To learn more about my research experiences, check out my cv (updated February 2021).


To understand speech, we have to figure out how the acoustic signal relates to the speech sounds we know (a task made difficult by the considerable variability in how individuals talk), find the word breaks in the speech stream (which aren’t always easy to identify), and retrieve the correct words from our mental dictionaries (and not, for instance, a similar-sounding one). It should be a daunting task, and yet listeners seem to perform the task optimally.

As a graduate researcher, I am particularly interested in the mental computations that underlie the process of spoken word recognition as well as how these computations are achieved in the brain. How are we able to achieve such good perception, especially given the limited amount of cognitive resources we have at our disposal? How do listeners leverage their knowledge of who is talking as well as their knowledge of what that person is likely to say in order to comprehend the speech signal?

Selected Recent Work:


  1. Luthra, S., Peraza-Santiago, G., Beeson, K., Saltzman, D., Crinnion, A. M., & Magnuson, J. S. Robust lexically-mediated compensation for coarticulation: Christmash time is here again. Cognitive Science. In press. [Video Summary.] [OSF Preregistration.] [GitHub].
  2. Luthra, S., Mechtenberg, H., & Myers, E. B. Perceptual learning of multiple talkers requires additional exposure. Attention, Perception, & Psychophysics. In press. [Video Summary]. [OSF Repository].
  3. Luthra, S. The role of the right hemisphere in processing phonetic variability between talkersNeurobiology of Language. In press.
  4. 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), 1-31. [GitHub].
  5. Luthra, S., Magnuson, J. S. & Myers, E. B. (2020). Boosting lexical support does not enhance lexically guided perceptual learning. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication. [OSF Repository]. 
  6. Luthra, S., Correia, J. M., Kleinschmidt, D. F., Mesite, L. & Myers, E. B. (2020). Lexical information guides retuning of neural patterns in perceptual learning for speechJournal of Cognitive Neuroscience, 32(10), 2001-2012. [OSF Repository].
  7. 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(4), 1-17. [Supplementary Materials]. [GitHub].
  8. Luthra, S., Fuhrmeister, P., Molfese, P. J., Guediche, S., Blumstein, S. E., & Myers, E. B. (2019). Brain-behavior relationships in incidental learning of non-native phonetic categories. Brain & Language, 198
  9. Luthra, S., Guediche, S., Blumstein, S. E., & Myers, E. B. (2019). Neural substrates of subphonemic variation and lexical competition in spoken word recognitionLanguage, Cognition and Neuroscience, 34(2), 151-169. [Supplementary Materials.]
  10. Luthra, S., Fox, N. P., & Blumstein, S. E. (2018). Speaker information affects false recognition of unstudied lexical-semantic associatesAttention, Perception & Psychophysics80(4), 894-912. [OSF Repository].
  11. Magnuson, J. S., Mirman, D., Luthra, S., Strauss, T., & Harris, H. D. (2018). Interaction in spoken word recognition models: Feedback helpsFrontiers in Psychology, 9. 1-18
  12. Theodore, R. M., Blumstein, S. E., & Luthra, S. (2015). Attention modulates specificity effects in spoken word recognition: Challenges to the time-course hypothesis. Attention, Perception & Psychophysics, 77(5), 1674-1684.


  1. Grubb, S., Dalal, P., Daniel, J., Peraza-Santiago, G., Luthra, S., Saltzman, D., Xie, B., Crinnion, A.M., & Magnuson, J. S. Talkers, time, tasks, and similarity in spoken word recognition. Psychonomic Society, Virtual Conference, November 2020.
  2. Saltzman, D., Luthra, S., Myers, E. B., & Magnuson, J. S. Multi-talker processing costs in monitoring reflect task demands, not normalization. Psychonomic Society, Virtual Conference, November 2020.