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Geometric Hyperscanning, Interbrain Networks and Social Cognition (w/ Nicolás Hinrichs)

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Sep 20, 2025

Guest

Nicolás Hinrichs

Dr. Nicolás Hinrichs is a researcher specializing in computational and social neuroscience at prestigious institutes. He delves into geometric hyperscanning and how interbrain networks influence social cognition. The conversation highlights the transition from conventional methods to geometry-driven models, the interplay between relational meaning and process philosophy, and how active inference can enhance dyadic interactions. Hinrichs also discusses ethical considerations in neuroscience, emphasizing the importance of responsible mathematical practices for mental health interventions.

01:37:26

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INSIGHT

Edge-Centric Geometry Reveals Routing

Geometric machine learning shifts hyperscanning from node metrics to edge-centric topology.
Curvature reveals whether edges bridge modules or live in tight clusters, enabling information-routing analysis.

question_answer

ANECDOTE

Learning Geometry Through Collaboration

Nicolás describes learning geometry while collaborating with Melanie Weber and being 'positively overwhelmed'.
He used toy models and expert guidance to make complex curvature concepts accessible.

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ADVICE

Learn Geometry By Testing Data

Don't wait to fully master the math before testing new tools on data.
Use toy examples and iterative experiments to understand geometric concepts in practice.

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From Critique to Geometric Methods

04:55 • 17min

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Hyperscanning: Topology over Correlation

21:34 • 13min

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Relational Meaning and Philosophical Context

34:18 • 24min

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Active Inference for Dyadic Interaction

57:52 • 12min

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Interaction as Emergence — Clinical Applications

01:09:42 • 8min

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Speculation, Ethics, and Mathematical Practice

• Mentioned in 5 episodes

Active Inference

The Free Energy Principle and Its Applications

Sanjeev Namjoshi

This book, written by Dr. Sanjeev Namjoshi, delves into the Active Inference framework and the Free Energy Principle (FEP). It explains how these frameworks describe how biological and artificial systems maintain stability by minimizing uncertainty about their environment. The book covers the theoretical foundations, including Bayesian mechanics and variational free energy minimization, and discusses the practical applications of Active Inference in perception, learning, attention, planning, and decision-making. It contrasts Active Inference with traditional machine learning approaches, highlighting its natural capacity for exploration and curiosity through epistemic value.

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• Mentioned in 3 episodes

Meeting the Universe Halfway

Quantum Physics and the Entanglement of Matter and Meaning

Karen Barad

Karen Barad's "Meeting the Universe Halfway" offers a unique perspective on quantum mechanics, integrating physics, philosophy, and feminist theory. Barad challenges traditional notions of objectivity and representation, arguing for a more relational understanding of reality. She draws on the work of Niels Bohr to develop her concept of agential realism, which emphasizes the active role of the observer in shaping the observed. The book is known for its innovative approach and its interdisciplinary engagement. It's a challenging but rewarding read for those interested in the philosophical implications of quantum mechanics.

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• Mentioned in 2 episodes

Context Changes Everything

Alicia Juarrero

In 'Context Changes Everything', Alicia Juarrero challenges traditional views of causation by arguing that constraints, rather than forceful impacts, are key to creating coherence. She uses complexity science to offer a new perspective on identity, hierarchy, and top-down causation, with implications for sociology, economics, and more. The book presents a framework for understanding how dynamic interdependencies maintain coherent structures and how humans can foster robust and resilient identities.

#16142

• Mentioned in 2 episodes

Mathematics of Machine Learning

Master Linear Algebra, Calculus, and Probability for Machine Learning

Tivadar Danka

This book provides a rigorous yet accessible introduction to the mathematical foundations of machine learning, including linear algebra, calculus, and probability theory. It is designed for engineers, developers, and data scientists seeking to deepen their understanding of machine learning algorithms. The book includes Python implementations to illustrate practical applications of these mathematical concepts.

Dr Nicolás Hinrichs is a researcher at the Okinawa Institute for Science and Technology and the Max Planck Institute for Human Cognitive and Brain Sciences, interested in the behavioural and neurobiological correlates of mental health interventions via hyperscanning; drawing on active‑inference theory, process philosophy, and embodied cognition, he models interpersonal neural networks via information geometry to examine how collective priors and affect drive shared intelligence in agential systems. In this episode, we discuss second-person neuroscience, geometric hyperscanning, interbrain networks, social cognition and neomaterialism.

You can find more of Dr Hinrichs' work at https://nicolashinrichs.de/ and https://www.linkedin.com/in/nicolas-hinrichs/

⁠Watch it on YouTube here⁠.⁠⁠⁠

RSam Podcast #82

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