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Ilker Duymaz

@ilkrdymz.bsky.social
43 followers 84 following 3 posts

PhD candidate at Kaiser Lab, JLU Giessen

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Reposted by Ilker Duymaz
Susan Ajith @suzibot.bsky.social · 29/08/2026
How does the brain track an object even when it is occluded and in constrained, non-linear motion? 👁️🧠 In a new preprint, we use EEG+behavior to study how we mentally track motion (w/ @dkaiserlab.bsky.social, @luchunyeh.bsky.social, @kathadobs.bsky.social,...) www.biorxiv.org/content/10.6... 1/5 🧵
biorxiv.org
Cortical alpha rhythms predictively track occluded motion trajectories
Objects in the real world frequently move along complex, non-linear trajectories shaped by their environment. As they do, they often pass temporarily out of sight, occluded by the surrounding environm...
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Reposted by Ilker Duymaz
Kaiser Lab @dkaiserlab.bsky.social · 03/09/2026
🚨 Preprint alert! 🚨 Check out @ricostecher.bsky.social's new preprint: "A neuro-computational approximation of the qualities of mental images."
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Reposted by Ilker Duymaz
Kaiser Lab @dkaiserlab.bsky.social · 19/09/2026
🚨New paper alert: "Visual features explain dynamic aesthetic experiences across distinct movie content" 👀🧠
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Reposted by Ilker Duymaz
Kaiser Lab @dkaiserlab.bsky.social · 05/08/2026
🚨New paper alert: "Spatiotemporal Representations of Contextual Associations for Real-World Objects" 👀🧠
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Ilker Duymaz @ilkrdymz.bsky.social · 15/07/2026
New paper! We compared EEG decoding for scenes that appeared abruptly versus scenes emerging gradually during continuous visual input. Takeaway: Presentation context matters when interpreting EEG decoding time courses. With @michaengesee.bsky.social and Daniel Kaiser. doi.org/10.1152/jn.0...
doi.org
Abrupt scene onsets and gradually emerging scene information produce distinct EEG decoding dynamics | Journal of Neurophysiology | American Physiological Society
Multivariate analyses of magneto-/electroencephalography (M/EEG) data are typically performed on neural responses time-locked to discrete stimulus onsets. Such designs usually reveal high decoding performance during the initial transient response (0–500 ms), which subsequently drops to a lower, sustained level. Here, we examined time-resolved EEG decoding of natural scene processing when scenes gradually enter the visual field without a clear onset. We created video sequences in which one scene category (e.g., a beach) smoothly transitioned into another category (e.g., a forest) by blending two scenes into a single composite panorama and moving a square aperture across it. We compared EEG decoding for the first scenes within the transitions, which appeared with a sudden, artificial onset, to the second scenes, which emerged naturalistically as the videos progressed. For the first scenes, we observed robust category decoding from 60 ms after onset with a clear peak structure. For the second scene, category decoding was markedly weaker and showed no discernible peak structure. Realigning the appearance of category-diagnostic content for the second scene using deep neural networks did not enhance decoding or recover a peak structure. Furthermore, classifiers trained on the first scene generalized to the second, but with a broad, temporally diffuse pattern, instead of a diagonal pattern more consistent with a shared hierarchical processing timeline. Together, these findings demonstrate that time-resolved EEG decoding is sensitive to stimulus-presentation context. Accordingly, temporal decoding patterns obtained in conventional trial-based paradigms may not generalize unchanged to conditions involving gradual scene transitions and continuous visual input. NEW & NOTEWORTHY Using multivariate EEG decoding, we show that scene-category information follows different temporal profiles across two presentation regimes: abrupt scene appearance after a grayscale screen and gradual scene emergence during continuous visual input. Our findings demonstrate that time-resolved decoding is sensitive to stimulus-presentation context and should not be interpreted as an invariant signature of a fixed visual processing hierarchy.
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Ilker Duymaz @ilkrdymz.bsky.social · 20/03/2026
New publication 🥳 We show that some SSVEP components may reflect retinotopic variations in signal strength, rather than the periodic activity of feature-selective neural mechanisms. Thanks to @alvinlab.bsky.social and Naoki Kogo for their hard work! www.sciencedirect.com/science/arti...
sciencedirect.com
Origin of neural frequency responses: Sensory coding versus structural influences
Periodic changes in visual input elicit rhythmic patterns in EEG signals that manifest as narrowband frequency components. These components are typica…
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Ilker Duymaz @ilkrdymz.bsky.social · 30/10/2025
media.tenor.com
a black cat is looking at the camera with a caption that says when the brain soup wizard .
ALT: a black cat is looking at the camera with a caption that says when the brain soup wizard .
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