Ep.07. The Body Keeps the Beat

Ep.08. The Clock That was Never There

Paper: Buzsáki, G. (2025). Time, space, memory and brain–body rhythms. Nature Reviews Neuroscience, 27, 61–78. https://doi.org/10.1038/s41583-025-00987-2 [https://doi.org/10.1038/s41583-025-00987-2] Key concepts: Physical time (chronos), experienced time (kairos), Weber-Fechner law, logarithmic time organization, cross-frequency phase-amplitude coupling, brain-body oscillation hierarchy, ripple oscillations, theta oscillations, ultraslow oscillations, interstitial cells of Cajal, insular cortex as timing hub, supplementary motor area (SMA), place cells, time cells, factorization framework (what-where-when), episodic memory, prospective and retrospective timing, scalar expectancy model, subjective time warping, dopamine and duration estimation, reader mechanism problem. Further reading: * Buzsáki, G. (2006). Rhythms of the Brain. Oxford University Press. * Meissner, K., & Wittmann, M. (2011). Body signals, cardiac awareness, and the perception of time. Biological Psychology, 86, 289–297. * Engelen, T., Solcà, M., & Tallon-Baudry, C. (2023). Interoceptive rhythms in the brain. Nature Neuroscience, 26, 1670–1684. Cognitive observation: The next time time seems to stretch or compress, in a moment of fear, in deep concentration, in the strange compression of a memory from years ago, notice that what is changing is not the clock. The clock was never there. What is changing is the body’s rhythmic contribution to the brain’s metric of succession.

Ep.07. The Body Keeps the Beat

Paper: Engelen, T., Solcà, M., & Tallon-Baudry, C. (2023). Interoceptive rhythms in the brain. Nature Neuroscience, 26, 1670–1684. Key concepts: Interoception, heartbeat-evoked response (HER), heartbeat-evoked potential (HEP), cardiac cycle effects, pulsed inhibition hypothesis, baroreceptors, nucleus tractus solitarius, pre-Bötzinger complex, nasal respiration, limbic entrainment, gastric slow wave, interstitial cells of Cajal, gastric network, scaffolding hypothesis, oscillatory synchrony, predictive coding, allostasis, multisensory integration, depersonalization disorder, default mode network, bodily self-consciousness. Further reading: * Azzalini, D., Rebollo, I., & Tallon-Baudry, C. (2019). Visceral signals shape brain dynamics and cognition. Trends in Cognitive Sciences, 23, 488–509. * Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17, 565–573. * Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature Reviews Neuroscience, 16, 419–429. Cognitive observation: The next time we feel our own heartbeat — at rest, under the fingers, or in a moment of sudden alertness — we are noticing one of the rhythms currently modulating our perceptual threshold, our self-related thought, and the neural basis of the sense that this experience is ours.

Ep.06. The Wandering Mind

What is the brain doing when attention drifts away from the task at hand? This episode explores a 2024 Nature Communications study linking hippocampal sharp-wave ripples to naturally occurring self-generated thoughts in humans. Paper: Iwata et al. (2024). Hippocampal sharp-wave ripples correlate with periods of naturally occurring self-generated thoughts in humans. Nature Communications, 15, 4078 Key ideas * Mind wandering is not necessarily a failure of attention. * Hippocampal sharp-wave ripples are linked to memory, internal simulation, and offline processing. * In this study, SWR rates predicted the content of self-generated thought. * Thought content explained SWR rates much better than physiological variables. * Mood did not explain SWR rates, which makes the finding more specific and more interesting. Cognitive observation Next time we notice our mind wandering, we might not treat it as a failure of attention. We might treat it as a sign that the brain is quietly reorganizing experience. As always, keep questioning your priors.

Ep.05. The Inference Engine

A rat freezes to a tone it was never shocked with. It learned that the tone predicted a light, and separately that the light predicted a shock, and its brain built the rest. This is inferred fear: emotional memory assembled from pieces of knowledge that were never directly dangerous. A 2025 paper in Nature from Gu and Johansen at RIKEN Center for Brain Science in Wako City, Japan, identifies where in the brain this inference is built. Neurons in the dorsomedial prefrontal cortex encode a flexible internal model, linking sensory experience to emotional consequence through a multi-step cellular mechanism that begins before any fear has entered the picture. This episode covers the tag-and-capture mechanism, the anatomy of the dmPFC-to-amygdala projection, and what selective extinction reveals about how the emotional brain is structured. The amygdala learns what it is directly taught. The prefrontal cortex infers the rest. Paper: Gu, X. & Johansen, J. P. (2025). Prefrontal encoding of an internal model for emotional inference. Nature, 643, 1044-1056. Key concepts: Sensory preconditioning, inferred fear, dorsomedial prefrontal cortex (dmPFC), basolateral amygdala, calcium imaging, miniscope imaging, optogenetics, model-based learning, associative inference, computational psychiatry, predictive coding. Further reading: Rescorla, R. A. (1980). Pavlovian second-order conditioning: Studies in associative learning. Erlbaum. Schiller, D., et al. (2008). Preventing the return of fear in humans using reconsolidation update mechanisms. Nature. Quirk, G. J., & Mueller, D. (2008). Neural mechanisms of extinction learning and retrieval. Neuropsychopharmacology. Arc connection: Episode 5 makes the prefrontal thread explicit. Across Episodes 1–4, the prefrontal cortex appeared performing prediction updates, schema consolidation, extended conversational context processing, and emotional regulation. Here it appears as the structure that builds the internal model itself — constructing a representation of how the world is organized before emotional significance arrives. Cognitive observation: The next time you feel afraid of something you have never directly encountered, the dmPFC is running an inference from relational knowledge it built quietly, without your awareness, from associations it observed long before the emotion arrived.

Ep.04. The Talking Brain: How Conversation Happens Inside Your Head

Think about a conversation that felt genuinely connected, where meaning was built between two people into something neither could have reached alone. And think about one that didn't, where, despite good intentions, something in the rhythm was off, the timing never synced, the understanding never quite landed. What's the difference? It might not be what was said. It might be when. In Episode 4 of The Latent State, we cover Yamashita, Kubo, and Nishimoto's 2025 paper in Nature Human Behaviour, a study that did something previous language neuroscience never managed: scan people's brains during hours of real, spontaneous conversation, and map how linguistic meaning is organized across multiple timescales simultaneously. The finding is clean and striking. When we speak, the brain prioritizes short timescales such as words, single sentences, and immediate context. When we listen, it prioritizes long timescales such as multiple turns, extended discourse, and the accumulated meaning of the exchange. Same brain, same conversation, two fundamentally different temporal architectures running in parallel. This is Japan-based research, produced at CiNet at Osaka University — and it marks The Latent State's deliberate turn toward covering world-class neuroscience happening right here. We cover the methodology, the findings, and what they reveal about AI language models, language disorders, and the predictive coding framework. We also ask the uncomfortable questions: what does GPT actually tell us about the brain? And what does n=8 really mean for generalizability? 🎙️ The Latent State, Episode 4. Paper: Yamashita, M., Kubo, R. & Nishimoto, S. (2025). Conversational content is organized across multiple timescales in the brain. Nature Human Behaviour, 9, 2066–2078. Key concepts covered: Naturalistic neuroscience, voxel-wise encoding modeling, GPT contextual embeddings, timescale selectivity, production vs. comprehension, variance partitioning, bimodal voxels, semantic principal components, interactive language, default mode network, theory of mind network Further reading: * Huth et al. (2016), Nature — the foundational semantic mapping paper * Lerner et al. (2011), Journal of Neuroscience — temporal receptive windows in narrative comprehension * Caucheteux, Gramfort & King (2023), Nature Human Behaviour — predictive coding in speech comprehension * Goldstein et al. (2022), Nature Neuroscience — shared computational principles for language in humans and language models * Hasson & Frith (2016), Philosophical Transactions of the Royal Society B — coupled dynamics in social interaction Japan connection: This research was conducted at the University of Osaka and CiNet — the Center for Information and Neural Networks, one of Japan's leading computational neuroscience institutes. Arc connection: Episodes 1–3 established predictive coding as a framework for perception, cognition, and emotion. Episode 4 extends the framework to interactive language, showing that conversation is hierarchical predictive coding running simultaneously in two directions, with distinct timescale architectures for production and comprehension. Cognitive observation: Next time a conversation feels like it isn't quite connecting, ask whether you and your conversational partner are operating on the same timescale, integrating context across the same temporal window. Sometimes, conversational mismatch is about rhythm and timing, not just content.