David Watson

Paper: authors.elsevier.com/a/1mhTF_fKKq...

Code & data: osf.io/93dva

Correlations of region-level connectivity fingerprints. Fingerprints are strongly correlated between regions in the face and scene networks, but even more strongly correlated within regions.

We also compare connectivity fingerprints for individual regions in the face & scene networks. Fingerprints are similar but still distinct between regions in each network, indicating connectivity profiles track differences in functional specialisations between regions.

Correlations of connectivity fingerprints within and between subjects. Connectivity fingerprints are strongly correlated between subjects, but even more strongly correlated within subjects.

Connectivity fingerprints are highly similar across people, but also more similar within a person than between people, showing connectivity profiles of face & scene regions track individual differences.

Connectivity fingerprints of face and scene regions with the rest of the brain. Fingerprints are distinct between different regions, and consistent across data splits.

We measure whole-brain functional connectivity "fingerprints" of face & scene regions, representing patterns of connectivity with the rest of the brain, in multiple movie-watching and resting-state datasets. We compare these fingerprints across 2 independent splits of each dataset.

New paper in Neuropsychologia 🎉

Patterns of functional connectivity differentiate individuals and individual regions in face and scene selective networks

With @kiranoad.bsky.social @bartholomewquinn.bsky.social & Tim Andrews, @yorkpsychology.bsky.social

authors.elsevier.com/a/1mhTF_fKKq...

A thesis student is looking at parasocial attachment to generative AI in undergraduate and postgraduate students. The questionnaire will take about 10 minutes. Please pass the study link on to anyone who might be interested.

exp.psy.gla.ac.uk/project?para...

A 2D Gabor-wavelet baseline model out-performs a 3D surface model in scene-responsive cortex Author summary To gain a more complete picture of human visual processing, it is critical to understand the precise format of representations of naturalistic visual scenes. Recent work has approached ...

Excited that this work with @serences.bsky.social and @timbrady.bsky.social is now out! Our Gabor-wavelet model better predicted voxel responses in scene regions than 3D models. Does this mean that scene areas aren’t “for” processing 3D scene structure? NO, we argue. 1/
dx.plos.org/10.1371/jour...

We are looking at a frozen river with a sprinkling of snow dusted on top. You can make out prints of geese and ducks. To one side of the river is a large brick built Victorian building of five floors. A stone arched bridge with pillars spans the river in the distance.

Cold you say?

The River Foss next to the Hall is frozen!

#JNeurosci: Using fMRI, Han and Epstein explored how people integrate different kinds of views to form mental maps of places, revealing two sets of brain regions involved in integrating views of landmarks into existing mental maps of a virtual city.
https://doi.org/10.1523/JNEUROSCI.0187-25.2025

Conceptual Roadmap of the present study. To examine the relationship between the metabolic costs of visual processing and aesthetic pleasure, we used both computational and physiological measures to quantify metabolic costs during visual processing: 1. Model-derived estimates of metabolic costs based on the activation of a deep neural network; 2. Metabolic activity of human brains, specifically in the visual processing areas. We found that both measures were inversely related to aesthetic pleasure.

Energy efficiency drives evolution, and humans may have evolved pleasure-based signals to optimize actions. Does this extend to aesthetic pleasure?

Yes!

Strong evidence in silico and humans, out in PNAS Nexus:
tinyurl.com/3kbu8xw4

With Yikai Tang and Wil Cunningham.
@uoftpsychology.bsky.social

Memorability of visual stimuli and the role of processing efficiency Nature Reviews Psychology - Certain items are better remembered than others across individuals, a property known as memorability. In this Review, Bainbridge and colleagues detail memorability...

What makes visual stimuli memorable? Wilma Bainbridge, @keisukefukuda.bsky.social, Lore Goetschalckx, and I investigate the role of processing fluency for memorability in a new review paper in Nature Reviews Psychology. Check it out!

rdcu.be/eSyjz

Investigating individual-specific topographic organization has traditionally been a resource-intensive and time-consuming process. But what if we could map visual cortex organization in thousands of brains? Here we offer the community with a toolbox that can do just that! tinyurl.com/deepretinotopy

Still think this was one of the best power moves of all time

www.sciencedirect.com/science/arti...

Academics in Assyria in the 7th c BC complain that admin is preventing them from doing research and teaching

#JNeurosci: Lu et al. show that two brain regions—retrosplenial complex and superior parietal lobe—represent facing direction when people perform a naturalistic navigation task in a virtual-reality city. @russellepstein.bsky.social @gkaguirre.com @zhenganglu.bsky.social
vist.ly/4chuk

New paper in Imaging Neuroscience by Kirsten L. Peterson, Michael W. Cole, et al:

Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding

doi.org/10.1162/IMAG...

Distinct neural processing underlying visual face and object perception in dyslexia Developmental dyslexia is a disorder marked by difficulties in reading, spelling, and connecting sounds to written language. The high-level visual dys…

Excited to share a new article from @icevislab.bsky.social! In a sample of over 60 people, we found distinct neural differences underlying object and face recognition in dyslexia. These findings highlight crucial domain-general visual processes that may contribute to word reading challenges.

Confronting the connectivity crisis in human M/EEG research The cognitive neuroscience community using M/EEG has not converged on measures of task-related inter-regional brain connectivity that generalize acros…

In our Trends in Cogn Sci paper we point to the connectivity crisis in task-based human EEG/MEG research: many connectivity metrics, too little replication. Time for community-wide benchmarking to build robust, generalisable measures across labs & tasks. www.sciencedirect.com/science/arti...

"I feel full with shame": A qualitative perspective on gastric interoceptive sensibility “Am I hungry? Did I overeat at lunch?” Gastric interoception - the sensing, interpretation, and regulation of signals from the gastrointestinal system…

New qualitative paper! A foray into gastric interception. @lucysta02475610.bsky.social ran a LOT of focus groups, across groups with eating disorders, gastric disorders and neither, to understand how people experience the sensations from their GI system. www.sciencedirect.com/science/arti...

Our target discussion article out in Cognitive Neuroscience! It will be followed by peer commentary and our responses. If you would like to write a commentary, please reach out to the journal! 1/18 www.tandfonline.com/doi/full/10.... @cibaker.bsky.social @susanwardle.bsky.social

📣 New job alert! I'm looking for a 2-year research assistant for a project on word learning from childhood to adulthood. Come and join us in lovely York! Please RT 🙏 @yorkpsychology.bsky.social jobs.york.ac.uk/vacancy/rese...

While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.

Everyone is talking about this MIT research on how using ChatGPT academically affects the brain.

It's striking how much ongoing impact there is from overreliance on Gen AI.

It goes beyond spoon-feeding; LLMs appear to be dismantling the apparatus we need to use a spoon ourselves in future.

GitHub - spm/spm-python: The Python interface to SPM The Python interface to SPM. Contribute to spm/spm-python development by creating an account on GitHub.

Drumroll... The SPM team will announce that SPM is now fully accessible from Python! 🐍 Learn more about SPM-Python at the SPM roundtable event (Friday, 1pm) and poster number 1841 at #OHBM2025. Try the beta for yourself at github.com/spm/spm-python [2/7]

Representation of locomotive action affordances in human behavior, brains, and deep neural networks | PNAS To decide how to move around the world, we must determine which locomotive actions (e.g., walking, swimming, or climbing) are afforded by the immed...

In these tumultuous times, still happy to report a scientific achievement: our preprint on affordance perception was just published in PNAS!

www.pnas.org/doi/10.1073/...

Using behavior, fMRI and deep network analyses, we report two key findings. To recapitulate (preprint 🧵lost on other place):

Now out in Nature Human Behaviour @nathumbehav.nature.com : “End-to-end topographic networks as models of cortical map formation and human visual behaviour”. Please check our NHB link: www.nature.com/articles/s41...

Overview of the simulation strategy and analysis. a) Pial and white matter boundaries surfaces are extracted from anatomical MRI volumes. b) Intermediate equidistant surfaces are generated between the pial and white matter surfaces (labeled as superficial (S) and deep (D) respectively). c) Surfaces are downsampled together, maintaining vertex correspondence across layers. Dipole orientations are constrained using vectors linking corresponding vertices (link vectors). d) The thickness of cortical laminae varies across the cortical depth (70–72), which is evenly sampled by the equidistant source surface layers. e) Each colored line represents the model evidence (relative to the worst model, ΔF) over source layer models, for a signal simulated at a particular layer (the simulated layer is indicated by the line color). The source layer model with the maximal ΔF is indicated by “˄”. f) Result matrix summarizing ΔF across simulated source locations, with peak relative model evidence marked with “˄”. g) Error is calculated from the result matrix as the absolute distance in mm or layers from the simulated source (*) to the peak ΔF (˄). h) Bias is calculated as the relative position of a peak ΔF(˄) to a simulated source (*) in layers or mm.

🚨🚨🚨PREPRINT ALERT🚨🚨🚨
Neural dynamics across cortical layers are key to brain computations - but non-invasively, we’ve been limited to rough "deep vs. superficial" distinctions. What if we told you that it is possible to achieve full (TRUE!) laminar (I, II, III, IV, V, VI) precision with MEG!

I’m looking for a postdoc and RA for an ERC-funded project “SLEEPAWAY: Forgetting unwanted memories in sleep”. You’ll use MEG/EEG and fMRI to understand how the sleeping brain remembers and forgets. PLEASE REPOST 😊

Postdoc: tinyurl.com/vr5thp7s
RA: tinyurl.com/ycyzkatc

✊ Dundee Uni backs down from plans to cut 700 jobs after pressure from unions and the community.

Now they must take compulsory redundancies off the table.

Solidarity works.

Beyond binding: from modular to natural vision

Opinion by H. Steven Scholte & Edward de Haan
Open Access: tinyurl.com/4b5myz68

I am so happy—this is the first 1st author paper I have written, since the cancer diagnosis of my late wife seven years ago.
📑 PAPER ALERT: "Beyond binding: from modular to natural vision" in Trends in Cognitive Sciences (2025) sciencedirect.com/science/arti...