Cogan Lab

Cogan Lab at Duke University: Investigating speech, language, and cognition using invasive neural human electrophysiology

[Cogan Lab @CoganLab]

Thank you to the authors @UTHealth @TandonLab for your work!
cc: @TessyMThomas, Jinglong Li, @GregoryHickok, @xaqlab & @DrNTandon

[Cogan Lab @CoganLab]

Last week, Zac Spalding (@zspald11, 4th year PhD student, @DukeEngineering) presented Aditya Singh and colleagues’ 2025 paper on multi-regional, multi-subject neural speech decoding using from SEEG electrodes.

[Cogan Lab @CoganLab]

❔3️⃣: In the multi-subject REO analysis showing robustness to loss of SMC electrodes (Fig. 4b), did you investigate which non-SMC regions contributed the most in the absence of SMC electrodes? Could this be driven by auditory feedback signals when SMC electrodes are removed?

[Cogan Lab @CoganLab]

❔3️⃣: How does individual variability in structural connectivity account for the variance in
early frontal speech representations?

Thank you to the authors @UCSF, @NeurosurgUCS, and @ChangLabUcsf for your
work, and we look forward to following its progress!

[Cogan Lab @CoganLab]

Last week, Baishen Liang (postdoctoral associate) led a discussion on a recent ieeg
speech paper from the Chang Lab by Patrick W. Hullett and colleagues on the frontal
and temporal parallel cortical speech processing pathways.

[Cogan Lab @CoganLab]

❔1️⃣: How does the prevalence of early-response electrodes differ between the frontal
lobe and the STG?
❔2️⃣: How do the phonological representation profiles of early-activating electrodes
diverge across these two regions

[Cogan Lab @CoganLab]

Thank you to the authors @PrincetonNeuro for your work, and we look forward to following more of it!
CC: @timbuschman, @tafazolisina

[Cogan Lab @CoganLab]

Last week, Jim Zhang (4th year PhD student, @DukeBrain) presented Sina Tafazoli’s paper on building compositional tasks with shared neural subspaces. This 📷 explores our thoughts (📷 & 📷) https://t.co/zDw06g5OxR

[Cogan Lab @CoganLab]

❔3⃣: Do tasks of differing complexity occupy differing capacity in working memory? Or are complex tasks abstracted away such that they occupy the same amount of working memory capacity as simple tasks?