Şahcan Özdemir

@sahcan.bsky.social

sahcanozdemir.github.io | PhD candidate @IfADo| cognitive neuroscience | working memory | attention| action

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...

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.

doi.org

Preprint alert! 🚀 Spontaneous eye blinks are often treated as EEG artefacts. We show they can serve as temporal markers of internal attention, aligning with moments of attentional focusing in working memory. Check it out:

Spontaneous eye blinks as temporal markers of internal attention

Eye blinks are among the largest physiological artefacts in electroencephalography and are typically removed from the recordings. Yet their timing may carry information about cognition. Here, we asked...

biorxiv.org

I am looking for a postdoctoral researcher to join my lab at Sabancı University in Istanbul for a 2-year TÜBİTAK-funded position, with possible extension. Interested candidates can email me with a CV and a brief note about their research interests. More details on gunselilab.com soon.

Memory, Attention, & Cognitive Control Lab | Istanbul | Sabanci Uni.

Memory, Attention, & Cognitive Control Lab at Sabancı University - Eren Günseli. We study how memory and attention interact, how humans control what to remember, what to forget, what to attend to, and...

gunselilab.com

#VSS2026 attendees make sure to stop by my poster today in the afternoon poster session in the Banyan Breezeway. I will present a series of experiments, in which we investigated attentional selection in long-term memory.

Brain and Cognition Lab@brognition.bsky.social · 3mo ago

The B&C Lab is excited to attend the @vssmtg.bsky.social. William, @sabomelinda.bsky.social, and @irenetxeberria.bsky.social will each present posters. Lab alumni @danielagresch.bsky.social and @jamalamal.bsky.social will also present projects conducted in collaboration with the B&C Lab. #VSS2026

Too bad to miss this year's VSS. So I guess I will do my poster session here sharing my latest preprint 😉. We developed a method inspired by reverse correlation to characterize the behavioral preference across the object space, and provided evidence on how object category shapes the object space.

In collaboration with @monicarosenb.bsky.social , we showed that individual diffs in LTM encoding is uniquely predicted by inter-electrode correlations even controlling for working memory abilities. This suggests that WM & LTM encoding are separate abilities coded by different neural signatures! 1/n

Imaging Neuroscience@imagingneurosci.bsky.social · 3mo ago

New paper in Imaging Neuroscience by Chong Zhao, Edward K. Vogel, and Monica D. Rosenberg: A unique neural signature of long-term memory encoding from EEG inter-electrode correlation doi.org/10.1162/IMAG...

☀️ New paper by @veerahelmisofia.bsky.social ☀️showing how robust the positive correlation between pupil size and visual detection is #psychology #cognition 👇

Veera Ruuskanen@veerahelmisofia.bsky.social · 3mo ago

How robust is the large-pupil advantage in visual detection? Turns out, very! Here we show with @cogsci.nl that the effect persists for different stimulus colors, eccentricities, and retinal adaptation-states ✨👀 Check it out in JEP:HPP: doi.org/10.1037/xhp0...

More evidence for the role of alpha/beta oscillations in top-down control. Sustained alpha oscillations serve attentional prioritization in working memory, not maintenance doi.org/10.1162/IMAG... #neuroscience

Sustained alpha oscillations serve attentional prioritization in working memory, not maintenance

Abstract. Recent theory on the neural basis of working memory (WM) has attributed an important role to “activity-silent” or -quiescent mechanisms, suggesting that sustained neural activity might not be essential in the retention of information. This idea has been challenged by reports of ongoing neural activity in the alpha band during WM maintenance, however. The precise role of these alpha oscillations is unclear: Do they reflect attentional prioritization of stored information, or do they serve as a general maintenance mechanism, for instance to periodically refresh synaptic traces? To address this, we designed a visual WM task involving two memory items, one of which was prioritized by being tested first for recall. The task included both short (1 second) and long (3 seconds) delay intervals between encoding and retrieval. The long delay condition allowed us to test whether the alpha-based decoding effects persist beyond the early delay period, thereby putting accounts that attribute alpha activity to generic maintenance processes to the test. Time-resolved decoding analyses revealed that both tested-first and tested-second items were initially decodable following stimulus presentation. However, only the tested-first item exhibited sustained decodability throughout the delay, particularly in the long delay condition, where it transitioned into a stable coding scheme. This prolonged representation was selectively supported by induced alpha power, which reliably tracked the prioritized tested-first item, but not the deprioritized tested-second item. Impulse-based decoding further confirmed this asymmetry, showing a selective increase in readout for the tested-second item only when it became immediately task relevant. Together, these findings suggest that sustained alpha-band activity primarily reflects attentional prioritization, rather than general memory maintenance. Unattended, deprioritized items appear to transition into an activity-quiescent state, consistent with models of synaptic storage in WM.

doi.org