Rosanne Rademaker

Attractor dynamics of working memory explain a concurrent evolution of stimulus-specific and decision-consistent biases in visual estimation People exhibit biases when perceiving features of the world, shaped by both external stimuli and prior decisions. By tracking behavioral, neural, and mechanistic markers of stimulus- and decision-rela...

Excited to share that our paper is now out in Neuron @cp-neuron.bsky.social (dlvr.it/TM9zJ8).

Our perception isn't a perfect mirror of the world. It's often biased by our expectations and beliefs. How do these biases unfold over time, and what shapes their trajectory? A summary thread. (1/13)

Brain Surfaces of 70 primate species

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To predict the behaviour of a primate, would you rather base your guess on a closely related species or one with a similar brain shape? We looked at brains & behaviours of 70 species, you’ll be surprised!

🧵Thread on our new preprint with @r3rt0.bsky.social , doi.org/10.1101/2025...

Are you looking for work in a research group that is supported by an ERC grant?

Remember there are new positions appearing almost every day on #Euraxess's website, with 140 currently open.

You could find the right fit for you! 👉 buff.ly/aVAu7vm

#ChooseEurope

Assembly-based computations through contextual dendritic gating of plasticity Neuronal assemblies — groups of strongly connected neurons — are considered the basic building blocks of perception and memory in the brain by encoding representations of specific concepts. Despite re...

Excited to share our new preprint in which we propose a biologically plausible mechanistic solution to the problem of how the brain can flexibly learn and store new representations without forgetting previously learned information.

www.biorxiv.org/content/10.1...

Detailed 🧵 below 👇 1/12

What do the music of Miles Davis, the paintings of Edward Hopper, and the equations of Isaac Newton have in common? They all rely – in different ways – on absences.

Introducing our new favorite stimulus. A few minutes are enough to map the visual preferences of thousands of neurons.

Mapping the visual cortex with Zebra noise and wavelets
www.biorxiv.org/content/10.1...

By Sophie Skriabine and Max Shinn, with Samuel Picard and
@kenneth-harris.bsky.social

After five years of confused staring at Greek letters, it is my absolute pleasure to finally share our (with @smfleming.bsky.social) computational model of mental imagery and reality monitoring: Perceptual Reality Monitoring as Higher-Order inference on Sensory Precision ✨
osf.io/preprints/ps...

Everything you always wanted to know about color, but were afraid to ask.

A superb, highly didactic video, now in English!

The University of Sydney is recruiting senior and mid-career academics "within the top 5–10% of academics in their field, with a strong track record and availability to relocate to Australia within 6-months".
If you're a psychologist/neuroscientist and are interested, get in touch with me soon.

📣 Ever considered applying for a ERC Starting Grant? The 2026 Call for proposals is now open!

Application portal 👉 lnkd.in/dcsPAwqJ
Information for Applicants 👉 lnkd.in/dsE6B8eE

Deadline to apply for #ERCStG is 14 October 2025.

Lots of room for growth at @uwaterloo.ca. 🇨🇦 And were hiring 6 new faculty in #optometry and #visionscience. Spread the news. Contact me if you want to learn more.

share.google/8HXvp3LbwLPw...

Structure uncovered: understanding temporal variability in perceptual decision-making Studies of perceptual decision-making typically present the same stimulus repeatedly over the course of an experimental session but ignore the order o…

Sweet, sweet review by the awesome @anne-urai.bsky.social on the beautiful mess that is behaviour.
Also, best abstract ending I've seen in a long time:
'There is probably no such thing as stable behavior.'
Check it out!
www.sciencedirect.com/science/arti...

A direct neural signature of serial dependence in working memory Magnetoencephalographic recordings revealed a direct neural signature of serial dependence, that is, an attractive bias of current toward previous representations, which emerged at late, post-encoding...

Our paper on the neural basis of serial dependence is finally published!

In @elife.bsky.social: A direct neural signature of serial dependence in working memory doi.org/10.7554/eLif...

Oh, and check out the commentary by Yang&Kiyonaga too!
elifesciences.org/articles/101...

Bonus re-analysis of a whole bunch of EEG data: Spatial position decoding is SO MUCH better along the horizontal compared to the vertical meridian! N/N

As with the discourse over the IEM, we hope this work will make you think deeply about models, about what they can do, and their limitations. And we encourage everyone to think deeply about data, and what might explain it. After all, we all may get it wrong sometimes! 14/N

Model mimicry limits conclusions about neural tuning and can mistakenly imply unlikely priors Nature Communications - Model mimicry limits conclusions about neural tuning and can mistakenly imply unlikely priors

It’s been cool to connect visual field anisotropies & stimulus driven effects, and potentially explain some data that piqued our interest. But we do caution against “generative forward modeling”. There’s more in the paper (rdcu.be/eugi0), but the take-home is hopeful: 13/N

We replicated the horizontal-vertical decoding differences for centrally presented gratings using independent data. But look! This decoding difference is entirely absent for laterally presented gratings! 12/N

Why does this matter? Well, for centrally presented gratings there is more energy in parts of the visual field with higher sensitivity. This could give a real boost to full-field horizontal gratings. 11/N

And remember vignetting? Higher orientation energy at grating-edges aligned with an orientation contributes to decoding (Roth et al., 2018). Visual field anisotropies and vignetting can interact. 10/N

elifesciences.org/articles/37241

We wondered that too! First, remember visual field anisotropies? Human performance is better at, and double the V1 surface area is dedicated to, the horizontal compared to the vertical meridian (Himmelberg et al., TINS, 2023). 9/N

Those are all pretty similar! And that’s exactly the point: “generative forward modeling” is susceptible to model mimicry, and can deliver many possible solutions of unknowable correctness. But what about the actual EEG data, and those big differences between vertical & horizontal? 8/N

But there’s an inverse problem here: The underlying neural tuning cannot be inferred due to model mimicry: Many possible “sensory coding schemes” can generate very similar outcomes. Look what happens when we consider a slightly larger parameter space… 7/N

This is tricky. Via “generative forward modeling” the tuning functions are unevenly distributed (i.e. uneven preference) or are made to have uneven widths, and from these 2 models EEG data is simulated and then decoded. Finally, simulated data are compared to the real data. 6/N

This sensory coding scheme embeds a prior (from natural image statistics) via redistribution of orientation tuning functions, with more functions around horizontal vs. vertical – not predicted by previous proposals! But… How to recover sensory tuning from EEG data? 5/N

What’s in the paper? EEG data reveal stark differences in decoding performance for vertical (bad) compared to horizontal (good) orientations. Wow, but why? The authors say it’s because of a unique sensory coding scheme they were able to recover. 4/N

Neural tuning instantiates prior expectations in the human visual system - Nature Communications Perception is often modelled using a Bayesian framework, but its neural instantiation remains unclear. Using a novel modelling approach, the authors reveal an empirical encoding scheme for visual orie...

Fast forward to a recent paper by Harrison, Bays, and Rideaux (2023) in Nature Communications, which uses electroencephalography (EEG) to infer neural tuning properties from human visual cortex. A lot of data, gorgeous figures, and a real head-scratcher. 3/N

First, throwback to 2018, when scientific discourse broke loose over the so-called “Inverted Encoding Model” (IEM). In back-and-forth papers, the strengths & limitations of IEM were outlined. Some issues maybe obvious in hindsight, but I admit I learned a ton. 2/N
www.eneuro.org/content/eneu...

Model mimicry limits conclusions about neural tuning and can mistakenly imply unlikely priors Nature Communications - Model mimicry limits conclusions about neural tuning and can mistakenly imply unlikely priors

Who doesn't like a good model of the brain? Yet, from simple regression to neural nets, some limitations keep popping up (e.g., overfitting) @mjwolff.bsky.social & I saw some cool but puzzling data, ran a quick analysis & found one such limitation: model mimicry. Now in #naturecommunications &🧵below

PhD position — Rademaker lab

Curious about the visual human brain, a vibrant and collaborative lab, and pursuing a PhD in the heart of Europe? My lab is recruiting for a 3-year PhD position. More details: www.rademakerlab.com/job-add

This will be my first ever time attending BACN, looking forward to it 😊