Micha Engeser

Micha Engeser

@michaengesee.bsky.social

PhD Student at Kaiser Lab, JLU Gießen, Germany @dkaiserlab.bsky.social

108 followers206 following13 posts

Micha Engeser reposted

Kabir Arora
@arora-borealis.bsky.social

It's already cool that we can use neural responses to *invisible* flickers of different frequencies to track covert attention (with RIFT), but in a new preprint we show you can even track attention between tags at the same frequency (using phase): www.biorxiv.org/content/10.6... (1/4)

biorxiv.orgCovert attentional tracking with invisible phase taggingAttention forms a fundamental component of visual processing. The locus and spread of covert attention reveals how specific locations or features in the environment are preferentially processed, howev...

Micha Engeser reposted

Ingmar de Vries
@ingmardevries.bsky.social

🔭JOB ALERT🔬 2 postdoc positions in my lab at the University of Trento, IT, as part of an Italian FIS3 starting grant. TOPIC: Investigating predictive neural representations in naturalistic settings using MEG-based dynamic RSA. See thread and CALL below for details, or write me for more info!

lavoraconnoi.unitn.itCIMeC - Center for Mind/Brain Sciences: Call for applications for the award of no. 2 research contracts pursuant to art. 22 L. 240/2010, decree n. 195/2026, dott. Ingmar De Vries | Lavora con noi

Micha Engeser reposted

Rico Stecher
@ricostecher.bsky.social

The final preprint of my PhD is out! We present a neuro-computational approach that can approximate mental images and allows us to assess their qualities without introspection. We evaluated this approach on a large-scale EEG data set (43,200 trials). (1/n) www.biorxiv.org/content/10.6...

biorxiv.orgA neuro-computational approximation of the qualities of mental imagesMental images are challenging to study, given that our conscious experience is notoriously hard to access. The currently prevalent introspective methods are inherently subjective and can thus only pro...

Micha Engeser reposted

Susan Ajith
@suzibot.bsky.social

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.orgCortical alpha rhythms predictively track occluded motion trajectoriesObjects 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...

Micha Engeser reposted

Lu-Chun Yeh
@luchunyeh.bsky.social

🚨Out now in J Neurosci! 🧠 Using fMRI and EEG, we mapped where and when contextual knowledge of real-world objects emerges within the visual system - even when those objects are viewed in isolation. @dkaiserlab.bsky.social @peelen.bsky.social www.jneurosci.org/content/46/1...

jneurosci.orgSpatiotemporal Representations of Contextual Associations for Real-World ObjectsIn the real world, objects always appear in context. Many objects are reliably associated with a certain scene context (e.g., pots appear in kitchens) and other objects that appear in the same context...
Micha Engeser
@michaengesee.bsky.social

Check out our new paper! We investigated how the temporal context of natural scene videos (abrupt onsets vs. gradual transitions from a previous scene) shapes EEG decoding time courses, revealing how context influences the dynamics of visual processing.

Ilker Duymaz@ilkrdymz.bsky.social

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.orgAbrupt scene onsets and gradually emerging scene information produce distinct EEG decoding dynamics | Journal of Neurophysiology | American Physiological SocietyMultivariate 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.

Micha Engeser reposted

Kaiser Lab
@dkaiserlab.bsky.social

New paper alert 🚨👀 Check out @ilkrdymz.bsky.social's paper: "Abrupt scene onsets and gradually emerging scene information produce distinct EEG decoding dynamics".

Ilker Duymaz@ilkrdymz.bsky.social

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.orgAbrupt scene onsets and gradually emerging scene information produce distinct EEG decoding dynamics | Journal of Neurophysiology | American Physiological SocietyMultivariate 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.
Micha Engeser
@michaengesee.bsky.social

What is the functional role of conscious experience? That question led me to approach Cyriel Pennartz about writing my literature thesis with him for my master's at the University of Amsterdam. A few years later, that project has evolved into a paper, now published in Consciousness and Cognition.

sciencedirect.comCould we perceive the world differently than we do? Neuroscience-based emergentism and the biological function of consciousnessWhether consciousness serves a biological and psychological function is a topic of intense interest. We reexamine this issue by first focussing on an …
Micha Engeser
@michaengesee.bsky.social

If you're at #VSS2026 come check out our posters and talks! On Saturday 6:45 pm I'll present my talk on individual differences in scene perception.

Kaiser Lab@dkaiserlab.bsky.social

Kaiser Lab is at @vssmtg.bsky.social with 6 posters and 3 talks! 👁️🏖️☀️ Come check out our works!

Micha Engeser reposted

Kaiser Lab
@dkaiserlab.bsky.social

📢 Workshop announcement. We are super excited to announce the workshop Perceptual Inferences, from philosophy to neuroscience, organized by Alexander Schütz and Daniel Kaiser. 📍 Rauischholzhausen Castle, near Marburg, Germany 🗓️ June 8 to 10, 2026. 1/4

Micha Engeser reposted

Borjan (Boki) Milinković
@neuromorphicboki.bsky.social

🎊📜 NEW PAPER 📜🎊 Can we seriously build synthetic consciousness? And if so, where do we start? I’m super excited to present recent publication in Neuroscience & Biobehavioral Reviews where @jaanaru.bsky.social and I confront this challenge head on. 1/n

sciencedirect.comOn biological and artificial consciousness: A case for biological computationalismThe rapid advances in the capabilities of Large Language Models (LLMs) have galvanised public and scientific debates over whether artificial systems m…
View full thread
View full thread
Micha Engeser
@michaengesee.bsky.social

We then used the inter-subject similarities in internal models to predict inter-subject similarities in perceptual task performances and neural responses to a fully independent set of natural scenes.

View full thread
Micha Engeser
@michaengesee.bsky.social

To characterize these internal models, participants drew what they considered the most typical version of a given scene category. Using a combination of deep learning tools, we quantified inter-subject similarities in these drawings.

Micha Engeser
@michaengesee.bsky.social

In this paper we present flexible methods for participants to express their expectations about natural scenes.

Kaiser Lab@dkaiserlab.bsky.social

From line drawings to scene perception — our new review argues for moving beyond experimenter-driven manipulations toward participant-driven approaches to reveal what’s in our internal models of the visual world. 👁️✍️🛋 royalsocietypublishing.org/doi/10.1098/...

royalsocietypublishing.orgCharacterizing internal models of the visual environment | Proceedings of the Royal Society B: Biological SciencesDespite the complexity of real-world environments, natural vision is seamlessly efficient. To explain this efficiency, researchers often use predictive processing frameworks, in which perceptual effic...

You are up to date.