Jinwoo Lee

Excited to share our paper published in @CommsPsychol 🥳 Using VR, EEG, real-time affect rating, and deep representation learning, we show that subjective awe is better predicted by ambivalence-related behavior and neurogeometry than univalent ones (1/n) nature.com/articles/s4427…

How do you know how someone is feeling? We don't just use facial expressions, we combine expressions with "context", like body language and scenes, to truly understand emotions. Our study investigated the brain's computational mechanisms for integrating these cues in real-time.

Using Artificial Intelligence to Generate Affective Images: Methodology and Initial Library - journals.sagepub.com/doi/10.1177/25…

Clarifying the conceptual dimensions of representation in neuroscience — a Perspective by Stephan Pohl, Edgar Y. Walker, David L. Barack, Jennifer Lee, Rachel N. Denison, Ned Block, Florent Meyniel & Wei Ji Ma nature.com/articles/s4158…

A new preprint titled Lost in Emotional Space introduces the concept of emotional bandwidth as a metric for navigating daily emotional landscapes. doi.org/10.31234/osf.i…

How the developing brain (mostly the hippocampus) constructs increasingly flexible representations of predictive temporal structure biorxiv.org/content/10.648…

𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗺𝗮𝗻𝗶𝗳𝗼𝗹𝗱𝘀 𝗮𝗻𝗱 𝗿𝗲𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗯𝗿𝗮𝗶𝗻? This looks like a must read (suppl material bursting with goodies). doi.org/10.1016/j.neur…

Online now: Rethinking neuroaesthetics: Toward a multidimensional and integrative science of aesthetic experience dlvr.it/TRJ1NN

A unifying taxonomy of dyadic emotional processes Review by Martine W.F.T. Verhees, Batja Mesquita (@BatjaMesquita), Eva Ceulemans, Joeri Hofmans, Lesley Verhofstadt, & Peter Kuppens tinyurl.com/2xzmmt5z

In our new work, we find that the ways individual brains differ are *not* constrained to a few dominant patterns. We find distinct patterns in how individual brains process natural movies along many latent dimensions, and these differences are reliable across different movies.

New Substack post: When neural networks learn to speak the language of science Modern AI excels at prediction—forecasting weather, folding proteins, winning games—but ask it why and you get silence. The knowledge that must be encoded somewhere in those billions of parameters remains locked away, inaccessible to human understanding. Ziming Liu and coauthors present a framework to change that. Their paper develops Kolmogorov-Arnold Networks into tools for curiosity-driven science—not just prediction, but discovery. I wrote a deep dive on Substack covering the key ideas, the new tools introduced in the paper, and applications ranging from conservation laws to hidden symmetries in black hole physics. Link to the post: open.substack.com/pub/bravoabad/…

Now out in Nature Human Behaviour! 🚀🚀 Over the past decades, research on collective human behaviour has relied heavily on networks. This is intuitive: people interact with other people. However, we argue that this dominant framework misses a crucial ingredient. Traditional networks represent agents as nodes and pairwise relations as edges. As a result, they fundamentally assume that social interactions can be decomposed into pairs. Yet many social processes are irreducibly group-based. A simple example: a group of three coauthors writing a paper cannot be reduced to three independent pairs of coauthors. The group itself matters. In this article, we review a wide range of empirical and theoretical cases where group interactions cannot be decomposed into pairwise ones, and show that higher-order interactions shape collective behaviour above and beyond dyadic ties. We advocate studying collective behaviour on hypergraphs, where interactions can involve multiple agents simultaneously. We review how hypergraphs provide new insights across domains, including affiliation and collaboration networks, high-frequency contact settings (families, friends), and key social processes such as social contagion, cooperation, truth-telling, and moral behaviour. Finally, we outline promising directions for future research: addressing computational challenges of higher-order models; studying bias and inequality in group dynamics; combining hypergraphs and large language models to investigate the coevolution of language and behaviour; and using higher-order networks to simulate the impact of policies before implementation; and others. We are very excited about this work and hope it will inspire further research in a rapidly growing and fundamental area with broad real-world implications. Link to the paper in the first reply This work was brilliantly led by Federico Battiston (@fede7j), with an outstanding team of co-authors: Fariba Karimi (@fariba_k), Sune Lehmann, Andrea Bamberg Migliano, Onkar Sadekar (@OnkarSadekar), Angel Sanchez, & Matjaz Perc (@matjazperc)

Ventral tegmental area GABA neurons integrate positive and negative valence | Nature Communications nature.com/articles/s4146…

Just out in Nature Reviews Neuroscience! We've been studying information synergy within the brain for a few years now: here we explore how we can take this approach to the level of synergy *between* brains! 🧠🔄🧠 Thanks to Edoardo Chidichimo for leading this inter-brain synergy!

New paper on AI consciousness! Here we present the theory-derived indicator method for assessing AI systems for consciousness. Link below.

Another new paper from Feldman Barrett and colleagues connecting allostasis and interoception. Many new insights! Cortical and subcortical mapping of the human allostatic–interoceptive system using 7 Tesla fMRI doi.org/10.1038/s41593…

The process of affect labeling Review by Ella Givon, Nachshon Meiran (@NMeiran), & Amit Goldenberg (@Amit_Goldenb) Free access before Dec 6: tinyurl.com/4a9h5bkw

Feldman-Barret et al: "Allostasis is predictive regulation, distinguished from the concept of homeostasis, which involves reactive regulation to perturbations that return a system back to an optimal set point." 1/8 Allostasis at the core of brain function cell.com/neuron/fulltex…

@jimpe_hitsuwari @tmitter Congrats, Jimpei! It's truly wonderful to expand the scope of empirical/neuro aesthetics together with Dr. Ishizu!

学振PD無事採択されました！来年度からは関西大学、石津先生 @tmitter のもとでお世話になりながら、ドイツでの滞在を継続する予定です。#学振 採択課題「日独文化比較を通した風化に対する美的感性の心理学・神経科学的検討」

@nico_hinrichs @neur_reps @NeurIPSConf @NoahGuzman14 @mweber_PU This is really cool, I've been a big fan of your geometric hyperscanning arxiv paper. I will track your future studies, too!

On a Geometry for Interbrain Networks: discrete Ricci curvature + entropy can capture phase transitions and information routing in social neural dynamics. Accepted to the 4th NeurReps @neur_reps workshop at NeurIPS @NeurIPSConf 2025. With @NoahGuzman14 & @mweber_PU.