At Beacon, I work on neuroscience tool development, data science, and software engineering.
Academic work
In my academic work, I was interested in how the brain manages to efficiently allocate representational resources in a world where the statistics of sensory features changes from situation to situation. I’m particularly interested in how the structure in this sensory non-stationarity makes it possible to adapt to such changes more efficiently. Speech perception serves as an excellent model organism, because the statistics of the speech signal depend both on what is being said and who is saying it, both of which introduce highly structured variability that listeners are sensitive to.
My work aims to develop explicit, computational models of perception and adaptation, with a particular emphasis on speech. I think that good theories and models draw on insights from—and try to make connections between—neuroscience, behavioral data, and broader computational-level cognitive modeling.
Topics that I’ve worked on or am interested in include: cognitive neuroscience of learning and perception, perceptual category learning, phonetic adaptation/recalibration, acquisition of phonetic categories, and cue combination and complex acoustic feature extraction. I’m also particularly interested in increasing awareness and appreciation of Bayesian methods for modeling (although I don’t consider myself a capital-B-Bayesian) and—especially—data analysis.
Papers
- Luthra, S., Correia, J. M., Kleinschmidt, D. F., Mesite, L. & Myers, E. B. (in press). Lexical information guides retuning of neural patterns in perceptual learning for speech. Journal of Cognitive Neuroscience. [preprint]
- Wu*, M., Kleinschmidt*, D. F., Emberson, L. L., Doko, D., Edelman, S., Jacobs, R. & Raizada, R. D. S. (in press). Cortical transformation of stimulus-space in order to linearize a linearly inseparable task. Journal of Cognitive Neuroscience. [preprint]
- Kleinschmidt, D. F. (2019). Structure in talker variability: How much is there and how much can it help? Language, Cognition and Neuroscience, 34 (1), 43-68. DOI:10.1080/23273798.2018.1500698 [preprint] [osf]
- Kleinschmidt, D. F., Weatherholtz, K. & Jaeger, T. F. (2018). Sociolinguistic perception as inference under uncertainty. Topics in Cognitive Science, 10 (4), 818-834. DOI:10.1111/tops.12331
- Pajak, B., Fine, A. B., Kleinschmidt, D. F. & Jaeger, T. F. (2016). Learning additional languages as hierarchical probabilistic inference: Insights from first language processing. Language Learning, 66 (4), 900-944. DOI:10.1111/lang.12168
- Kleinschmidt, D. F. & Jaeger, T. F. (2016). Re-examining selective adaptation: Fatiguing feature detectors, or distributional learning? Psychonomic Bulletin & Review, 23 (3), 678-691. DOI:10.3758/s13423-015-0943-z
- Kleinschmidt, D. F. & Jaeger, T. F. (2015). Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel. Psychological Review, 122 (2). DOI:10.1037/a0038695
- Salverda, A. P., Kleinschmidt, D. F. & Tanenhaus, M. K. (2014). Immediate effects of anticipatory coarticulation in spoken-word recognition. Journal of Memory and Language, 71 (1), 145-163. DOI:10.1016/j.jml.2013.11.002
- Zaki, S. R. & Kleinschmidt, D. F. (2014). Procedural memory effects in categorization: Evidence for multiple systems or task complexity? Memory & cognition, 42 (3), 508-24. DOI:10.3758/s13421-013-0375-9
- Croft, W., Bhattacharya, T., Kleinschmidt, D. F., Smith, D. E. & Jaeger, T. F. (2011). Greenbergian universals, diachrony, and statistical analyses. Linguistic Typology, 15 (2), 433-453. DOI:10.1515/LITY.2011.029
Submitted and in prep
- Kleinschmidt, D. F. (submitted). What constrains distributional learning in adults? [preprint] [osf]
- Kleinschmidt, D. F., Raizada, R. & Jaeger, T. F. (in preparation). Neural mechanisms of rapid adaptation to unfamiliar talkers via distributional learning.
Conference proceedings
- Kleinschmidt, D. F. & Hemmer, P. (2019). A Bayesian model of memory in a multi-context environment. Proceedings of the 41st Annual Conference of the Cognitive Science Society. [osf] [pdf]
- Kleinschmidt, D. F. (2018). Learning distributions as they come: Particle filter models for online distributional learning of phonetic categories. Proceedings of the 40th Annual Conference of the Cognitive Science Society. [osf] [pdf]
- Kleinschmidt, D. F. & Jaeger, T. F. (2016). What do you expect from an unfamiliar talker? Proceedings of the 38th Annual Meeting of the Cognitive Science Society. [github] [pdf]
- Kleinschmidt, D. F., Raizada, R. & Jaeger, T. F. (2015). Supervised and unsupervised learning in phonetic adaptation. Proceedings of the 37th Annual Conference of the Cognitive Science Society. [github] [pdf]
- Kleinschmidt, D. F. & Jaeger, T. F. (2012). A continuum of phonetic adaptation: Evaluating an incremental belief-updating model of recalibration and selective adaptation. Proceedings of the 34th Annual Conference of the Cognitive Science Society, 605-10. [pdf]
- Kleinschmidt, D. F., Fine, A. B. & Jaeger, T. F. (2012). A belief-updating model of adaptation and cue combination in syntactic comprehension. Proceedings of the 34th Annual Conference of the Cognitive Science Society, 599-604. [pdf]
- Kleinschmidt, D. F. & Jaeger, T. F. (2011). A Bayesian belief updating model of phonetic recalibration and selective adaptation. Proceedings of the 2nd ACL Workshop on Cognitive Modeling and Computational Linguistics (CMCL). [pdf]
