New chapter

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]

Do X in Y: Convert values to subject-relative z-scores

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$zRT <- data %>% dplyr::group_by(subject) %>% dplyr::mutate(zRT = scale(RT))

— Jim Magnuson

New-ish publication

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

New publication — EARSHOT model of human speech recognition

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]

3 lab presentations / proceedings publications at CogSci2019

We had 3 presentations/papers at CogSci2019.

  1. Magnuson, J.S., Li, M., Luthra, S., You, H., & Steiner, R. (2019). Does predictive processing imply predictive coding in models of spoken word recognition? In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 735-740). Montreal, QB: Cognitive Science Society. [PDF]
  2. Magnuson, J.S., You, H., Rueckl, J. R., Allopenna, P. D., Li, M., Luthra, S., Steiner, R., Nam, H., Escabi, M., Brown, K., Theodore, R., & Monto, N. (2019). EARSHOT: A minimal network model of human speech recognition that operates on real speech.  In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 2248-2253). Montreal, QB: Cognitive Science Society. [PDF]
  3. McClelland, J.L., McRae, K., Borovsky, A., Kuperberg, G., & Hill, F. (2019). Symposium in memory of Jeff Elman: Language learning, prediction, and temporal dynamics. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 33-34). Montreal, QB: Cognitive Science Society. [PDF]

New paper from Monica Li et al.

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!