The research team, led by Bartłomiej Król-Józaga of AGH University of Krakow together with colleagues at Dartmouth College and Jagiellonian University, tackled a long-standing gap in computational neuroscience. Most existing models of decision making simulate only one of the many parallel operations the brain performs—reward processing, evidence accumulation, strategy selection, or option choice—rather than the whole coordinated process. An earlier spiking neural network model had attempted a more holistic simulation, but it ignored a key biological feature: the powerful role that inhibitory neural connections play in shaping cognition.

Inhibition is far more than a neural brake. Inhibitory interneurons synchronize the brain oscillations that coordinate the networks involved in choice, filter out irrelevant information, and refine neural responses so that behavior can adapt to varying cognitive demands. This mechanism supports the interplay between brain regions central to decision making: the orbitofrontal cortex (OFC), which binds sensory input with value signals; the dorsolateral prefrontal cortex (dlPFC), which accumulates and integrates information over time; and the basal ganglia, which select the best option. Without inhibition, neural circuits risk runaway excitation—the kind of pathological over-activation seen in conditions such as epilepsy and schizophrenia.

To test whether inhibition could explain how people choose decision strategies, the team used a well-studied experimental paradigm called the sequential probabilistic inference task. Twenty-four participants, aged 21 to 27, memorized the importance weights of six attributes and then viewed the binary values of two choice options, one attribute at a time. On each trial they could either pick option A, pick option B, or request more information. Crucially, the task came in two flavors: a Non-Compensatory condition, where one attribute dominates and a simple heuristic called Take The Best (TTB) works well, and a Compensatory condition, where all attributes carry similar weight and the rational Weighted Additive rule (WADD)—which sums weighted evidence across everything—is the smarter strategy.

The model itself was built within the Neural Engineering Framework (NEF), a mathematical methodology for constructing large-scale neural models with realistic spiking neurons. In the NEF, each neuron is characterized by an encoder, a gain, and a bias that together define its tuning curve—how strongly it fires in response to an input signal. Filtered spike trains are decoded with optimized weights to reconstruct the represented signals, and transformations such as value-weight multiplication are implemented through learned connection strengths. Dynamics, the third NEF principle, captures how neural systems evolve over time, enabling the model to reproduce mechanisms like working memory, evidence accumulation, and choice competition.

The architecture mirrors known brain anatomy. Cue values flow through perception and memory components into the model’s OFC population, where they are combined with stored importance weights. The weighted values then feed into a dlPFC evidence population whose recurrent connections act as an integrator, accumulating information across time steps. A dlPFC contrast population computes the difference between the integrated values of the two options, sharpening the distinction before signals reach the basal ganglia. There, a Winner-Take-All mechanism—implemented through the striatum, globus pallidus, and subthalamic nucleus—selects the option with the highest accumulated evidence, triggering a motor response once the decision threshold is reached.

The critical innovation was a neuromodulatory pathway inspired by the locus coeruleus-norepinephrine (LC-NE) system, a brainstem nucleus famous for regulating attention, arousal, and cognitive effort. In the model, a neuromodulatory population receives input from the accumulating dlPFC evidence—interpreted as a marker of cognitive load—and sends inhibitory connections to the OFC. As accumulated evidence grows, this pathway progressively suppresses the incoming cue-value and weight products, simulating how effort-related arousal sharpens the selectivity of pre-decisional information processing. The strength of this inhibition is captured by a single participant-specific parameter, fitted to each individual’s behavioral data alongside parameters for decision threshold, urgency scaling, and lateral-inhibition gain.

The validation strategy was deliberately demanding. The researchers fitted both model variants—the standard version and the inhibition-enhanced version—only to the Non-Compensatory condition, then transferred the frozen parameters to the structurally different Compensatory condition as an out-of-distribution test. This design served a dual purpose: it directly probed whether the load-sensitive inhibitory mechanism, calibrated under low cognitive load, could predict behavior under higher load, and it acted as a built-in control for model complexity. A model that merely exploited an extra free parameter to overfit the training data should generalize worse, not better, to unseen conditions.

The results were striking. On the training condition, the two models performed comparably, with no statistically significant difference. But on the held-out Compensatory data, the inhibition model achieved approximately 17 percent lower mean prediction error than the no-inhibition model—0.55 versus 0.67—a difference confirmed by a Wilcoxon signed-rank test (p = 0.009, rank-biserial correlation r = 0.55), with the inhibition model winning in 16 of 24 participants. Because the advantage appeared precisely where added flexibility should have hurt rather than helped, the finding argues strongly against a mere complexity artifact. Parameter importance analysis reinforced this picture: the inhibition strength ranked second on average, but with high inter-individual variability, being decisive for some participants and negligible for others—hinting that arousal-based inhibition shapes decision behavior meaningfully in only a subset of people.

Intriguingly, the inhibition advantage showed up in the continuous pattern of information sampling rather than in categorical strategy shifts. Both model variants reproduced the characteristic bimodal shape of human cue acquisition—people tend to either sample very few cues or exhaust all six—and both predicted similar WADD-versus-TTB strategy distributions across conditions. What inhibition bought was a quantitatively more accurate replication of how many attributes people consulted under demanding conditions, consistent with the idea that neuromodulation coupled with inhibition grades the selectivity of information gathering rather than flipping the brain between discrete strategies. The authors note that future work should refine the cognitive-load signal to produce realistic trial-by-trial buildup, validate neural trajectories against pupillometry and fMRI data, and explore the role of other neuromodulators such as dopamine. For now, the study offers compelling evidence that the brain’s inhibitory circuitry is not just noise suppression—it is an active, load-sensitive mechanism that helps us decide how to decide.

Subject of Research: The role of neural inhibition in decision strategy selection modeled with spiking neural networks

Article Title: The role of inhibition in modeling decision making with spiking neural networks

Article References: Król-Józaga, B., Duggins, P., Broniec-Wójcik, A., & Wichary, S. (2026). The role of inhibition in modeling decision making with spiking neural networks. Frontiers in Neuroinformatics, 20, Article 1854811. https://doi.org/10.3389/fninf.2026.1854811

Image Credits: AI Generated

DOI: 10.3389/fninf.2026.1854811

Keywords: spiking neural networks, decision making, neural inhibition, orbitofrontal cortex, locus coeruleus, norepinephrine, cognitive load, heuristics, Neural Engineering Framework, basal ganglia, evidence accumulation, computational neuroscience

The vagus nerve is the body’s longest cranial nerve, wandering from the brainstem down to the heart and gut, and its auricular branch provides a rare accessible entry point for external stimulation. When electrodes stimulate the cymba concha of the left ear, signals travel along afferent vagal fibers to the nucleus tractus solitarius in the brainstem, which in turn activates the locus coeruleus and basal forebrain. These nuclei trigger the release of norepinephrine and acetylcholine, two neuromodulators critical for arousal regulation, synaptic plasticity, and cognitive control. In animal models, vagus nerve stimulation has been shown to enhance cognitive flexibility and memory, and to consolidate the synaptic plasticity produced by learning itself. The Dallas team, led by F. M. Filbey, wanted to translate those preclinical findings into a controlled human model that assessed multiple domains of executive function rather than the narrow learning paradigms that have dominated most human taVNS studies to date.

Forty-two healthy volunteers aged 18 to 54 were recruited from the community and assigned to either active or sham stimulation using an alternating allocation procedure stratified by age and sex, a quasi-experimental design in which participants were blinded to their condition but assignment was not fully randomized. After completing mood questionnaires and having their heart rate variability monitored with a validated chest strap, participants received one hour of cognitive training through the BrainHQ program while wearing the NEMOS taVNS device. In the active condition, electrodes were placed on the cymba concha of the left ear, targeting the auricular vagal branch; in the sham condition, they were placed on the center of the ear lobe, which lacks vagal innervation. Stimulation intensity was individually calibrated just below each participant’s pain threshold, with pulses of 200 to 300 microseconds delivered at 25 Hz in cycles of 30 seconds on and 30 seconds off.

BrainHQ is an adaptive, computer-based training program whose algorithm continuously adjusts task difficulty based on moment-to-moment performance, ramping up challenge when participants succeed and easing off when they struggle. The participants worked through three tasks in fixed sequence: Mind Bender, a set-shifting exercise; Divided Attention, which trains discrimination of specific criteria in competing images; and Mixed Signals, a Stroop-based task matching auditory and visual stimuli. This continuously adaptive design was central to the study’s logic. Because the algorithm keeps performance near an individual’s capacity, it continuously taxes cognitive control processes rather than allowing them to plateau, potentially making the training sensitive to subtle transient changes in neural state that static tasks might miss.

After the training hour and a rest break, participants were fitted with a 64-channel EEG cap and underwent simultaneous taVNS and EEG recording during rest with eyes closed and eyes open, followed by a monetary incentive delay task measuring responses to reward and punishment. The researchers then collected a battery of executive function assessments targeting the three core components of the classic tripartite model: cognitive flexibility via the Dimensional Change Card Sort, inhibitory control via the Flanker test, and working memory via the List Sorting test, along with a modified Stroop Color Word task. Spectral power and coherence were calculated across delta, theta, alpha, beta, and gamma bands, with electrodes grouped into frontal, central, parietal, and occipital regions in each hemisphere to map how cortical communication differed between groups.

The results painted a nuanced picture. Unadjusted multivariate analyses suggested a significant overall effect of taVNS on executive function measures, and follow-up comparisons showed that the active group outperformed the sham group on BrainHQ, earning more total stars with a moderate-to-large effect size. Yet when the models were adjusted for age, sex, and education, the evidence for an independent group effect became unclear, and the BrainHQ difference did not survive Bonferroni correction. No group differences emerged on the monetary incentive delay task, the standard Stroop, or the NIH Toolbox measures. The researchers suggest that the adaptive nature of BrainHQ may have made it more sensitive to transient neural modulation, whereas the static difficulty of the other assessments, combined with a healthy sample performing near ceiling, may have lacked sufficient sensitivity to capture momentary changes in cognitive capacity.

The EEG findings were equally intriguing. During the eyes-closed resting condition, the active group showed lower delta-band clustering coefficients than the sham group in the left frontal, central, and parietal regions, with directionally similar but more uncertain effects in left occipital and right-hemisphere regions. Delta oscillations, roughly 1 to 4 Hz, are markers of lower arousal states and large-scale network synchrony, typically linked to deep sleep or low-engagement conditions. A reduction in delta connectivity during active stimulation may reflect a down-regulation of broadly synchronized low-frequency communication and a sharpening of more focal, functionally specific connectivity, consistent with the notion that vagal modulation facilitates flexible reconfiguration of large-scale brain networks. In other words, active taVNS may push the brain toward a higher state of cognitive readiness, reducing reliance on large-scale delta synchrony and enabling more targeted, efficient neural communication. None of these regional effects met the Bonferroni-adjusted significance threshold, however, and no differences appeared in theta, alpha, beta, or gamma bands, or in any band during the eyes-open condition.

Secondary analyses revealed suggestive mood effects. The active group showed an estimated reduction in depression and tension scores from pre to post stimulation compared with the sham group, and delta-band clustering coefficients in left frontal, central, parietal, and occipital regions were positively correlated with depression improvement in the active group, though neither the mood differences nor the correlations survived correction. Notably, heart rate variability showed no clear changes during or after stimulation, aligning with a meta-analysis that found no significant effect of taVNS on vagally mediated HRV. Because auricular taVNS primarily engages afferent rather than efferent vagal pathways, its cognitive effects may be mediated predominantly by central mechanisms such as locus coeruleus-noradrenergic activation without producing detectable peripheral cardiac changes, a central-peripheral dissociation that challenges the use of HRV as a reliable biomarker for these interventions.

The authors are careful to frame their findings as exploratory and hypothesis-generating. The study lacked a pre-stimulation EEG baseline, limiting interpretation to between-group differences during the later stimulation period and precluding distinction between immediate and residual effects of the preceding training session. The quasi-experimental design, modest sample size, and sensitivity of the effects to covariate specification all warrant caution. Nevertheless, the convergence of improved adaptive training performance, reduced left-hemisphere delta connectivity, and suggestive mood benefits provides mechanistic plausibility for the idea that pairing vagal stimulation with continuously adaptive cognitive demands may maximize neuromodulatory effects by repeatedly engaging task-relevant cortical networks. The observed improvements may reflect enhanced neural efficiency emerging from the interaction between vagally mediated neuromodulation and adaptive cognitive training.

What makes this study compelling is its attempt to move beyond the narrow paradigms that have limited the field, evaluating multiple dimensions of executive function while capturing network-level modulation in real time. The divergent effects on adaptive versus static cognitive performance suggest that taVNS-mediated benefits may depend critically on task demand and complexity, a distinction supported by recent literature showing that taVNS effects on cognitive flexibility emerge specifically under high task complexity. If future studies with prespecified outcomes, theoretically justified covariates, and adequate statistical power replicate these patterns, the implications could extend from cognitive enhancement in healthy adults to clinical applications, given preclinical evidence that vagus nerve stimulation facilitates extinction learning and that taVNS ameliorates memory impairment in animal models. For now, the message is one of cautious optimism: a gentle pulse to the ear may indeed tune the brain, but the evidence is still finding its frequency.

Subject of Research: Neuroscience

Article Title: A non-randomized trial of the effects of transcutaneous auricular vagus nerve stimulation on cognitive performance and resting state connectivity

Article References: Filbey, F. M., Short, C. T., Leazure, A., Nekoui, N. E., Brown, T., Kroener, S., & Liu, C. (2026). A non-randomized trial of the effects of transcutaneous auricular vagus nerve stimulation on cognitive performance and resting state connectivity. Frontiers in Human Neuroscience, 20, Article 1801066. https://doi.org/10.3389/fnhum.2026.1801066

Image Credits: AI Generated

DOI: 10.3389/fnhum.2026.1801066

Keywords: taVNS, vagus nerve stimulation, neuromodulation, executive function, EEG, delta band, BrainHQ, cognitive training, fronto-parietal network, neural efficiency, locus coeruleus, mood

The clinical backdrop explains why the work matters. Autism spectrum disorder affects a substantial and growing share of children worldwide, yet the gold-standard diagnostic pathway still depends on behavioral observation and caregiver interviews, most notably instruments such as the Modified Checklist for Autism in Toddlers, or M-CHAT, and the Autism Diagnostic Observation Schedule-2, known as ADOS-2. These tools are effective in trained hands, but they are slow, expensive, and heavily dependent on the child’s willingness and ability to cooperate, something that is notoriously difficult in toddlers, the very age group in which early intervention delivers its greatest benefit. In resource-limited settings, where specialist clinicians are scarce and waiting lists stretch for years, many children reach school age without ever receiving a formal diagnosis. The research team, led by Juan Flores and Andrea E. Montero of Universidad Peruana Cayetano Heredia in Lima, together with collaborators at Johns Hopkins Bloomberg School of Public Health, Yale Child Study Center, and other institutions, set out to test whether a biological measurement that requires almost nothing from the child could help close that gap.

Their approach rests on a well-established body of prior evidence. Earlier studies had already reported atypical PLR characteristics in individuals with autism, including abnormal transient pupillary responses documented as far back as 2009, and enhanced pupillary light reflex in infants who were later diagnosed with autism in toddlerhood, a finding reported in Nature Communications in 2018. A systematic review and meta-analysis published in 2021 concluded that pupillometry shows consistent group differences between autistic and non-autistic individuals, though with heterogeneous methods across studies. What remained missing was a practical, low-cost pipeline that could capture and quantify the reflex in very young children using nothing more exotic than a video camera and careful image processing, and then test whether the resulting numbers could discriminate diagnosed children from typically developing ones in a real clinical screening context.

The study enrolled 30 children between the ages of two and five: 14 with a confirmed diagnosis of autism spectrum disorder, three of them girls, and 16 typically developing peers, ten of them girls. The diagnoses were not taken at face value. Each child in the autism group was evaluated with the M-CHAT screening questionnaire and the ADOS-2, the observational assessment widely regarded as a clinical reference standard, to confirm that the classification was sound. The pupillary testing itself was deliberately minimal. Researchers recorded five-second videos of each child’s eyes while delivering a controlled light stimulus, a protocol short enough that even a restless toddler could tolerate it. From those recordings, the team extracted three quantitative features of the reflex: the degree of pupillary constriction, meaning how much the pupil shrank in response to the light; the latency, meaning how long it took for constriction to begin; and the time to maximum contraction, meaning how quickly the pupil reached its tightest point.

The image-processing pipeline behind those measurements is where the engineering becomes interesting. Extracting a reliable pupil diameter from video frames of a moving child’s face is a nontrivial computer vision problem. The researchers drew on classical techniques that are foundational to the field, including the Canny edge detection algorithm for locating boundaries in each frame, direct least-squares ellipse fitting for modeling the pupil’s shape, the Viola-Jones cascade framework for detecting facial regions, and RANSAC-based robust model fitting to reject outlier measurements caused by blinks, head movements, or partial occlusion. Signal-processing steps, including Savitzky-Golay smoothing of the diameter time series, cleaned up the raw traces before the characteristic parameters of the reflex were computed. The result is a system that converts a short video clip into a compact set of numbers describing the dynamics of an involuntary neural reflex, without requiring any specialized pupillometer hardware beyond a camera and a light source.

The headline result concerns the degree of constriction. Children with autism showed a significantly greater degree of pupillary constriction in response to the light stimulus than their typically developing peers, a difference that reached statistical significance in the quantitative analysis. The other two features, latency and time to maximum contraction, were shorter in the autism group on average, but these differences rose only to the level of statistical trends rather than definitive effects. The direction of the finding is consistent with prior work suggesting enhanced or exaggerated pupillary light responses in autistic children, which some investigators have linked to atypical functioning of the locus coeruleus-norepinephrine system, the brain’s principal arousal circuitry. If the pupil of a child with autism clamps down harder and faster under the same light, that overshoot may reflect a nervous system tuned differently, and potentially measurable, long before behavioral symptoms can be reliably scored by a clinician.

To test whether those differences could translate into an actual screening decision, the team built an exploratory logistic regression model using the pupillary features as predictors of diagnostic status. The in-sample performance was remarkable: the model achieved a sensitivity of 93 percent, meaning it correctly identified nearly all of the children with autism, and a specificity of 86 percent, meaning it correctly cleared most of the typically developing children. The area under the receiver operating characteristic curve, a standard summary of discriminative power that ranges from 0.5, equivalent to a coin flip, to 1.0, perfect classification, came in at 0.93. Those are numbers that, if they held up in the real world, would place pupillometry among the more accurate screening tools available for early autism detection.

But the authors are careful, and rightly so, about what those numbers do and do not mean. The performance estimates were obtained without external validation or cross-validation, meaning the model was evaluated on the same 30 children from which it was built. In machine learning terms, the model may be overfitted to this particular sample, and its true performance on new, unseen children from different populations could be considerably lower. The sample was also small, with only 14 autistic children, and skewed toward girls in the typically developing group, which limits how broadly the findings can be generalized. These caveats do not undermine the proof of concept, but they mark the distance between a promising pilot and a deployable clinical tool. Larger, preregistered studies with independent validation cohorts, ideally across multiple sites and demographic groups, are the necessary next step before any claim of diagnostic utility can be made responsibly.

Even with those limitations, the practical appeal of the approach is hard to overstate. Pupillometry demands almost no cooperation from the child, unlike behavioral assessments that require sustained social engagement, which is precisely the capacity that autism affects. The equipment is inexpensive, the capture takes seconds, and the analysis can be automated, making the technique attractive for primary care clinics, community health posts, and low-resource settings where traditional diagnostic facilities are scarce. The researchers frame it as a complement to, not a replacement for, comprehensive clinical evaluation: a fast, objective first filter that could flag children who need full diagnostic workup, shortening the path from first concern to intervention during the developmental window when therapy has the greatest impact. For a condition in which every month of delayed diagnosis matters, a five-second video of a toddler’s eye responding to a flash of light is an unusually elegant place to start.

Subject of Research: Pupillary light reflex measurement as a potential biological screening tool for autism spectrum disorder in young children

Article Title: Analysis of the Pupillary Light Reflex Response Against Light Stimuli for Autism Screening in Children

Article References: Flores, J., Montero, A. E., Barrientos, F., Zavaleta, R., Dioses, A., Noazin, S., Powers, M., Gilman, R. H., Roman-Gonzalez, A., & Zimic, M. (2026). Analysis of the Pupillary Light Reflex Response Against Light Stimuli for Autism Screening in Children. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07501-2

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07501-2

Keywords: autism spectrum disorder, pupillometry, pupillary light reflex, early screening, M-CHAT, ADOS-2, toddlers, logistic regression, biomarkers, computer vision, locus coeruleus, resource-limited settings

The core problem Kafkas identifies is one that has frustrated cognitive neuroscientists for years. Animal studies have delivered exquisitely detailed accounts of how dopamine and norepinephrine shape hippocampal plasticity, influencing long-term potentiation, memory persistence and the prioritisation of behaviourally significant events. Work on reward, novelty and surprise suggests that catecholaminergic modulation is not one unitary arousal response but a family of state-dependent mechanisms through which the brain selects experiences for long-term storage. Yet translating those mechanistic insights into human research remains strikingly difficult, because direct measures of endogenous catecholaminergic activity in a living human brain are nearly impossible to obtain during the fleeting seconds in which memories are actually formed.

Existing tools each capture only a partial slice of the underlying biology. Positron emission tomography offers receptor- and transporter-specific measurements, but its temporal resolution is poor, it is costly and it involves radiation exposure. Neuromelanin-sensitive magnetic resonance imaging reveals the structural integrity of catecholaminergic nuclei, most famously the locus coeruleus, the brain’s principal norepinephrine factory, but says nothing about their moment-to-moment functional dynamics. Pharmacological manipulations can establish causal links between neurotransmitters and cognition, but they lack anatomical specificity and are confounded by systemic effects scattered throughout the body. What the field needs, Kafkas argues, is a biomarker that is non-invasive, inexpensive, temporally sensitive, easy to combine with imaging and modelling, and capable of tracking trial-by-trial fluctuations in neuromodulatory state.

Pupillometry ticks every one of those boxes, and an expanding literature shows that pupil dynamics genuinely matter for memory. Encoding-related pupil responses predict later recognition, memory strength, associative learning and mnemonic discrimination across a wide range of paradigms. They even distinguish qualitative features of remembering, separating recollection from familiarity, novelty from known, and in one striking study true memories from false ones. These are not trivial byproducts of effort or alertness, Kafkas contends, but physiological signatures associated with whether an experience gets encoded at all. Crucially, pupil responses also track the computational variables at the heart of hippocampal learning: novelty, expectation and prediction error all modulate pupil behaviour during encoding, and those modulations predict different subsequent memory outcomes.

The provocative part of the new framework is its refusal to treat the pupil as a narrow proxy for locus coeruleus activity. Pupillary responses emerge through the coordinated action of sympathetic and parasympathetic pathways and are shaped by distributed cortical, subcortical and brainstem circuits. Recent simultaneous pupillometry-fMRI evidence supports that broader view. During incidental encoding, pupil responses covaried with hippocampal and parahippocampal activity and predicted later recognition, and successful encoding was associated with pupil-related functional interactions between the hippocampus, the Edinger-Westphal nucleus, the ventral striatum and the orbitofrontal cortex. Pupil-linked memory effects, in other words, ride on a network spanning hippocampus, cortex and brainstem, not on a single neuromodulatory nucleus. Kafkas is careful about causality: these associations do not establish the direction of influence, and they may reflect direct interactions or common upstream drives, or both.

The framework’s boldest proposal tackles an apparent contradiction in the literature. Many studies report that larger pupil dilations during encoding predict successful memory. Others have found that encoding-related pupil constriction can predict memory too, under specific conditions. Rather than dismissing one set of findings, Kafkas suggests that constriction and dilation may index genuinely different physiological states recruited by different encoding demands. In an expectation-learning experiment, unexpected novel stimuli produced steadily increasing dilation and preferentially boosted later recollection, while expected novel stimuli elicited constriction yet still predicted successful recognition. Both routes led to memory, but through different physiological configurations.

Those two configurations map neatly onto two computational modes of hippocampal learning. When new information broadly fits existing knowledge, successful encoding may depend on integrating it into established representations, a state that plausibly leans more on parasympathetic and autonomic brainstem processes, consistent with the link between expected novelty, constriction and hippocampal connectivity with the Edinger-Westphal nucleus, the parasympathetic hub controlling the pupil’s constrictor muscle. When events violate predictions and demand revision of internal models, encoding may instead rely more heavily on catecholaminergic hippocampal-midbrain interactions. Here dilation during surprise reflects rapid memory updating rather than mere arousal, an idea compatible with the GANE model, in which norepinephrine amplifies high-priority neural representations under salient conditions, but extending it to hippocampal-midbrain network states. Animal work adds a further nuance: rapid pupil fluctuations track noradrenergic activity closely, whereas sustained dilation during locomotion tracks cholinergic activity instead. One pupil response, multiple possible neuromodulatory backdrops.

Turning the framework into hard science will require multimodal and, ultimately, causal tests, and Kafkas lays out a concrete experimental agenda. High-resolution fMRI can relate trial-by-trial pupil fluctuations to hippocampal subfields and network connectivity. Neuromelanin-sensitive MRI can index individual differences in catecholaminergic nuclei, despite ongoing challenges in accurately localising the locus coeruleus. Pharmacological studies can test whether cholinergic, dopaminergic and noradrenergic manipulations differentially alter constriction- and dilation-linked memory states. Magnetic resonance spectroscopy can probe the excitatory-inhibitory balance of the hippocampus, and autonomic measures such as heart-rate variability and skin conductance can help tease apart sympathetic and parasympathetic contributions. Computational modelling supplies the final ingredient: instead of simple remembered-versus-forgotten contrasts, future studies should estimate latent variables like surprise, uncertainty and belief updating on each trial, then relate them simultaneously to pupils, hippocampal activity and later memory quality.

The falsifiable predictions are explicit. Manipulating expectation while holding stimulus novelty constant should shift encoding-related pupil responses between constriction-dominated and dilation-dominated profiles, and those shifts should be accompanied by changes in hippocampal-midbrain connectivity rather than locus coeruleus activation alone. Drug studies should perturb the two profiles differently if they truly reflect distinct neuromodulatory states. The framework would falter, by contrast, if a single arousal process could explain everything, or if the dissociations failed to replicate beyond the small set of paradigms that currently motivate them. Kafkas acknowledges this limitation candidly, calling for independent replication and cross-species work in which circuit-level manipulations could determine whether pupil profiles are causally tied to specific neuromodulatory mechanisms or merely parallel consequences of broader physiological states.

The stakes extend well beyond the laboratory. Altered catecholaminergic signalling and hippocampal dysfunction are central features of healthy ageing, Alzheimer’s disease and several neuropsychiatric conditions, and recent evidence links pupil dynamics to functional network integrity in ageing and to Alzheimer’s disease biomarkers. Because pupillometry is cheap, non-invasive and endlessly repeatable, it could help characterise individual differences in neuromodulatory function during memory formation, not as a standalone diagnostic but as one layer in multimodal profiles combining memory performance, network integrity and disease progression. Whether every specific hypothesis survives testing or not, the reframing itself may prove durable: by treating the pupil as a systems-level marker of integrated neuromodulatory, autonomic and hippocampal processes, the field gains a scalable route for connecting molecular mechanisms of dopamine and norepinephrine to the human memories that depend on them, across health, ageing and disease.

Subject of Research: Pupillometry as a systems-level biomarker of neuromodulatory influences on human hippocampal memory

Article Title: Beyond the locus coeruleus: pupil dynamics as a systems-level marker of neuromodulatory influences on hippocampal memory

Article References: Kafkas, A. (2026). Beyond the locus coeruleus: pupil dynamics as a systems-level marker of neuromodulatory influences on hippocampal memory. Frontiers in Molecular Neuroscience, 19, Article 1976085. https://doi.org/10.3389/fnmol.2026.1976085

Image Credits: AI Generated

DOI: 10.3389/fnmol.2026.1976085

Keywords: pupillometry, hippocampus, dopamine, norepinephrine, locus coeruleus, episodic memory, neuromodulation, novelty, prediction error, memory encoding, autonomic nervous system, fMRI

The theory enters a crowded field. Global Neuronal Workspace Theory holds that conscious access corresponds to a non-linear ignition of fronto-parietal networks that broadcasts information widely across the brain. Integrated Information Theory characterizes consciousness in terms of irreducible integrated causal structure, quantified as phi, with the Perturbational Complexity Index serving as an empirically validated perturbational measure of cortical complexity. The Predictive Processing framework and Free Energy Principle describe the brain as a hierarchical inference engine minimizing prediction error, with neuromodulatory systems regulating the precision of ascending signals. Each framework has generated substantial empirical progress, yet López Parra identifies a shared blind spot: none of them formalizes autonomic and neuromodulatory state as explicit dynamic control parameters within models of conscious integration. That gap, he argues, is precisely where EFT belongs.

The core architecture of EFT organizes consciousness-supporting integration across three nested temporal regimes. The first, labeled ΔTi, spans roughly 50 to 200 milliseconds and is autonomic-interoceptive in character — a preparatory phase in which cardiovascular, respiratory, and gastrointestinal afferent signals are synthesized into a coherent representation of bodily state that shapes the gain conditions for subsequent cortical processing. The second regime, ΔTp, spans roughly 200 to 500 milliseconds and corresponds to conscious access itself: the non-linear, self-amplifying ignition cascade across fronto-parietal workspace neurons familiar from Global Neuronal Workspace Theory. The third, ΔTf, extends from about 500 milliseconds to 3 seconds and is phenomenological-narrative — the period during which globally accessed information is contextualized within episodic memory, a temporal self-model, and narrative comprehension, anchored in the Default Mode Network. These regimes are explicitly not rigid compartments but overlapping dominant processing epochs within a continuous dynamical system, with boundaries that vary across individuals, tasks, and physiological states.

The theory’s boldest move is its formalization of autonomic coherence, denoted gamma, as a dimensionless latent gain parameter modulating the probability and quality of transitions between these regimes. Operationally, gamma is estimated through a normalized heart rate variability measure: raw RMSSD — a standard beat-to-beat HRV metric — divided by an individual’s own resting baseline. The relationship is captured in a phenomenological dynamic equation for a latent integrated-complexity state Q: the rate of change of Q equals a driving term in which gamma scales the effective integration rate at every node-regime pair, minus a dissipation term reflecting the metabolic cost of sustained integration, plus additive stochastic noise. The equation is dimensionally closed and stated entirely in latent theoretical quantities, with the Perturbational Complexity Index treated as an observational constraint on Q rather than an identity — a distinction the author maintains in exact structural parallel to the gamma-versus-gamma-hat separation.

Why should cardiac variability matter for cortical integration? The proposed routing pathway runs from the nucleus tractus solitarius, the brainstem’s primary relay for vagal afference, through the parabrachial nucleus to the locus coeruleus, the brain’s principal noradrenergic nucleus. The author is careful to distinguish evidentiary layers here: the individual rodent-documented links are supported by tract-tracing work, while the proposal that they compose a functional gain-setting axis in humans is EFT’s own Tier-2 synthesis. From the locus coeruleus, the account draws on the adaptive gain model of Aston-Jones and Cohen, in which phasic noradrenergic bursts amplify responses to task-relevant signals while tonic-high activity degrades signal-to-noise discrimination. Computational and neuroimaging evidence independently supports the general principle: whole-brain information-processing dynamics have been shown to transition from storage-dominated to transfer-dominated states as neural gain increases, with a critical transition at intermediate gain values, and phasic neuromodulatory bursts indexed by pupil diameter coincide with abrupt large-scale network reconfigurations during perceptual switches.

EFT also connects its autonomic precondition to the cortical machinery of temporal segmentation. The framework is convergent with the oscillatory syntax perspective, which holds that the brain segments continuous time into discrete processing windows through hierarchically nested rhythms, coordinated by phase-amplitude coupling between slow theta oscillations and fast gamma activity. EFT proposes, as a Tier-2 mechanistic elaboration, that autonomic coherence modulates cortical gain through locus coeruleus-norepinephrine influences on excitatory-inhibitory balance and gamma-generating parvalbumin-positive interneuron networks. In the salience network — anchored by the anterior insula and dorsal anterior cingulate — the theory locates a state-dependent routing interface that may bias processing toward the executive network under optimal gain conditions, or toward subcortical threat-responsive circuits under hyperarousal. A putative top-down feedback loop, running from ventromedial prefrontal cortex through amygdala GABAergic intercalated cells to restore ventral vagal tone, is supported by meta-analytic evidence of consistent HRV associations with the amygdala and ventromedial prefrontal cortex, though the specific cellular mechanism remains untested.

What elevates EFT above armchair theorizing is its commitment to falsifiability. Five empirical predictions are derived with explicit adjudication criteria, including pre-registered equivalence testing that distinguishes genuine evidence against a prediction from merely underpowered results. Prediction P1 targets the covariation between the HRV-derived estimator and the Perturbational Complexity Index in within-subjects designs using transcranial magnetic stimulation and EEG. Prediction P2 tests whether transcutaneous vagus nerve stimulation enhances conscious access markers such as P3b amplitude and gamma synchrony. Prediction P3 examines whether acute stress disrupts the phenomenological-narrative regime, measurable through theta-gamma phase-amplitude coupling and narrative coherence. Prediction P4 is split into a falsifiable existence test — whether any systematic relationship between the observable estimator and the observable proxy exists at all — followed by a conditional model discrimination between a monotonic saturating relationship and a peaked inverted-U profile consistent with criticality accounts. Prediction P5 probes causal contributions of the salience network through disruption paradigms.

The author’s intellectual honesty is notable throughout. The framework is explicitly labeled Tier-2: grounded in and consistent with a substantial empirical literature, but requiring direct experimental validation of its integrated claims. The master equation, in its present first-order form, generates only a monotonic relationship between gain and integration; if the peaked inverted-U model were empirically favored, the equation itself — not merely its interpretation — would require extension. Methodological constraints are acknowledged candidly: HRV requires averaging windows of 30 seconds to 5 minutes, a discrepancy of at least three orders of magnitude with the 50-millisecond interoceptive window, which is why gamma is defined as a tonic baseline parameter rather than an instantaneous multiplier. No validated biomarker currently exists to dissociate the proposed brainstem gain-setting route from the independently established thalamo-insular interoceptive relay in human vagal stimulation studies.

Indirect clinical anchors lend the proposal plausibility. In 127 patients with disorders of consciousness, cardiac autonomic markers — including stimulus-related cardiac-cycle modulations elicited during an auditory oddball paradigm — improved data-driven classification between vegetative and minimally conscious states when combined with EEG complexity markers. EEG-derived criticality measures have been shown to predict perturbational complexity with mean error below 7 percent across propofol, xenon, and ketamine anesthesia states, though a preprint reports these features did not generalize to predicting maximal perturbational complexity in disorders of consciousness. None of this convergent evidence directly tests EFT’s specific claims; each anchor is offered at the level of plausibility and motivation, not confirmation.

If the predicted relationships between autonomic coherence, neuromodulatory gain, and the temporal architecture of cortical integration are confirmed, EFT will have provided substantive empirical support for a functional bridge between the body’s regulatory physiology and the dynamics of conscious access. The framework is offered as complementary to — not a replacement for — Global Neuronal Workspace Theory, Integrated Information Theory, and Predictive Processing, situating autonomic coherence as a proposed physiological precondition within which the integrative capacities described by those frameworks may be realized. Whether the heartbeat truly sets the gain on consciousness is now an empirically adjudicable question, and the tools to answer it — psychophysiology, TMS-EEG, non-invasive neuromodulation, and causal disruption paradigms — are already on the laboratory bench.

Subject of Research: A theoretical framework proposing that autonomic coherence, indexed by heart rate variability, functions as a gain parameter modulating the temporal architecture of conscious cortical integration.

Article Title: Emergent flow theory: autonomic coherence as a gain parameter for the temporal architecture of conscious integration

Article References: Parra, A. L. (2026). Emergent flow theory: autonomic coherence as a gain parameter for the temporal architecture of conscious integration. Frontiers in Integrative Neuroscience, 20, Article 1841714. https://doi.org/10.3389/fnint.2026.1841714

Image Credits: AI Generated

DOI: 10.3389/fnint.2026.1841714

Keywords: consciousness, autonomic coherence, heart rate variability, Emergent Flow Theory, locus coeruleus, Perturbational Complexity Index, salience network, temporal architecture, Global Neuronal Workspace Theory, Integrated Information Theory, predictive processing, vagus nerve stimulation

Inner speech, the act of speaking silently in one’s mind, is a cornerstone of human cognition. We use it to plan our day, rehearse conversations, regulate our emotions, and wrestle with difficult decisions. For decades, neuroimaging studies have shown that inner speech draws on a fronto-temporal speech-production network that partly overlaps with, but is not identical to, the network engaged during overt speech, including regions such as the left inferior frontal cortex, the supplementary motor area, the insula, and superior temporal areas. Depending on the region and task examined, inner speech can produce weaker, comparable, or even greater cortical activation than spoken speech. Yet nearly all of this work has focused on the cortex itself, treating speech-production networks as if they operated in isolation from the systems that regulate their functional state.

The Japanese team, led by Ryuta Kuwamizu, took a different approach. Rather than asking which cortical regions light up during inner versus overt speech, they asked whether those regions operate in the same neuromodulatory state during the two forms of speech. To answer this, they turned to pupillometry, the continuous, non-invasive measurement of pupil diameter. The pupil is a remarkably faithful proxy for brain arousal: it tracks the coordinated activity of the locus coeruleus-noradrenergic system, the cholinergic basal forebrain, the hypothalamic orexin system, and the large-scale cortical network states these systems govern. Crucially, unlike EEG, fMRI, or facial electromyography, pupillometry remains robust during overt articulation and requires no head-mounted apparatus, making it uniquely suited for directly comparing silent and spoken speech within a single paradigm.

The researchers designed an elegant counting task. Across three experiments, a total of 80 healthy, native Japanese-speaking participants sat before a screen-based eye tracker and completed blocks of three conditions: counting aloud, counting silently in their heads, and an instructed no-thought control in which they attempted to think of nothing at all. Each block consisted of a 30-second rest period followed by a 45-second task period, with pupil diameter recorded continuously at 60 Hz. In the first experiment, participants simply counted upward from one. In the second, they counted using grammatically structured numeral-noun phrases in Japanese, such as one cat, two cats, adding phonological and morphological complexity. In the third, they performed step counting in increments of two, three, or four, sharply increasing demands on working memory and cognitive control.

The results upended the team’s initial hypothesis. They had expected inner speech to produce pupil dilation comparable to overt speech, reflecting the cognitive effort of covert verbal production. Instead, in the simple counting experiment, inner speech produced significant pupil constriction relative to baseline, while overt speech produced significant dilation. Inner speech even drove the pupil smaller than the no-thought condition did. In the second experiment, the constriction vanished: inner speech no longer differed from baseline, while overt speech again produced robust dilation. Only in the third experiment, under the highest cognitive load, did inner speech finally produce clear pupil dilation, converging toward, but never reaching, the level seen during spoken counting.

The most robust finding across all three experiments was the consistent dissociation between the two speech modes. Overt speech reliably elicited pupil dilation in every condition, whereas inner speech was always accompanied by lower pupil-linked arousal, even as its direction relative to baseline shifted from constriction to no change to dilation as task demands grew. The researchers verified that participants genuinely engaged in inner counting by comparing final counts across conditions: counting speed correlated strongly between overt and inner speech in every experiment, with Spearman correlations ranging from 0.85 to 0.92. In the noun-phrase experiment, participants actually counted to higher numbers during inner speech, consistent with evidence that silent speech can proceed faster than spoken speech.

The magnitude of the overt-speech effect is itself remarkable. In the step-counting experiment, the sustained pupil dilation reached approximately 0.4 millimeters for more than 10 seconds, a response comparable in size to those reported during high-intensity cycling at roughly 80 percent of maximal oxygen uptake. In other words, speaking aloud under cognitive load drives pupil-linked arousal on a scale similar to vigorous physical exertion. This likely reflects the integrated demands of motor execution, respiratory variation, sensorimotor feedback, and attention that vocalization entails, each of which engages brainstem arousal systems and cortical network states.

The authors propose a state-dependent account of inner speech. On this view, inner and overt speech share much of the same linguistic machinery, but they run that machinery in different neuromodulatory states. Overt speech requires transforming an internal linguistic representation into an externally executable sensorimotor format, complete with articulatory execution, respiratory coordination, and auditory feedback monitoring against an efference copy of the motor command. These demands may recruit stronger noradrenergic gain modulation of speech-related auditory-motor networks, whereas inner speech can often remain in a compressed representational format and operate at lower gain. Notably, inner speech arousal scaled with cognitive-control demand rather than linguistic complexity per se: enriching the verbal content alone did not raise arousal, but forcing participants to update and maintain running sums did. This may explain why neuroimaging studies have reported such mixed cortical differences between covert and overt speech; the shared networks may simply be operating at different arousal states across studies and tasks.

The implications stretch well beyond the laboratory. Because pupillometry is contactless and silent, it could be used to study how inner speech becomes internalized over childhood development, and in populations such as young children or certain clinical groups for whom overt reporting or sensor-based recording is difficult. The capacity of inner speech to sustain verbal processing at reduced arousal may be advantageous where mental calm and efficient cognition must coexist, as in cognitive control, sport performance, or the routinized inner counting many people use as a sleep aid. Yet the same low-arousal efficiency might also enable maladaptive cognitive loops that contribute to rumination. Conversely, the elevated arousal accompanying overt speech may carry functional value; prior work has found that pupil dilation during reading aloud better tracks the memory advantage for spoken words than for silent ones.

The researchers are careful to note the limits of their work. The study operationalized inner speech as silent numerical counting, an externally instructed, repetitive task that cannot capture the full diversity of naturally occurring inner speech, from dialogic self-conversation to spontaneous verbal thought. The no-thought condition was an instructed control rather than verified mental silence, and pupillometry provides only an indirect index of arousal, not a direct readout of inner speech itself. The sex ratios also differed across the three experiments, warranting caution in cross-experiment comparisons. Still, the authors frame the work as a focused first step toward a neuromodulatory science of verbal thought. If the pattern holds in more naturalistic settings, the little voice in our heads may turn out to be defined not just by what it says, but by the quiet, energy-efficient state in which it says it, a whisper that runs the brain’s arousal machinery on a fraction of the power demanded by the spoken word.

Subject of Research: Pupil-linked arousal differences between inner speech and overt speech

Article Title: Dissociable pupil-linked arousal during overt and inner speech

Article References: Kuwamizu, R., Watanabe, R., Yamamoto, N., Otani, K., & Moriguchi, Y. (2026). Dissociable pupil-linked arousal during overt and inner speech. iScience, 29(11), Article 117825. https://doi.org/10.1016/j.isci.2026.117825

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117825

Keywords: inner speech, overt speech, pupillometry, arousal, locus coeruleus, noradrenergic system, cognitive load, counting task, neuromodulation, working memory, self-talk, brain state

The study drew on 461 participants from the Wisconsin Sleep Cohort, a community-based longitudinal project that has tracked sleep health in Wisconsin adults since the late 1980s. For this analysis, the researchers used data collected between 2019 and 2023, when participants first completed the Sleep Inertia Questionnaire, a validated 23-item instrument that probes the cognitive, behavioral, emotional, and physiological dimensions of the sleep-to-wake transition. Participants averaged 73.8 years of age, were slightly more likely to be male, and were predominantly non-Hispanic white with at least some college education. Crucially, the cohort’s decades of infrastructure meant the researchers could adjust their statistical models for an unusually rich set of confounders, including body mass index, depression scores, alcohol and caffeine use, smoking history, chronotype, habitual sleep duration, and objectively measured sleep apnea severity from overnight polysomnography.

Cognitive performance was assessed with a standardized battery of six widely used neuropsychological tests, yielding seven outcome measures. The battery included the Rey Auditory Verbal Learning Test for memory, the Grooved Pegboard for fine motor speed and dexterity, Trail Making Test Part B for executive function and cognitive flexibility, the Symbol Digit Modalities Test for processing speed, the Controlled Oral Word Association Test for verbal fluency, and the Digit Cancellation Test for attention. Testing sessions lasted roughly 35 minutes and were deliberately scheduled during typical waking hours—usually late morning, mid-afternoon, or evening—rather than immediately upon awakening, with testing beginning an average of nearly 11 hours after participants reported waking on the day of assessment.

The results were striking in their selectivity. Higher scores on the Sleep Inertia Questionnaire were significantly associated with slower completion times on the Grooved Pegboard and Trail Making Test Part B, and with fewer correct responses on the Symbol Digit Modalities Test in unadjusted analyses. After the researchers statistically controlled for the full panel of demographic, psychosocial, and sleep-related covariates, the associations with the Grooved Pegboard and Trail Making Test Part B remained robust. These two tasks share a common thread: both depend heavily on psychomotor speed and executive functioning, suggesting that sleep inertia may be most closely tied to the brain’s capacity for rapid, coordinated, goal-directed processing rather than to memory or language abilities.

Perhaps the most intriguing finding was what did not predict cognition. The Epworth Sleepiness Scale, the gold-standard self-report measure of daytime sleepiness, showed no significant associations with any cognitive outcome. The Hypersomnia Severity Index, a broader measure of hypersomnia symptoms and impairment, produced only a single weak association—and it ran in a seemingly paradoxical direction, with greater hypersomnia severity linked to slightly better immediate verbal recall. The contrast implies that sleep inertia is not simply another face of general sleepiness. Instead, it appears to be a specific symptom with its own relationship to brain function, one that broader somnolence questionnaires may dilute or miss entirely.

Secondary analyses of the questionnaire’s four subscales sharpened the picture. The physiological, cognitive, and emotional subscales all showed significant associations with cognitive performance, particularly on the Grooved Pegboard and Trail Making Test Part B, with the physiological and cognitive subscales also linked to slower performance on the Digit Cancellation Test and reduced output on the Symbol Digit Modalities Test. The responses subscale, which captures behaviors such as waking to an alarm, showed no significant relationships—a pattern the authors attribute in part to the fact that most participants were retired and less likely to encounter the situations those items describe. Sensitivity analyses confirmed that the findings held when accounting for APOE4 carrier status, a major genetic risk factor for Alzheimer’s disease, and when excluding the small number of night-shift workers in the sample.

Why would the severity of morning grogginess relate to cognitive performance hours later? The biology of sleep inertia remains incompletely understood, but leading hypotheses center on the neural machinery of the sleep-to-wake transition itself. Awakening is not a single switch but a staggered cascade: brainstem arousal systems activate rapidly, while cortical regions—especially prefrontal areas supporting executive function—come online more slowly, and the functional segregation between task-positive and task-negative brain networks is temporarily lost. Proposed contributors include the slow clearance of sleep-promoting substances such as adenosine after waking, disrupted cortical arousal, circadian misalignment, and awakening from slow-wave sleep or during the biological night, all of which can intensify inertia. Pathologically severe sleep inertia, as seen in idiopathic hypersomnia, may reflect dysregulation of these transition mechanisms.

The connection to neurodegeneration is speculative but biologically plausible, and it is where the study becomes genuinely provocative. The locus coeruleus, the brain’s primary noradrenergic nucleus, is a key regulator of transitions between sleep and wakefulness and between REM and non-REM sleep—and it is among the earliest sites of pathological tau accumulation in Alzheimer’s disease, becoming hypoactive during preclinical stages. Impaired monoaminergic neurotransmission upon awakening could, in theory, mediate prolonged episodes of sleep inertia. Adding an intriguing circumstantial link, caffeine—an adenosine receptor antagonist—reduces sleep inertia in sleep-deprivation paradigms, increases locus coeruleus activity, and has been associated in epidemiological studies with diminished Alzheimer’s risk and better cognitive function. None of this establishes causation, but it sketches a coherent framework in which chronic, severe sleep inertia could serve as an early behavioral fingerprint of arousal-circuit vulnerability.

The authors are careful to frame the work as exploratory and preliminary. The cross-sectional design cannot determine whether sleep inertia contributes to cognitive decline, shares an underlying cause with it, or both. The sample, while large and well characterized, was predominantly white and non-Hispanic, limiting generalizability, and most cognitive testing occurred in the evening hours—though the models adjusted for hours since awakening, which the researchers argue makes it unlikely that the measured performance simply reflected acute grogginess during testing. The Sleep Inertia Questionnaire also relies on retrospective self-report, and because the analyses were exploratory, no correction for multiple comparisons was applied, leaving open the possibility that some associations reflect chance. Objective, ambulatory measures of sleep inertia and longitudinal designs in more diverse populations are the clear next steps.

Even with those caveats, the implications are tangible. Sleep inertia is a near-universal experience that typically dissipates within 30 minutes of waking, but its duration and intensity vary dramatically between individuals, and prolonged, debilitating inertia is a hallmark of idiopathic hypersomnia and a frequent complaint in obstructive sleep apnea, insufficient sleep syndrome, circadian rhythm disorders, and mood disorders. If replicated, the current findings suggest that a simple question about morning grogginess—how long it lasts, how severe it feels, how often it occurs—could be folded into clinical assessments of older adults as an inexpensive, readily obtainable signal of cognitive risk. They also raise the possibility that interventions designed to reduce sleep inertia, from optimized wake timing to pharmacological countermeasures, might one day help modify cognitive trajectories in aging. For now, the message is that the fog of waking up may be more than a nuisance: it could be a window into how the aging brain transitions between states of consciousness, and how well it will continue to perform.

Subject of Research: The association between sleep inertia severity and cognitive performance in older adults

Article Title: Association between sleep inertia and cognitive performance among older adults in the Wisconsin Sleep Cohort study

Article References: Love, J. J., Cook, J. D., Hagen, E. W., Rasmunon, A., Ravelo, L. A., Palta, M., Peppard, P. E., & Plante, D. T. (2026). Association between sleep inertia and cognitive performance among older adults in the Wisconsin Sleep Cohort study. Journal of Clinical Sleep Medicine, 22(1), Article 105. https://doi.org/10.1007/s44470-026-00133-4

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00133-4

Keywords: sleep inertia, cognitive performance, older adults, Wisconsin Sleep Cohort, hypersomnolence, executive function, psychomotor speed, neurocognitive testing, locus coeruleus, Alzheimer's disease, daytime sleepiness, sleep health

The theoretical backdrop for the study is arousal-biased competition theory, developed by Mara Mather and Mary Sutherland and colleagues. According to this framework, arousal increases the competitive advantage of whatever information already dominates mental processing. If a person is focused narrowly on a specific location, arousal should further strengthen that narrow focus; if attention is broadly distributed, arousal should broaden it further. The theory draws on neuroscientific work suggesting that norepinephrine, released by the locus coeruleus during states of heightened arousal, ignites local hotspots of neuronal excitation that amplify the most active representations while suppressing weaker competitors. Earlier experiments had reported that arousal amplifies selectivity in perception and memory, and classic studies from the 1950s through the 1970s, including work by John Easterbrook on cue utilization and by Donald Cornsweet on peripheral cues under arousal, had suggested that stress and noise narrow the useful field of view.

Kolnes and Nieuwenhuis set out to test this amplification hypothesis directly for the spatial dimension of attention. Thirty-nine participants performed a visual search task specifically designed to measure the breadth of attention across the visual field. The critical manipulation involved tonic arousal, the relatively stable background level of physiological activation that fluctuates over minutes rather than seconds. In half of the experimental blocks, continuous auditory white noise was played to participants, a well-established method for raising tonic arousal without startling them or triggering phasic alerting responses. The researchers reasoned that if arousal genuinely amplifies the dominant attentional scope, then the difference in search performance between narrow-attention and broad-attention conditions should grow larger under noise than under quiet conditions.

Before each trial, the experimenters used a procedure intended to induce either a narrow or a broad breadth of attention. This manipulation of attentional scope is central to the design, because the amplification hypothesis makes a specific prediction: arousal should not change the baseline scope itself, but should exaggerate whatever scope is currently dominant. The team also drew on their own earlier findings, including a 2023 study showing that broadening attention dilates the pupil, suggesting a close relationship between attentional breadth and the autonomic arousal system. That relationship made the null result they ultimately obtained all the more theoretically pointed, since it indicates that the coupling between arousal physiology and attentional breadth does not translate into arousal-driven amplification of scope.

A crucial strength of the study lies in its manipulation checks. The researchers measured pupil size continuously, exploiting the well-documented link between pupil diameter and locus coeruleus-norepinephrine activity. Pupil measurements confirmed that the white-noise manipulation successfully raised tonic arousal, and task performance confirmed that the scope-induction procedure successfully shifted the breadth of attention between narrow and broad states. In other words, the experiment was not undermined by failed manipulations. Both arousal and attentional scope were demonstrably altered, yet the predicted interaction between them simply did not appear. The effect of attentional breadth on visual search remained statistically indistinguishable between high-arousal and low-arousal blocks.

The analysis relied on Bayesian statistical methods, which allow researchers to quantify evidence in favor of a null hypothesis rather than merely failing to reject it. The authors report strong Bayesian evidence against an amplifying effect of arousal on the dominant attentional scope. This distinction matters in the ongoing debate about replication and statistical practice in psychology, where nonsignificant p-values are often ambiguous. A Bayes factor indicating strong support for the null hypothesis means the data actively favor the absence of an amplification effect, not simply that the study lacked power. The researchers also used mixed-effects models with maximal random effects structures, following methodological recommendations for confirmatory hypothesis testing, and conducted simulation-based power analyses to ensure the design was sensitive enough to detect a plausible amplification effect if one existed.

The null result is not without precedent. Ásgeirsson and Nieuwenhuis had previously reported no arousal-biased competition in focused visuospatial attention in a 2017 study published in Cognition, and a 2019 follow-up examining attention and short-term memory produced similarly mixed support for the theory. The new findings extend this line of work by showing that even when both arousal and attentional scope are successfully manipulated and verified through physiological and behavioral measures, the amplification predicted by arousal-biased competition theory fails to materialize for spatial attention. This convergence across independent experiments and paradigms suggests that the earlier classic findings of arousal-induced narrowing, such as those from noise studies in the 1970s, may reflect mechanisms other than amplification of dominant attentional scope, such as changes in response bias, strategy, or the relative salience of stimuli.

The authors discuss the implications for arousal-biased competition theory in measured terms. The theory remains influential and has generated a large body of research on how emotion and arousal shape perception and memory, including findings that emotional arousal strengthens high-priority memory traces while weakening low-priority ones. However, the present results suggest that the amplification principle may not generalize to the spatial scaling of attention. Alternative accounts of arousal’s effects, including models emphasizing adaptive regulation of arousal and the inverted-U relationship described by the Yerkes-Dodson law, may better accommodate the full pattern of findings. Recent work on pharmacologically shifting the peak of the Yerkes-Dodson curve through catecholaminergic enhancement indicates that arousal’s effects on cognition are complex and state-dependent, defying simple amplification rules.

For everyday intuition, the study suggests that being stressed, caffeinated, or startled does not automatically zoom your mental spotlight further in or out. The spatial distribution of attention appears to be governed by its own control mechanisms, relatively insulated from the background hum of bodily arousal. This dissociation has practical implications for domains ranging from driving safety to sports performance and the design of warning systems, where it is often assumed that high arousal inevitably produces tunnel vision. The evidence here indicates that assumption is too simple. The study was not preregistered, a limitation the authors acknowledge, but subject-level data and analysis code are openly available in the Open Science Framework repository, allowing other researchers to scrutinize and extend the findings. Funded by the Estonian Research Council and the Dutch Research Council, the work exemplifies how careful manipulation checks, physiological validation, and Bayesian inference can turn an apparently negative result into a substantive theoretical contribution about how attention and arousal are, and are not, connected.

Subject of Research: Whether tonic arousal modulates the spatial scope of visual attention

Article Title: Arousal does not enhance the dominant spatial scope of attention

Article References: Kolnes, M., & Nieuwenhuis, S. (2026). Arousal does not enhance the dominant spatial scope of attention. Attention, Perception, & Psychophysics, 88(7), Article 184. https://doi.org/10.3758/s13414-026-03327-3

Image Credits: AI Generated

DOI: 10.3758/s13414-026-03327-3

Keywords: arousal, attentional scope, visual attention, arousal-biased competition, pupil size, norepinephrine, visual search, Bayesian inference, psychophysics, locus coeruleus, breadth of attention, tonic arousal

The research, led by Bernard Mulvey, Heena R. Divecha, and colleagues at the Lieber Institute for Brain Development working with collaborators at Johns Hopkins and the University of Cambridge, applied spatially resolved transcriptomics, a technique that maps gene expression across intact tissue sections while preserving information about where each gene is active, to postmortem locus coeruleus tissue from 33 neurotypical middle-aged human brain donors. Crucially, the donors were stratified by major Alzheimer’s disease risk factors, including biological sex, African or European genetic ancestry, and apolipoprotein E haplotype, specifically carriers of the APOE E4 allele, which raises disease risk, versus carriers of the APOE E2 allele, which appears protective. This design allowed the investigators to ask a subtle but important question: do the genes expressed in and around the locus coeruleus already differ, in healthy middle age, depending on which Alzheimer’s risk variants a person carries?

The answer, in several respects, is yes, and the details are illuminating. When the researchers compared gene expression patterns across APOE haplotypes, they found reduced expression of astrocytic genes, meaning genes characteristically active in star-shaped support cells called astrocytes, in the tissue immediately surrounding locus coeruleus neurons in E4 carriers. Astrocytes are far more than passive glue; they regulate synapse formation, supply neurons with cholesterol and metabolic support, control local blood flow, buffer neurotransmitters, and respond to norepinephrine signaling from the locus coeruleus itself. A diminishment of astrocytic gene expression near these noradrenergic neurons suggests that E4 carriers may have subtly weakened local support infrastructure around one of the brain’s most Alzheimer’s-vulnerable cell populations, potentially lowering the threshold at which tau pathology or other stresses become damaging.

The study also uncovered ancestry-specific differences in locus coeruleus gene expression, a finding with real public health significance given well-documented disparities in dementia incidence and outcomes across populations. Follow-up analyses using in situ sequencing at single-cell resolution demonstrated that the APOE-related differences in regional gene expression were partly attributable to astrocytes themselves, and that the haplotype effects were more pronounced in donors of European ancestry. This interaction between genetic ancestry and APOE haplotype echoes earlier work showing that the relationship between APOE E4 and Alzheimer’s pathology differs across admixed populations, and it underscores the importance of including ancestrally diverse cohorts in neuroscience research rather than extrapolating from studies of predominantly European-ancestry subjects.

Perhaps the most visually and conceptually striking component of the study concerns neuromelanin, the dark pigment that gives the locus coeruleus its name, which means “blue spot” in Latin. Neuromelanin accumulates in certain catecholaminergic neurons, including the noradrenergic neurons of the locus coeruleus and the dopaminergic neurons of the substantia nigra, as a byproduct of oxidative metabolism of neurotransmitters. It is sequestered within specialized autolysosomal organelles that bind potentially toxic metals such as iron, and its loss from these neurons is a hallmark of both Alzheimer’s and Parkinson’s disease. Neuromelanin-sensitive magnetic resonance imaging has emerged as a promising biomarker, with reduced pigment signal in the locus coeruleus predicting clinical severity and future progression in Alzheimer’s patients. What has remained unclear is the precise molecular relationship between a neuron’s pigment content and its gene expression program.

To address this, the team quantified neuromelanin content directly in the tissue and related it to spatial gene expression. They found that higher APOE gene expression correlated with reduced neuromelanin, and that genes whose expression tracked with local pigment levels were enriched for aging-related biological pathways. Taking the analysis to its finest resolution, the investigators used in situ sequencing data to examine individual locus coeruleus neurons, measuring neuromelanin in each cell and validating its associations with the expression of APOE itself, genes involved in norepinephrine metabolism, and components of the autophagy machinery, the cellular recycling system that clears damaged proteins and organelles. This constellation of pigment-linked genes paints a coherent picture: neuromelanin content reflects, at the single-cell level, the interplay of neurotransmitter handling, lipid biology, and protein quality control, all processes implicated in neurodegeneration.

The significance of these findings is best appreciated against the backdrop of what is already known about the locus coeruleus in Alzheimer’s disease. Postmortem studies stretching back decades, including the classic staging work of Heiko Braak and colleagues, established that phosphorylated tau appears in locus coeruleus neurons exceptionally early, often in individuals who died with no cognitive impairment. Autopsy studies have documented substantial neuronal loss in this nucleus in mild cognitive impairment and early Alzheimer’s disease, and neuroimaging research has repeatedly linked reduced locus coeruleus integrity to tau burden, memory decline, neuropsychiatric symptoms, and sleep disruption. Animal work has shown, conversely, that noradrenergic depletion exaggerates the inflammatory response to amyloid-beta pathology, while pharmacological enhancement of norepinephrine signaling can suppress neuroinflammation. The locus coeruleus, in other words, is not merely a passive victim of Alzheimer’s pathology; its noradrenergic output actively modulates the brain’s response to it.

The new study adds two crucial molecular threads to this narrative. First, it demonstrates that Alzheimer’s genetic risk acts on the locus coeruleus ecosystem, not just on its neurons in isolation. The astrocytic deficit observed near E4-carrier neurons is particularly intriguing in light of recent evidence that norepinephrine signals through astrocytes to modulate synaptic function, and that astrocytes undergo stereotyped transcriptomic changes across the spatiotemporal progression of Alzheimer’s disease. If E4 carriers begin adulthood with less robust astrocytic support around their noradrenergic neurons, this could help explain why these neurons succumb early, and why E4 homozygosity has recently been characterized as a distinct, nearly deterministic genetic form of Alzheimer’s disease. Second, by tying neuromelanin, a biomarker measurable in living patients with specialized MRI, to concrete gene expression programs involving autophagy and catecholamine metabolism, the study strengthens the mechanistic bridge between what clinicians can image and what is happening molecularly inside vulnerable neurons.

Technically, the study represents a tour de force of modern spatial genomics applied to a notoriously difficult brain region. The locus coeruleus is small, deeply located in the dorsal pons, and densely pigmented, making it easy to miss in standard postmortem dissection and challenging to analyze with conventional bulk methods that average away spatial relationships. The team’s spatially resolved transcriptomics data captured tissue architecture at spot-level resolution, while their in situ sequencing pushed the analysis to individual cells, using the pigment itself as an additional segmentation cue to define neuromelanin-rich cellular compartments. The authors have deposited all data in public repositories and built interactive web portals, using visualization tools such as Samui and spatialLIBD, allowing any researcher to explore the spatial domain assignments, gene expression maps, and stained tissue sections, an unusually transparent approach that should accelerate follow-up work across the field.

The implications reach toward prevention and early detection. Because the donors in this study were neurotypical and middle-aged, the observed molecular differences, diminished astrocytic gene expression in E4 carriers, ancestry-dependent expression patterns, pigment-linked autophagy and norepinephrine genes, represent the brain’s baseline state before overt pathology, a snapshot of vulnerability rather than of damage. Interventions aimed at supporting astrocytic function, enhancing autophagic clearance of tau, or modulating noradrenergic tone might therefore be most effective when targeted at these early molecular states rather than at established dementia. Meanwhile, the confirmation that APOE expression itself inversely tracks neuromelanin suggests that pigment imaging could serve not only as a marker of neuronal loss but as a window into the lipid-handling pathways that connect APOE biology to neurodegeneration. As the authors conclude, Alzheimer’s risk factors appear to modulate locus coeruleus vulnerability through molecular processes intrinsic to both the noradrenergic neurons and their astrocytic partners, a reminder that the neurons that wake us, focus us, and help us remember may hold some of the earliest clues to when and how Alzheimer’s disease takes hold.