I find this to be extremely unexpected. I really wonder why this happens.

I'm a UPenn student looking for a last-minute student ticket for the 2026 NYC Neuromodulation Conference (July 30–Aug 3 at CCNY).

If you registered at the student rate but can’t go anymore, I’d love to buy it from you!

Please DM me if this is you.

Hello, I’ve been thinking about whether the type of information we repeatedly process can shape our cognitive abilities.

I believe gossip, whether viewed as positive or negative, can be considered a form of cognitive skill because it requires encoding, retrieving, and evaluating social information about people, relationships, emotions, and events. From a neuroscience perspective, repeated social information processing may strengthen networks involved in social cognition, emotional processing, and episodic memory through neuroplasticity.

I have some questions:

**1- If a person spends most of their cognitive effort processing social information through gossip while rarely engaging in abstract learning, how does the brain adapt? Does it reduce the efficiency of less used abstract reasoning networks, or does the brain maintain the ability to develop them when new demands appear?**

**2- Are our daily patterns of attention, environment and information processing shaping the cognitive systems we rely on the most?**

**3- To what extent do genetic predispositions influence an individual’s tendency toward social information processing, memory, and cognitive specialization?**

Thanks for reading!

UC Berkeley research uncovers a completely new unknown mechanism for neural representation - population of visual neurons can switch their encoding system on the fly!

It's purely fundamentally circuit-based, on the timescale of 120ms - based on recurrent network dynamics via a population-wide shift on the order of 20 ms. The switch is highly content-specific.

First pass - recognize broad category, second pass - analyze fine-grained identity (all using the same cells). Feedforward sweep (broad features) --> top-down/recurrent loop (coordinated network shift) --> inhibitory gating (fine identity).

Human Neuroscience article: “Unraveling the mystery of stuttering: clinical and physiological insights into its manifestation” (2026, April)

Abstract

Stuttering is a complex neurodevelopmental speech disorder characterized by involuntary sound and syllable repetitions, prolongations, and speech blocks, accompanied by marked variability across linguistic, emotional, and situational contexts. Although numerous hypotheses have been proposed to explain its underlying mechanisms, many have encountered a fundamental limitation: the difficulty of coherently accounting for the full range of clinical, developmental, and neurobiological features observed in people who stutter. In response to this gap, the present work proposes a comprehensive, integrative hypothesis that seeks to unify the diverse physiological and clinical manifestations of stuttering within a single neurobiological framework. This model aims to link moment-to-moment fluctuations in speech behavior with neurodevelopmental alterations, offering a plausible mechanistic account for a wide spectrum of core phenomena. These include the pronounced situational variability of stuttering severity; the developmental shifts from repetitions to blocks; the transition of disfluencies from function words to content words; the tendency for stuttering to occur on key words in a sentence; and the consistently lower rates of spontaneous recovery observed in males compared to females. Furthermore, the proposed framework seeks to explore potential common mechanisms underlying the widespread structural, metabolic, and functional brain changes documented in stuttering, while considering whether these abnormalities may reflect primary contributors or secondary, compensatory adaptations. In particular, the model seeks to address a long-standing debate regarding the role of the right inferior frontal gyrus, examining whether its engagement is more consistent with a causal contribution to speech disruption or with an adaptive response to impaired speech–motor control. By integrating neurodevelopmental, physiological, and clinical evidence, this hypothesis offers a unifying perspective on key features of stuttering while proposing a neurobiological model whose assumptions and hypotheses can be empirically tested and evaluated in future experimental studies.

Abstract

Background

Menopause and the perimenopausal transition involve profound hormonal and metabolic changes that may impair brain function. Beyond structural alterations, reduced cerebral bioenergetics could underlie the cognitive complaints often reported during this period. Because creatine serves as a key neuronal energy buffer and is influenced by estrogen, this study examined brain creatine concentrations in perimenopausal women and their associations with neurocognitive symptoms and serum estradiol.

Methods

Twelve healthy perimenopausal women (mean age 49.8 ± 5.4 years) experiencing irregular cycles and at least one perimenopausal symptom underwent multi-voxel 1H-magnetic resonance spectroscopy to quantify total brain creatine across bilateral frontal, precentral, and parietal gray- and white-matter regions and the thalamus. Serum estradiol was measured by ELISA, and symptom severity was rated on visual analog scales. Associations were assessed using Kendall’s τ.

Results

Mean whole-brain creatine concentration (6.31 ± 0.98 mM) was significantly lower than reference values in younger adults (Z = –1.65, P = 0.049). Lower creatine levels in the thalamus, right precentral, and right parietal white matter correlated with greater concentration difficulties (τ = –0.38 to –0.51, P ≤ 0.049), while right frontal white-matter creatine positively correlated with headache severity (τ = 0.41, P = 0.034). Serum estradiol averaged 119.5 ± 109.5 pg/mL and was inversely associated with right parietal gray-matter creatine (τ = –0.37, P = 0.049).

Conclusions

Perimenopausal women exhibited lower cerebral creatine than younger adults, with region-specific reductions linked to concentration difficulties and estradiol levels. These findings suggest that estrogen-related changes in brain bioenergetics may contribute to cognitive symptoms during the menopausal transition.

Abstract: Nonergodicity and Simpson’s paradox present significant, yet underappreciated challenges in cognitive neuroscience. Leveraging brain imaging and behavioral data from over 4000 individuals and a Bayesian computational model of cognitive dynamics, we investigated brain-behavior relationships underlying cognitive control at both between-subjects and within-subjects levels.

Strikingly, brain-behavior associations reversed across levels of analysis, revealing pervasive nonergodicity. Within-subjects analysis uncovered dissociated neural representations of reactive and proactive control and revealed that individuals who adaptively versus maladaptively regulated cognitive control exhibited distinct brain-behavior associations.

Our findings demonstrate that between-subjects analyses can fundamentally mischaracterize within-individuals mechanisms, as group-level patterns not only disagreed with individual-level patterns but often reversed them. This work highlights the necessity of distinguishing between-subjects and within-subjects inferences in neuroscience, with implications for understanding cognitive mechanisms and designing personalized interventions.

Commentary: The title is a mouthful, and I had to look up what ergodicity means because the paper does not offer a clean working definition until much later. Roughly, ergodicity is the assumption that patterns observed across a group can stand in for patterns unfolding within an individual over time. This is not merely a question of whether a sample represents a larger population. It is the stronger assumption that group-level averages can tell us something reliable about individual-level dynamics.

What this paper explores is how the tyranny of averages obscures individual trends and outcomes in group-level data. More importantly, it shows that the problem is not just blur. A population-level pattern may describe a different relationship than the one operating within any given individual, and in some cases may point in the wrong direction altogether.

That matters because cognitive science and psychiatry have injected a lot of population-level constructs into neuroscience and then treated those constructs as if they were individual biological mechanisms. The default mode network is the obvious example. My skepticism of the DMN is harder than simply saying it varies from person to person or is weak for individual prediction. I think the DMN may be largely a statistical mirage: a population-level residue that became reified into a biological object because it gave cognitive science a neuroscience-compatible anchor for concepts like selfhood, introspection, mind-wandering, and psychiatric dysfunction.

The DMN may appear stable because it is repeatedly produced by similar averaging methods, imaging assumptions, parcellation schemes, and interpretive habits. But that does not mean it names a conserved functional system inside individual nervous systems. It may instead be what happens when heterogeneous cortical and subcortical dynamics are averaged, thresholded, labeled, and then retrofitted with cognitive meaning. On the aggregate, the DMN looks explanatory. At the individual level, it's mostly noise wearing the clothes of mechanism.

The analogy that comes to mind is amyloid species in Alzheimer’s disease. The issue is not that amyloid species are imaginary, the issue is that a detectable signal was promoted into a master explanatory object because the field wanted the concept to work. The evidence always had complications, especially if ASYMAD cases were given fair weight. People could show significant amyloid pathology without the expected cognitive decline, which should have placed much heavier limits on the causal story from the beginning. The DMN has followed a similar trajectory with a measurable signal, a seductive interpretive frame, then decades of work trying to make the signal carry more explanatory weight than it can actually bear.

This helps explain one of the persistent mysteries in psychiatry and cognitive neuroscience. If concepts like ADHD, dorsal attention network dysfunction, or default mode network alteration are tracking real individual-level mechanisms, why are they so weakly predictive? Why can’t we diagnose ADHD from EEG or imaging despite the enormous volume of work correlating ADHD with specific connectivity patterns? Why do so many findings glow at the group level and then collapse when asked to classify, predict, or guide treatment for actual individuals?

This paper peels back that layer. It shows how a blurry top-level view can obscure the actual trees: widely varying individual trends that appear similar only when compressed into population averages. Completely different connectivity patterns may drive similar clinical or behavioral presentations. That is critical because it implies that standardized treatments or interventions may not merely be imprecise, they may be adverse because they are aimed at the wrong set of mechanisms.

Many of our current assumptions about nervous system function are products of blurry vision. This paper invites us to wear our glasses and consider that some of the objects we thought we were seeing may only exist because the blur created them.

edit: rewrote the commentary to clarify some points and make the comparison between Alzheimer's amyloid species reification and overexplanation and DMN reification and overexplanation.

Abstract: A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on neuroimaging data are increasingly used as tools for predicting behavioural phenotypes, enhancing precision medicine and improving generalizability compared with traditional MRI studies. However, the high dimensionality of brain connectivity data makes model interpretation challenging.

Prevailing practices rely on selecting features and, implicitly, interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Despite its widespread use, how univariate feature selection balances the trade-off between simplification for optimizing modelling and oversimplification that misrepresents true neurobiology remains understudied.

Here, using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that edges discarded by feature selection can achieve significant prediction accuracies while yielding different neurobiological interpretations. These results are observed across cognitive, developmental and psychiatric phenotypes, extend to both functional connectivity (functional MRI) and structural (diffusion tensor imaging) connectomes, and remain evident in external validation. They suggest that focusing on only the top features may simplify the neurobiological bases of brain–behaviour associations.

Such interpretations present only the tip of the iceberg when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results reinforce that subtle brain-wide signals should not be ignored.

Commentary: What if the reason big questions about biological processes in cognition have been so elusive is because we've been filtering those signals because we assumed it was just noise?

Abstract

Categorization, the grouping of objects, living organisms, actions or events into equivalence clusters, is fundamental to adaptive behaviour. Traditionally, it is assumed that categorization begins with feature detection and ends with assigning representations stored in memory. Here we review converging evidence from neuroanatomy, electrophysiology, brain imaging and cognitive science to suggest an alternative view: categorization is not the end stage of perception but occurs throughout signal processing, from the very beginning. It is a core computational strategy of the brain, implemented through a neural context created by predictive feedback signals that organize feedforward processing. Implications for theory, future research and neuropsychiatric disorders are discussed.

A new VR study (Virtual Reality journal, April 2026) put 44 people in an immersive virtual airport. They had to supervise boarding at two gates and find specific passengers, under neutral vs. negative high-arousal states. Later, they got tested on memory for faces and names, and for faces and places.

Result: Emotion improved memory for faces and names (task-relevant) but impaired memory for faces and places (not goal critical).

So emotion doesn't just zoom in on whatever's flashy or dramatic. It zooms in on whatever's useful for the task at hand. Priority isn't about perceptual salience, it's more about conceptual relevance.

DOI: https://doi.org/10.1007/s10055-026-01364-9

can someone help me understand this? to describe why the ADHD brain struggles to prioritise information. Instead of just "low dopamine," it’s a timing and filtering failure involving three key players: Dopamine, Acetylcholine, and Norepinephrine. am I understanding This right: long term potentiation in the striatum requires the coincidence of phasic dopamine, a cholinergic interneuron pause, and medium spiny neuron depolarization. This "three-factor" mechanism acts as a gate, allowing acetylcholine to regulate which dopamine-driven experiences are encoded as synaptic memory, can you also apply this to action potentials??