Dan Feuerriegel

UCL – University College London UCL is consistently ranked as one of the top ten universities in the world (QS World University Rankings 2010-2022) and is No.2 in the UK for research power (Research Excellence Framework 2021).

We are recruiting a new PI at the FIL @imagingneuroucl.bsky.social, Associate or Full Professor. This is an amazing place to do cognitive neuroscience, in the heart of London. If you or someone you know might be interested, please pass it on. #neuroskyence

www.ucl.ac.uk/work-at-ucl/...

Applications are invited for a Postdoctoral Position (80%) at the Cognitive Psychology Unit, Department of Psychology, University of Zurich. The ideal start date would be January 1st, 2026 but could be somewhat earlier or later. The position is for initially 3 years, with possible renewal for another 1.5 years. The successful applicant will work with the head of the Cognitive Psychology Unit, Prof. Dr. Klaus Oberauer, and the Cognitive Psychology team. The post involves research and teaching (one course per semester). Research at the Zurich Cognitive Psychology Unit focuses on capacity limits of cognition, in particular working memory, long-term memory, and attention, which we investigate with experimental, individual-differences and aging studies and computational modeling. For further information see: https://www.psychology.uzh.ch/en/areas/nec/allgpsy.html We offer: - An attractive research environment with close collaboration within the team - Excellent lab infrastructure - Salary according to Cantonal regulations We expect: - a PhD in psychology or a related discipline - experience with experimental research - excellent method knowledge and skills - very good command of English - interest in pursuing an academic career in cognitive psychology In addition, it would be desirable if you had: - experience with publishing in international journals - programming skills - experience with mathematical / computational modeling Please send applications including your CV and motivation letter no later than September 10, 2025 electronically as a single PDF to: Michaela Oestmann, michaela.oestmann@psychologie.uzh.ch For questions about the position please contact Klaus Oberauer, k.oberauer@psychologie.uzh.ch

🚨 PostDoc Opening 🚨 The lab of Klaus Oberauer is looking for a new postdoc, starting end of this/beginning next year. Research focus is #cognition, #workingmemory, #methods and #computationalmodeling or anything in that direction. I cannot highlight ENOUGH how great it is to work in this lab 🥰🤓

Interested in a cognitive neuroscience #postdoc down under?

Come work with me at @marcsinstitute.bsky.social on an ARC Linkage Project aimed at understanding how people perceive objects in the Powerhouse Museum's digitised collection. Applications closing 13 August! 📅 seek.com.au/job/86079413

Very excited to see this out - @jie-sun.bsky.social's latest tour-de-force in careful EEG measurement, joint neural-behavioural modelling techniques, and new discoveries.

If you measure the brain in recognition memory tasks, this one is for you!

@adamosth.bsky.social @psychunimelb.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...

The timecourse of inter-object contextual facilitation High-level vision is frequently studied at the level of either individual objects or whole scenes. An intermediate level of visual organisation that h…

New paper with @peelen.bsky.social showing that objects in familiar configurations (e.g., 🍽️) facilitate one another by 200 ms of visual processing time. Out now in Cortex! 🧠 doi.org/10.1016/j.co...

Indeed, this type of work paves the way for interesting new ideas about how predictions (in the myriad ways they are defined) relate to neural activity across the brain.

Cooperative thalamocortical circuit mechanism for sensory prediction errors - Nature Experiments in mice show that a cortico-thalamic circuit generates prediction-error signals in primary visual cortex that amplify visual input that deviates from animals’ expectations.

Pairs well with www.nature.com/articles/s41...

“Our results indicate that individual V1 neurons do not signal how the actual visual input deviates from the animal’s predictions, as postulated within the predictive coding framework”

Thrilling to see a major theory get tested!

Sensory responses of visual cortical neurons are not prediction errors Predictive coding is theorized to be a ubiquitous cortical process to explain sensory responses. It asserts that the brain continuously predicts sensory information and imposes those predictions on lo...

1/3) This may be a very important paper, it suggests that there are no prediction error encoding neurons in sensory areas of cortex:

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

I personally am a big fan of the idea that cortical regions (allo and neo) are doing sequence prediction.

But...

🧠📈 🧪

Also see Lauren's work on the neural correlates of voluntary actions (Poster session 1, Mon 12:30pm, P005)

And Jasmin's findings on loneliness and interpersonal regulation (Poster session 2, Mon 4:30pm, P073)

Very excited to be at @assc28.bsky.social with members of my lab! Make sure to check out:

My talk on predictive cueing featuring some surprising results (Tues 9:10am, Experimental Theatre Hall)

Morgan's talk on visual pre-activation (Wed 9:50am, Experimental Theatre Hall)

#assc28

Impressive behavioral + modelling + eeg work, led by @lucvermeylen.bsky.social , relating time-on-task changes in comp parameters to changes in metacognitive reports!

1/3 Check out our new commentary bsky.app/profile/imag....

Hey ECRs at @expsyanz.bsky.social this week - if you're interested in object perception and/or neural decoding, come find me for a chat about an upcoming postdoc spot at the @marcsinstitute.bsky.social 👀

Thrilled to get my hands on the first copies of my book!

“A Trick of the Mind: How the Brain Invents Your Reality” in bookshops soon - from 26th June 🧠 🍎

(Though Ralphie more interested in the packaging….)

www.penguin.co.uk/books/451515...

#booksky #neuroskyence

Expectation effects based on newly learnt object-scene associations are modulated by spatial frequency Objects typically appear within rich visual scenes. Some models of visual system function propose that scene information is extracted from low-spatial frequency components and rapidly propagates throu...

New preprint from the lab!

We tested for effects of learning scene-object pairings on distributed patterns of EEG signals. We report clear effects on ERPs but not object decoding performance.

Led by Morgan Kikkawa w/Marta Garrido

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

Neurally-informed modelling unravels a single evidence accumulation process for choices and subsequent confidence reports https://www.biorxiv.org/content/10.1101/2025.06.05.658071v1

I am so excited to share that our paper 'A neural basis for distinguishing imagination from reality' is now published in @cp-neuron.bsky.social! 🧠✨ See thread below! doi.org/10.1016/j.ne...

Finally the Version of Record of our paper "Criterion placement threatens the construct validity of neural measures of consciousness" is out in @elife.bsky.social as *fundamental* and *compelling*!
doi.org/10.7554/eLif....

🏁 Big news from #Cogitate!🏁

We’re proud to deliver the first part of our promised open multimodal dataset:
iEEG data from 38 patients across 3 centers, collected under a unified protocol.

📄 Paper: doi.org/10.1038/s415...
📀 Data: www.arc-cogitate.com/data-release
💻 Code: github.com/Cogitate-con...

Have recent changes led to uncertainty in your future scientific career?

Wonder it's like in Australia?

Good news!

Monash's is seeking to hire talented EMCRs from other countries.

Come join a wonderful community of brain mappers & modellers!

www.monash.edu/research/eme...

These studies were motivated by the realisation that, in the visual system, we don't actually have solid evidence for expectation-related effects such as expectation suppression.

Reviewed here: doi.org/10.1016/j.ne...

This builds on (and replicates) our prior study that presented face stimuli and also did not identify predictive cueing effects.

doi.org/10.1016/j.ne...

Our findings are relevant to the evidence base we use to build and develop predictive processing models.

<em>Psychophysiology</em> | SPR Journal | Wiley Online Library Stimulus expectations are widely assumed to modulate the responsiveness of neurons in the visual system. However, there is currently a lack of clear evidence from electrophysiological studies to supp....

New paper from the lab!!

We add to the (lack of) evidence for expectation effects in carefully-controlled predictive cueing designs.

doi.org/10.1111/psyp...

Led by Carla den Ouden and Máire Kashyap, collaborating with Morgan Kikkawa

Wonderful to see this published!! Huge congrats and hope things are well in the States

New paper out in @plosbiology.org w/ Charlie, @phil-johnson.bsky.social, Ella, and Hinze 🎉

We track moving stimuli via EEG, find evidence that motion is extrapolated across distinct stages of processing + show how this effect may emerge from a simple synaptic learning rule!

tinyurl.com/2szh6w5c

Hi #neuroskyence hive mind - can you recommend any really nice examples of OPM-MEG applied to answer systems/cognitive neuroscience questions? Preferably in designs using visual or auditory stimuli, but I'm grateful for any recommendations. Please feel free to promote your own work.

Thanks so much!

A schematic of our method. On the left are shown Bayesian inference (visualized using Bayes’ rule and a portrait of the Reverend Bayes) and neural networks (visualized as a weight matrix). Then, an arrow labeled “meta-learning” combines Bayesian inference and neural networks into a “prior-trained neural network”, described as a neural network that has the priors of a Bayesian model – visualized as the same portrait of Reverend Bayes but made out of numbers. Finally, an arrow labeled “learning” goes from the prior-trained neural network to two examples of what it can learn: formal languages (visualized with a finite-state automaton) and aspects of English syntax (visualized with a parse tree for the sentence “colorless green ideas sleep furiously”).

🤖🧠 Paper out in Nature Communications! 🧠🤖

Bayesian models can learn rapidly. Neural networks can handle messy, naturalistic data. How can we combine these strengths?

Our answer: Use meta-learning to distill Bayesian priors into a neural network!

www.nature.com/articles/s41...

1/n

Communication of perceptual predictions from the hippocampus to the deep layers of the parahippocampal cortex High-resolution neuroimaging reveals stimulus-specific predictions sent from hippocampus to the neocortex during perception.

Our study using layer fMRI to study the direction of communication between the hippocampus and cortex during perceptual predictions is finally out in Science Advances! Predicted-but-omitted shapes are represented in CA2/3 and correlate specifically with deep layers of PHC, suggesting feedback. 🧠🟦

(Not that it exists, but that it could be localised)