). ee12917. https://doi.org/10.1111/cogs.12917
April 10, 2020
This brief report has been YEARS in the making. Largest team on any publication from our lab? Congrats especially to Heejo, Sahil & Monica!
Magnuson, J.S., You, H., Luthra, S., Li, M., Nam, H., Escabí, M., Brown, K., Allopenna, P.D., Theodore, R.M., Monto, N., & Rueckl, J.G. (2020). EARSHOT: A minimal neural network model of incremental human speech recognition. Cognitive Science, 44, e12823. http://dx.doi.org/10.1111/cogs.12823 [PDF] [Supplementary Materials]
April 2, 2020
Starting April 6, Prof. Magnuson will be adding a special component to his course for undergraduate research assistants (“research in cognitive neuroscience”). Pending approval, we will use a DataCamp virtual classroom to add training this spring and summer in R, Python, and other data science tools.
October 17, 2019
Deadline extended! See https://magnuson.psy.uconn.edu/Positions
September 4, 2019
Jim Magnuson and Jay Rueckl are looking for a postdoc to work on computational modeling of human speech recognition. We are working on bridging the gap between cognitive models and state-of-the-art deep learning models for automatic speech recognition. Details available here: https://magnuson.psy.uconn.edu/Positions/
August 3, 2019
We had 3 presentations/papers at CogSci2019.
June 14, 2019
After years of dedicated work, Monica Li (with support from her co-authors) has published a terrific new paper in the Journal of Memory & Language:
Li, M.Y.C., Braze, D., Kukona, A., Johns, C.L., Tabor, W., Van Dyke, J. A., Mencl, W.E., Shankweiler, D.P., Pugh, K.R., & Magnuson, J.S. (2019). Individual differences in subphonemic sensitivity and phonological skills. Journal of Memory & Language, 107, 195-215. https://doi.org/10.1016/j.jml.2019.03.008 (links at publications page)
In addition to an epic set of experiments and individual differences measures (and implications for whether phonological processing is unusually precise or imprecise in individuals with lower reading ability), Monica provides a direct comparison between growth curve analysis (GCA) and generalized additive models (GAMs).
Congrats, Monica!