T. Florian Jaeger

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[T. Florian Jaeger @_hlplab_]

Faculty opening in computational neuroscience and cognition, including language, development, learning through the lifespan, etc.

[RochesterBrainCogSci @UoR_BrainCogSci]

We’re hiring a tenure-track Assistant Prof in Computational Neuroscience/Cognition at
@uor-braincogsci.bsky.social! Join a Simons-supported cluster across Math/Physics/Biology/BCS. Apply by Nov 1, 2025: https://t.co/fgs77TS7tX #ComputationalNeuroscience #Cognition #FacultyJobs

[T. Florian Jaeger @_hlplab_]

The OSF repo also contains a vignette for the R library, as well as a walk-through of the bootstrap analyses we applied. Hopefully this will make it easy for others to apply this model to their own data! https://t.co/OkptjsgxKc. Feedback welcome.

[T. Florian Jaeger @_hlplab_]

New R library STM https://t.co/WIyF55vj5o by Santiago Barreda implementing incremental vowel normalization & categorization (Nearey & Assmann's PSTM). New paper that describes & uses that STM to model vowel perception https://t.co/iZVaRJjWv1

[T. Florian Jaeger @_hlplab_]

(P)STM infers vowel & talker-specific normalization parameters from single observations, predicting listeners' perception far better than other common normalization models (incl. Lobanov or C-CuRE).

[T. Florian Jaeger @_hlplab_]

DL captures human speech perception both *qualitatively* & *quantitatively* (R2>96%) for over 400 combinations of exposure and test items. Yet, previous DL models fail to capture important limitations. Specifically, we find that DL seems to proceed by remixing prev experience 2/2

[T. Florian Jaeger @_hlplab_]

Very excited about this: putting distributional learning (DL) models of adaptive speech perception to a strong, informative test https://t.co/61EbFp5ME6 by Maryann Tan. We use Bayesian ideal observers & adapters to assess whether DL predicts rapid changes in speech perception 1/2

[T. Florian Jaeger @_hlplab_]

Great opportunity!

[T. Florian Jaeger @_hlplab_]

@mjpost I've asked myself surprisingly similar questions :)

[T. Florian Jaeger @_hlplab_]

@quarbby +1 Even (or especially?) as a PI, I found my time in industry super helpful. There is also lots to be learned about management, effective meeting structures, collaborative coding, etc. And for PIs, it can remind us what training mentees would actually benefit from!

[T. Florian Jaeger @_hlplab_]

@tallinzen That's exactly what I was thinking about when I saw your tweet. Eg it is hard to compare to countries with mostly public, federally or state-funded, research universities. I imagine, in those environments, the overhead is partly hidden in that gov-provided funding.

[#EEGManyLabs @eegmanylabs]

🚨Exciting news! We now have the first-ever complete #EEGManyLabs replication. This large-scale multi-site study revisits a key debate in EEG & reinforcement learning. A thread! 🧵👇
📄 Full paper: https://t.co/KlhpineNtI

[T. Florian Jaeger @_hlplab_]

@mjpost You are asking for too much. Turns out the inversion of an alarm clock is NP complete.

[Max Planck Society @maxplanckpress]

More than 40 percent of #postdocs leave academia. Those who landed a coveted faculty position were more likely to have had a highly cited paper, changed their research topic between their #PhD and postdoc, or moved abroad after receiving their doctorate. https://t.co/SkU2JVisuu @PNASNews -> https://t.co/GZKmXombKN #ScienceCareer

[RochesterBrainCogSci @UoR_BrainCogSci]

Please share: Postdoctoral position available in high-resolution retinal imaging and video eye-tracking at the University of Rochester.
https://t.co/PBKkfeOy7G
@aplabUR @CVSUoR @URNeuroscience @RochesterOptics @FlaumEye

[T. Florian Jaeger @_hlplab_]

Feedback welcome! & see @_wbushong_ 's thesis for more on this topic, and her very cool computational simulations--presenting a new effort to better understand what can be inferred from the types of data collected in studies on subcat infomaintenance during speech perception. /n

[T. Florian Jaeger @_hlplab_]

For how long is information about past speech input available in short-term memory? What evidence would inform this question? What does (not) follow from previous work on this question? Delighted to see this work by @klintonbicknell @_wbushong_ in JML https://t.co/0WMf0pw8aa

[T. Florian Jaeger @_hlplab_]

But Bayesian mixed-effect analyses also identify a previously undocumented tendency in all 4 datasets--unexpected under all existing accounts. We present initial simulations that suggests that a combination of attentional lapses & ideal integration might explain the data. /3

[T. Florian Jaeger @_hlplab_]

We show existing evidence is compatible with ideal maintenance & integration of uncertainty, and derive a stronger test of that hypothesis. 2 re-analysis & 2 new experiments find that the ideal observer's predictions fit listeners' behavior better than previous proposals /2

[T. Florian Jaeger @_hlplab_]

We revisit the classic work by Connine and colleagues, and show why its results are often misinterpreted. Using an ideal observer framework, we derive what would be expected if listeners maintained and integrated subcategorical information beyond word boundaries. /1