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Antonino Greco

@agreco.bsky.social

Computational Cognitive Scientist 🧠🤖 • NeuroAI, Predictive Coding, RL & Deep Learning, Complex Systems • Postdoc at @siegellab.bsky.social, @unituebingen.bsky.social • Husband & Dad 🎓 https://scholar.google.com/citations?hl=en&user=k5eR8_oAAAAJ

385 Posts2161 Follower370 Folgt
Francesco Poli@francescopoli.bsky.social · 2w ago

I've always been puzzled by how a sample size of 2 would be completely unacceptable for empirical work, but the norm for peer-reviews... Is 2 significantly better than 0? Has this been tested?

Alejandro Montenegro@aemonten.bsky.social · 2w ago

"Here we show, in a large post-publication peer review database, that research assessment is driven more by differences between evaluators than by difference in the evaluated research. " arxiv.org/abs/2607.09783

Giovanni Saraceno@giovannisaraceno.bsky.social · 2w ago

I am excited to share the new preprint with @antcalcagni.bsky.social “Towards a Geometric Characterization of Multiverse Analysis” arxiv.org/abs/2607.11345 #Statistics #OpenScience #SensitivityAnalysis 🧵👇

Towards a Geometric Characterization of Multiverse Analysis

Multiverse analysis makes explicit how empirical conclusions depend on alternative, defensible analytical specifications. Standard approaches usually generate the multiverse first and then summarize i...

arxiv.org

Serge Dumoulin@sergedumoulin.net · 2w ago

👁️🧠🧪 Vision takes time. But how does visual information flow through the human brain? Our new PLOS Computational Biology paper uses population receptive field (pRF) modelling to bring the spatial precision of fMRI to MEG, revealing the temporal dynamics of visual areas. 📖 doi.org/10.1371/jour...

Non-invasive mapping of the temporal processing hierarchy in the human visual cortex

Author summary Vision doesn’t happen instantaneously, but unfolds over time. While we understand a lot about how the brain processes visual space, understanding how the brain processes information ove...

doi.org

Marcelo Rinesi@marcelorinesi.bsky.social · 3w ago

Interesting! Moving from "*the* model" to "a handful of" to "the space of" strains culture, training, and political assumptions in science and industry but IMHO it's how we can best turn increased computational power into better research. @giovannisaraceno.bsky.social @antcalcagni.bsky.social

Towards a Geometric Characterization of Multiverse Analysis

Multiverse analysis makes explicit how empirical conclusions depend on alternative, defensible analytical specifications. Standard approaches usually generate the multiverse first and then summarize…

arxiv.org

Timothy O'Leary@timothyoleary.bsky.social · 3w ago

Compressed* into 1 post: plenty of things that compute are not computers. Computation is therefore a useful word to describe many processes in the brain and nobody who uses the word is implying the brain is a laptop. Let's move on. *using my brain

Dan Goodman@neural-reckoning.org · 3w ago

Is the brain a computer solved in 4 posts so that we never have to talk about this again (please please please). 1. It's not the same physically as a computer - it's made of meat. Hopefully this one at least is uncontroversial.

Antonino Greco@agreco.bsky.social · 4w ago

The bigger problem is that editors often fail to enforce this principle. It’s absurd that authors are expected to submit to toxic reviews. Editors should act as filters, screening out irrelevant comments and asking authors to address only the valid concerns raised by reviewers.

Timothy O'Leary@timothyoleary.bsky.social · 4w ago

Dear peer reviewers everywhere: sometimes we feel pressure to find issues with a submission as a demonstration of diligence. But we have to recognise this is a mistake: picking random and sometimes incoherent holes in research add confusion, generates unnecessary work and encourages defensive

Timothy O'Leary@timothyoleary.bsky.social · 4w ago

Dear peer reviewers everywhere: sometimes we feel pressure to find issues with a submission as a demonstration of diligence. But we have to recognise this is a mistake: picking random and sometimes incoherent holes in research add confusion, generates unnecessary work and encourages defensive

Jonathan A. Michaels@jonathanamichaels.bsky.social · 4w ago

Peer review doesn’t have to be like a generational cycle of abuse. You can break the cycle.

Timothy O'Leary@timothyoleary.bsky.social · 4w ago

Dear peer reviewers everywhere: sometimes we feel pressure to find issues with a submission as a demonstration of diligence. But we have to recognise this is a mistake: picking random and sometimes incoherent holes in research add confusion, generates unnecessary work and encourages defensive

Antonino Greco@agreco.bsky.social · 4w ago

Cool new paper on predictive learning of naturalistic auditory sequences! Nice evidence that predictive processing emerges from distributed cortical interactions rather than a purely hierarchical architecture. 👇👇👇

Biyu Jade He@biyuhe.bsky.social · 4w ago

New paper alert from our lab! nature.com/articles/s41... We used stimuli sequences following 1/f statistical patterns—an ubiquitous statistical structure in natural stimuli—to probe predictive processing in the human brain. How does the brain predict upcoming sensory inputs based on past inputs?

Mark Thornton@markthornton.bsky.social · last mo.

Now in press at Cortex! "False discovery rate correction promotes confounded neuroimaging designs" Paper 🔓: www.sciencedirect.com/science/arti...

Effect of confound mass on true positive rates under test-wise FDR correction in completely arbitrary data. Confound mass represents how large a confound is in terms of the product of the number of tests it is present in, and its mean effect size across these tests. Results are shown at differing combinations of true effect size, number of tests with true (i.e., non-confound) effects, and sample size. Inflated surface maps of meta-analytic z-statistics from Neurosynth for low-level confounds (top) and high-level cognitive tasks (bottom). Red reflects positive activations, blue reflects negative (de)activations, and darker colors indicate larger z-statistics. Maps are thresholded at |z| = 1 for visualization purposes.The difference in integrated true positive rate between parcel-wise FDR and parcel-wise FWER (y-axis) is plotted as a function of confound effect size. Each point represents 1 out of 5000 studies simulated for each combination of task and confound. Smoothed loess lines are used to better show the overall trends.Effect of FDR-based publication bias on observed confound effects sizes. Simulated meta-analytic confound effect sizes are visualized through violin plots for each combination of task effect and confound effect examined in the neural data simulations. Meta-analyses featuring publication bias (orange) substantially inflate these effect size estimates in all cases, relative to meta-analyses featuring no publication bias (blue). Moreover, this bias was present – and in most cases larger – in the subset of studies that were included in the meta-analysis specifically when the publication bias was based on FDR instead of FWER integrated true positive rates (green).
Mark Thornton@markthornton.bsky.social · 11mo ago

After 5 years, I finally carved out time to turn this blog post on FDR (markallenthornton.com/blog/fdr-pro...) into a manuscript. The preprint features a much broader range of simulations showing how FDR promotes confounds, and how this effect compounds with publication bias: osf.io/preprints/ps...

Effect of confound mass on true positive rates under FDR correction. Confound mass represents how large a confound is in terms of the product of its voxel extent and effect size. Results are shown at differing combinations of true effect size, true effect voxel extent, and sample size.
Roxana Zeraati@roxana-zeraati.bsky.social · 4w ago

Our paper "Neural timescales from a computational perspective" is finally out in @natneuro.nature.com: www.nature.com/articles/s41... We discuss how computational models and methods can distill empirical observations on neural timescales into quantitative, testable theories of brain computation.

Neural timescales from a computational perspective - Nature Neuroscience

This review integrates computational approaches to provide a unified view on how data analysis methods, biophysical mechanistic models and machine learning approaches can help to uncover the origins a...

nature.com

Pablo Samuel Castro@pcastr.bsky.social · 5mo ago

New paper 🚨 "Stable Deep Reinforcement Learning via Isotropic Gaussian Representations" Deep RL suffers from unstable training, representation collapse, and neuron dormancy. We show that a simple geometric insight, isotropic Gaussian representations, can fix this. Here's how 👇

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Richard Sever@richardsever.bsky.social · last mo.

“move from preprint to peer-reviewed publication leaves the central claims of most abstracts intact, indicating preprints are a reliable source…papers that were never posted as preprints were retracted at roughly twice the rate of those that were…” www.biorxiv.org/content/10.6...

Tracking claim changes from preprint to publication across 72,644 biomedical studies using large language models

Preprints now disseminate a large share of biomedical research before peer review. Because they have not yet passed peer review, some scientists regard preprint claims as unverified or potentially unr...

biorxiv.org