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]
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]
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
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]
Deadline extended! See https://magnuson.psy.uconn.edu/Positions
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/
We had 3 presentations/papers at CogSci2019.
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!
Okay, this one is actually new — it just appeared online today.
You, H. & Magnuson, J. S. (2018). TISK 1.0: An easy-to-use Python implementation of the time-invariant string kernel model of spoken word recognition. Behavior Research Methods. doi:10.3758/s13428-017-1012-5 [PDF]
This documents Heejo You’s beautiful re-implementation of Thomas Hannagan’s original TISK code. We were sad that Thomas could not join us in this paper (he has a new job in industry that precluded that), but we are immensely grateful to him for his help and advice.
I am very pleased to announce (belatedly) that the lab has a new paper out in Frontiers:
Magnuson, J. S., Mirman, D., Luthra, S., Strauss, T., & Harris, H. (2018). Interaction in spoken word recognition models: Feedback helps. Frontiers in Psychology, 9:369. doi:10.3389/fpsyg.2018.00369 [HTML]
This paper was a very long time in the making. This project inspired the jTRACE re-implementation of TRACE, and previous attempts at publication were stymied. The upshot of the paper is that feedback in a model like TRACE affords graceful degradation in the face of noise.