Micha Engeser

New preprint!

Why do people disagree about what looks beautiful, even when viewing the same stimulus?

We show that shared aesthetic experience is linked to shared gaze during naturalistic viewing: www.biorxiv.org/content/10.6...
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📢 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.
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🧨 Preprint alert
Is it easier to find a ball than a shoe? The answer lies in how variable we think these objects are in the real-world. www.biorxiv.org/content/10.6...

w/ the amazing @dkaiserlab.bsky.social & @luchunyeh.bsky.social 🦄

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On biological and artificial consciousness: A case for biological computationalism The rapid advances in the capabilities of Large Language Models (LLMs) have galvanised public and scientific debates over whether artificial systems m…

🎊📜 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.

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🚨 New preprints out!🚨
Excited to share two new preprints from my #MSCA project. With Daniel @dkaiserlab.bsky.social , Marius @peelen.bsky.social , and Belma Seferovic, we show how contextual associations shape real-world object representations and guide everyday visual task performance.
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These results provide a novel, mechanistic explanation of how perceptual and neural alignment across individuals is shaped by idiosyncrasies in prior experience.

Moreover, variations in participants' internal models predicted inter-subject correlations in BOLD time courses in the lateral occipital and lateral prefrontal cortices.

Individuals with more similar internal models showed more similar scene categorization performance and judged several scene properties (typicality, usability, and complexity) more similarly.

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.

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.

Here, we propose that idiosyncrasies in internal models—mental representations of what the world should look like—shape how individuals perceive and process natural scenes.

Every individual person perceives the visual world in their own unique way, yet we still know little about the origins of these individual differences.

I’m excited to share the first preprint from my PhD project!
Together with Daniel Kaiser (@dkaiserlab.bsky.social), we investigated how internal models shape inter-individual differences in the perception and neural processing of natural scenes.
Preprint: osf.io/preprints/ps...
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In this paper we present flexible methods for participants to express their expectations about natural scenes.