David Richter

If you are interested in pursuing a PhD in cognitive neuroscience, specially targeting conscious vs. unconscious processing, contact me. We are recruiting 🙏🧠 please RT

🚨 We’re hiring a postdoc!
Join the FLARE project @cimcyc.bsky.social to study sudden perceptual learning using fMRI, RSA, and DNNs.
🧠 2 years, fully funded, flexible start
More info 👉 gonzalezgarcia.github.io/postdoc/

DMs or emails welcome! Please share!

Exciting new preprint from the lab: “Adopting a human developmental visual diet yields robust, shape-based AI vision”. A most wonderful case where brain inspiration massively improved AI solutions.

Work with @zejinlu.bsky.social @sushrutthorat.bsky.social and Radek Cichy

arxiv.org/abs/2507.03168

Rapid Computation of High-Level Visual Surprise Predictive processing theories propose that the brain continuously generates expectations about incoming sensory information. Discrepancies between these predictions and actual inputs, sensory predict...

If you are interested in more details check out the preprint here:
www.biorxiv.org/content/10.1...

Taken together, our findings demonstrate that high-level visual predictions are rapidly integrated during perceptual inference. This suggests that the brain's predictive machinery is finely tuned to utilize expectations abstracted away from low-level sensory details, likely to facilitate perception.

We also found a curious decrease in ERP amplitude by semantic (word-based) surprise. Critically, we found no modulation by low-level visual surprise, even though stimuli were predictable all the way down to the pixel level.

Next, we turned to the key questions – when and what kind of surprise drive visually evoked responses? Results showed that neural responses around 200ms post-stimulus onset over parieto-occipital electrodes were selectively enhanced by high-level visual surprise.

First, as a sanity check, we used RSA to show that the DNN and other models of interest (a semantic word-based and a task model) well explained the EEG response irrespective of surprise.

We investigated these questions using EEG and a visual DNN. Participants viewed object images that were probabilistically predicted by preceding cues. We then quantified trial-by-trial surprise at low-levels (early DNN layers) and high-levels (late DNN layers) of visual feature abstraction.

Predictive processing holds that the brain continuously generates predictions about incoming sensory information. But at what level of abstraction does the brain predict – edges & contrasts or high-level textures & objects? And which stages of visual processing do such predictions modulate?

The what and when of visual surprise:
EEG shows that high-level visual surprise emerges rapidly and modulates neural responses ~200ms after stimulus onset.

New preprint with @paulapena.bsky.social and @mruz.bsky.social available here: doi.org/10.1101/2025...

Summary 🧵 below

Out now @cp-trendscognsci.bsky.social, w/ @akalt.bsky.social & @drmattdavis.bsky.social.

Are sensory sampling rhythms fixed by intrinsically-determined processes, or do they couple to external structure? Here we highlight the incompatibility between these accounts and propose a resolution [1/6]

Shared and diverging neural dynamics underlying false and veridical perception We often mistake visual noise for meaningful images, which sometimes appear as convincing as veridical percepts. This suggests considerable overlap between the mechanisms that underlie false and verid...

New MEG paper by @jhaarsma.bsky.social and @dotproduct.bsky.social‬! Same design and behavioural results as our 7T layer fMRI study (www.jneurosci.org/content/43/4...), but now shedding light on the temporal dynamics of the sensory signals underlying false percepts. #neuroskyence

PhD Studentship: Public Communication and Private Confidence at Birkbeck, University of London Find a PhD Studentship: Public Communication and Private Confidence on jobs.ac.uk, the top job board in higher education. Click to view more!

Looking for a PhD in #psychology or #cogneuro ? I’m recruiting a fully-funded @leverhulme.ac.uk PhD student to join my lab at @birkbeckpsychology.bsky.social. If you’re interested in metacognition, learning, social cognition and culture I’d love you to apply 🧠👇

www.jobs.ac.uk/job/DNJ330/p...

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...

Introducing All-TNNs: Topographic deep neural networks that exhibit ventral-stream-like feature tuning and a better match to human behaviour than the gold standard. Now out in Nature Human Behaviour. 👇

PhD position alert!!!
Together with @predictivebrain.bsky.social and Sonja Kotz we are looking for a PhD candidate to join a great project on the biological foundations of perceptual decisions vacancies.maastrichtuniversity.nl/job/Maastric...

Happy to answer any question you may have!

New preprint! 🚨→ Determinants of Visual Ambiguity Resolution. A new work with @ortiztudela.bsky.social @jvoeller.bsky.social @martinhebart.bsky.social and @gonzalezgarcia.bsky.social

We created ~2k images and collected ~100k responses to study visual ambiguity.

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

<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

my lab (lacns.github.io) at @mpi-nl.bsky.social and @dondersinst.bsky.social is recruiting for two PhD and two postdoctoral positions funded by an @erc.europa.eu Consolidator - come join us!

PhD: www.mpi.nl/career-educa...

Postdoc: www.mpi.nl/career-educa...

(please share widely)

Higher-level spatial prediction in natural vision across mouse visual cortex Theories of predictive processing propose that sensory systems constantly predict incoming signals, based on spatial and temporal context. However, evidence for prediction in sensory cortex largely co...

New preprint, w/ @predictivebrain.bsky.social !

we've found that visual cortex, even when just viewing natural scenes, predicts *higher-level* visual features

The aligns with developments in ML, but challenges some assumptions about early sensory cortex

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

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. 🧠🟦

Our @nature.com paper is out! Bringing together 2 major theories of consciousness - Integrated Information Theory (IIT) and Global Neuronal Workspace Theory (GNWT) - in an unprecedented collaboration. Here’s the story of how we advanced theory testing in neuroscience
www.nature.com/articles/s41...

Proactive distractor suppression in early visual cortex Implicitly learned spatial priors shape early visual cortex responses by suppressing potential distractions before stimuli appear, revealing a proactive mechanism that may enhance attentional control.

Combined, these findings show how the brain leverages implicitly learned spatial regularities to minimize distraction and proactively optimize sensory processing at locations likely to contain distracting inputs.

If you’re interested in more details, check out the paper: doi.org/10.7554/eLif...

Moreover, suppression was not limited to distractors, but also evident for target and neutral stimuli at (or near) the HPDL [panel A above]. This further supports the idea that suppression can arise predictively and before stimulus identification.

Surprisingly, suppression in EVC also extended to nearby neutral locations (NL-near), forming a broad suppression field around the HPDL. This could reflect an initial spatial bias in EVC that proactively prioritizes a broad area of the visual field, which is later refined in downstream areas.

Neural responses in EVC were proactively suppressed at the HPDL, even when no search display was presented. This suggests that distractor suppression can be driven by anticipation alone [panel B below].

We used fMRI to investigate how the brain may implement distractor suppression in early visual cortex (EVC). Participants performed a visual search task where a salient color distractor appeared more often at one location – the "High Probability Distractor Location" (HPDL).

Avoiding distraction by salient yet irrelevant stimuli is critical for many daily tasks (e.g., driving). One way to achieve this is by suppressing stimuli and locations that may be distracting such that they no longer compete for attention.

Proactive distractor suppression in early visual cortex!

In collaboration with Dirk van Moorselaar and @jthee.bsky.social we investigated how the brain suppresses distracting stimuli before they appear.

Paper here: doi.org/10.7554/eLif...
Summary 🧵 below