New research from Indiana University suggests that when your brain makes a decision, it may not actually “make” it in the way neuroscientists have traditionally assumed.

Conventionally, decision-making has been described as a sequence: you have options to consider, you choose one, and then you act. However, neuroscientist Tom James argues that this sequence may not accurately reflect what actually happens in the brain.

Traditional models suggest that the brain has centralized processes responsible for making decisions. James argues that what people experience as “decision-making” may instead arise from ongoing interactions among perception, movement, the body, and the environment. If correct, this perspective could significantly change how scientists understand human behavior.

The Sandwich Model Debated 

James describes one traditional approach to cognition as the “sandwich model”: sensory systems take in information, cognitive processes evaluate it and determine what to do, and motor systems then carry out the chosen action.

The problem, James argues, is that while scientists can identify many of the physical mechanisms involved in sensation and movement, it is much harder to locate a central point in the brain where a “decision” actually occurs.

Instead, James proposes thinking in terms of “action selection,” in which sensory, sensorimotor, and motor processes continually interact to produce behavior. But does that mean humans don’t actually make decisions?

“Of course they do. We use this language all the time, and it’s very helpful in terms of describing behavior,” James said in a statement. “The leap, I think, is to say that the brain works by having decision-making or control processes. It produces behavior that is well described in that way. But it doesn’t need a process that does that to make it look that way.”

In his view, “decision-making” may therefore be a useful way of describing human behavior rather than something the brain accomplishes through a specific, centralized process or system.

“As mental phenomena, they are defined on too abstract a level for the goals of cognitive neuroscience,” adds James.

Decisions are not “things”

Bridging neuroscience and philosophy, James draws on a physicalist framework associated with philosopher Daniel Dennett. Consider an object’s center of mass: it helps us describe and predict how the object behaves, but it isn’t a separate physical component causing the object to move.

James suggests decisions may work similarly. We can say someone “decided” to pick up a glass, but that does not necessarily mean there is a central location in the brain where that decision was made. Instead, the action could emerge from multiple brain and bodily processes working together.

As an analogy, James describes a robot programmed to follow a wall. Someone watching it might interpret its movements as a series of decisions, yet the robot does not require a centralized decision-making system. Instead, its sensors continually interact with its surroundings to guide its behavior.

James suggests that aspects of human behavior could be understood in much the same way.

A philosophical challenge 

James’s argument also addresses a longstanding philosophical problem. If the brain contains a central system that observes information and decides how we should act, what controls that controller?

This resembles what Dennett famously called the “Cartesian Theater”: the idea of an internal observer somehow watching and interpreting what happens inside the brain. But proposing such an observer only pushes the problem back another step—who, then, observes the observer?

Rather than placing a hypothetical decision-maker somewhere inside the brain, James argues that researchers should focus on the interacting physical systems that produce behavior.

That could mean studying cognition as something that emerges through the ongoing relationship between the brain, body, and environment, rather than as a sequence of isolated stages.

The feeling that “I made a decision” may remain perfectly real and useful as a description of human experience, even if there is no single place or process in the brain where that decision occurs. From James’s perspective, what we call a decision may instead be the outcome of interactions among the brain, body, and surrounding world.

This study can be found in the Journal of Cognitive Neuroscience.

Chrissy Newton is a PR professional and the founder of VOCAB Communications. She currently appears on The Discovery Channel and Max and hosts the Rebelliously Curious podcast, which can be found on YouTube and on all audio podcast streaming platforms. Follow her on X: @ChrissyNewton, Instagram: @BeingChrissyNewton, and chrissynewton.com. To contact Chrissy with a story, please email chrissy @ thedebrief.org.

Scientists have uncovered new details about how the brain’s “information hub” responds to uncertainty, revealing a remarkably flexible system that rapidly changes how it communicates with other brain regions as we make decisions.

Every second of our lives, the human brain sorts through enormous amounts of information. From remembering a driving route and handling daily tasks to making decisions, the brain must continually determine what information matters and how to respond. Researchers at the University of Iowa wanted to better understand how the human brain manages and processes this information and found that it may be more flexible than previously thought.

At the center of this sorting process is an area of the brain known as the frontoparietal cortex. This region supports decision-making and behavioral control and can function as a kind of “information hub” for the brain.

The Study 

Using MRI scans, behavioral experiments, and computational modeling, researchers took a deeper look at how the frontoparietal cortex responds when people are uncertain about what they should do next.

“That’s the main contribution,” says Kai Hwang, associate professor in the Department of Psychological and Brain Sciences and the study’s corresponding author, in a statement. 

“Our study shows in more detail how the frontoparietal cortex operates — what kind of information it extracts from other systems and how it uses its connectivity pattern to integrate information that is coming in from different areas of the brain,” Hwang adds. 

The research team recruited 38 participants between the ages of 18 and 35 and had each person learn associations between combinations of colors, faces, and scenes and particular actions—for example, pressing a button with the index or middle finger of either hand. The researchers then intentionally changed those associations.

When an action or rule changes, the brain has to determine what went wrong.

Did the situation change? Was the information unclear? Or did the person simply make a mistake? This uncertainty allowed researchers to observe how the brain’s “information hub” responds under different circumstances.

“It’s like where other areas of the brain don’t have all the information, so they send what they have to the frontoparietal cortex for guidance,” Hwang explains.

The results from the team’s research suggest the frontoparietal cortex does more than become more active when a task becomes difficult. Instead, the region changes how it communicates with other brain regions. This also depends on what information is needed at different decision-making stages. 

Hwang notes, “If they always get it right, they know they’ve made the correct association, but once they start doing it wrong, they will have to guess, ‘Oh, did the context change, or did I not see the color clearly?’ That creates uncertainty.” 

Your Brain Has an Air Traffic Controller

Hwang describes the brain’s “information hub” as a system that gathers information from different regions, determines what is relevant, and integrates those signals into a broader picture. One way to think of this function is as an air traffic controller, monitoring incoming information and adjusting communications as circumstances change.

“These are situations where people struggle with regulating their behavior. That, to me, is an integration problem. If that integration function is not working properly, then that could very likely mean they didn’t use the right context to regulate their behavior,” Hwang adds.

The study does not address how neurodiversity might affect this information-sorting process, although the findings could provide a foundation for researchers to investigate that question.

“It remains an open question how neurodiversity might affect frontoparietal integration. It is a very interesting question that, as far as I know, has not been studied directly, so I don’t have a definitive answer. One of our goals in conducting this study is to lay the foundation for future clinical investigations,” Hwang tells The Debrief in an email.

The findings suggest that the frontoparietal cortex can rapidly integrate and update information as circumstances change. Understanding this flexibility could eventually improve researchers’ understanding of neurological and psychiatric conditions involving behavioral regulation and decision-making. In particular, studying these communication patterns could help inform future investigations of conditions such as ADHD and schizophrenia, in which behavioral regulation and the use of contextual information can be impaired.

The new research was published in the Journal of Neuroscience.

Chrissy Newton is a PR professional and the founder of VOCAB Communications. She currently appears on The Discovery Channel and Max and hosts the Rebelliously Curious podcast, which can be found on YouTube and on all audio podcast streaming platforms. Follow her on X: @ChrissyNewton, Instagram: @BeingChrissyNewton, and chrissynewton.com. To contact Chrissy with a story, please email chrissy @ thedebrief.org.

Age-related remodeling of the immune system leaves older adults increasingly susceptible to sudden infections and cancer, while also promoting chronic, misdirected inflammation and impaired wound repair. These effects help drive a wide spectrum of long-term diseases. Yet despite rapid progress in immunology and gerontology, key mechanistic links—how specific immune-cell programs degrade over time and how that degradation shapes early response decisions—remain incompletely understood.

A major obstacle has been the limited availability of immune-aging models that are both predictive and experimentally tractable. Conventional in vitro systems often fail to capture the tissue context, cellular crosstalk, and microenvironmental cues that govern when and how immune responses ignite. As a result, “aging phenotypes” can be difficult to reproduce across laboratories and difficult to connect to causal pathways.

In a 2026 Nature Aging discussion, Nikolich and colleagues argue that the field needs a shift from descriptive comparisons toward engineered, mechanistically grounded platforms. They propose multidisciplinary strategies spanning immunology, gerontology, and engineering to interrogate immune aging at the point of initiation—when innate sensors, tissue barriers, and early signaling networks collectively determine the trajectory of downstream immunity.

The authors highlight drug delivery technologies as a route to more precise interventions. By tuning where, when, and at what dose immunomodulatory agents act, researchers could better test how age-altered signaling thresholds change the balance between protective responses and harmful inflammation.

They also emphasize extracellular matrix (ECM) and biomaterials, noting that aging reshapes the physical and biochemical scaffold surrounding immune cells. Biomaterials that recapitulate age-associated stiffness, ligand density, and degradation dynamics could reveal how microenvironmental cues reprogram immune recruitment, activation, and effector function.

Microphysiological systems and organoid-like models are presented as complementary engines for causality. These platforms can integrate multiple cell types, enable controlled perturbations, and better mimic tissue architecture than traditional culture formats.

Together, these approaches aim to deliver validated in vitro tools for immune-aging research—tools designed not only to measure differences, but to reproduce them reliably and connect them to specific upstream drivers.

The ultimate ambition is precision diagnostics and therapies that can “reprogram and rejuvenate” immune function in older adults, extending both lifespan and healthspan. By targeting immune-response initiation with engineered models, the field may finally bridge the gap between observational aging biology and interventions that work.

Subject of Research: Aging immune system; immune response initiation models and modulation

Article Title: Bioengineering strategies to interrogate and modulate the aging immune system

Article References: Nikolich, J.Ž., Wolf, M.T., Pompano, R.R. et al. Bioengineering strategies to interrogate and modulate the aging immune system. Nat Aging (2026). https://doi.org/10.1038/s43587-026-01187-y

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s43587-026-01187-y

Ararat-1, a modest limestone cave that lies at an elevation of 1,034 m on the eastern flanks of the Ararat Depression in central Armenia, has yielded a striking puzzle about how Middle Paleolithic humans moved across their landscape.

The post Stone Tools from Armenian Cave Reveal Split Strategy for Survival 50,000 Years Ago appeared first on Sci.News: Breaking Science News.

A groundbreaking advancement in photonic technology promises to redefine the landscape of medical artificial intelligence (AI) diagnostics, as a research consortium led by Professor Han Zhang at Shenzhen University has unveiled a novel all-fiber photonic AI platform utilizing black phosphorus (BP)-based tunable modulators. This innovative approach, which hinges on photon-based computing, surmounts the intrinsic constraints posed by traditional electronic processors, delivering unprecedented energy efficiency and ultrafast performance crucial for real-time clinical decision-making.

The prevailing paradigm in medical AI, deeply reliant on electronic architectures such as GPUs, faces severe limitations related to energy consumption, heat dissipation, and processing latency. These factors collectively impede the integration of AI into time-sensitive diagnostic workflows and exacerbate systemic carbon footprints. Photonic computing, with its reliance on photons rather than electrons, offers dramatically accelerated data transmission speeds and parallelism through wavelength multiplexing, alongside near-zero heat generation. Until now, however, the practical implementation of photonic AI had been hampered by the bulkiness, inefficiency, and fabrication challenges synonymous with conventional optical modulators.

Professor Zhang’s team has ingeniously engineered an atomically thin van der Waals heterostructure by stacking black phosphorus and molybdenum disulfide (MoS2), leveraging the extraordinary optoelectronic properties of these 2D materials. This heterostructure is meticulously integrated onto a microfiber knot resonator (MKR), an ultra-fine looped optical fiber with a diameter narrower than a human hair. This configuration significantly intensifies the interaction between light and material, enabling modulation of the refractive index with minimal applied voltage, which shifts the light’s wavelength to encode computational information with remarkable precision and energy economy.

The fabrication process commences with mechanical exfoliation to obtain pristine BP/MoS2 bilayers, followed by a dry transfer technique to position these heterostructures onto the MKR. The MKR’s torsional geometry enhances evanescent field coupling, ensuring that the optical mode overlaps maximally with the active layered materials. To broaden the modulator’s linear response and suppress nonlinear distortions, the researchers incorporated a Ring-Assisted Mach–Zehnder Interferometer (RAMZI) design. This integration extends the operational voltage range, ensuring fidelity and robustness essential for medical-grade AI computations.

The team transcended single-device innovation by architecting a full all-fiber photonic neural network (PNN). By deploying two RAMZI modulators in tandem with a highly sensitive photoreceiver within a sophisticated time-division multiplexing schema, they realized a closed-loop, coherent computing unit capable of executing neural network operations entirely in the optical domain. This milestone substantiates the transition of photonic neural networks from theoretical constructs to viable, deployable platforms for clinical diagnostics.

Clinical performance validation was conducted in collaboration with esteemed hospitals. The PNN demonstrated expert-level diagnostic accuracy in two stringent medical tasks: detecting retinal detachment from B-scan ultrasound imagery and diagnosing hepatocellular carcinoma (HCC) using multiphase liver computed tomography (CT) scans. Diagnostic accuracy and specificity metrics paralleled those of seasoned radiologists, underscoring the system’s potential for integration into frontline healthcare.

Beyond accuracy, the photonic system showcased transformative efficiency advantages. While a conventional NVIDIA A100 GPU requires approximately 85 milliseconds to process a liver CT study, the photonic platform accomplished the same in a mere 0.8 milliseconds. Additionally, the energy expenditure per operation plummeted to an astonishing 0.608 femtojoules, a staggering 246-fold decrease compared to electronic counterparts consuming 150 femtojoules. This synergy of speed and efficiency paves the way for deploying AI diagnostics in resource-constrained or mobile medical environments like rural clinics and ambulances, dramatically expanding access to critical health services.

The system’s environmental implications cannot be overstated. Conventional AI’s sizable energy footprint contributes appreciably to healthcare’s carbon emissions. The BP/MoS2-based photonic platform fundamentally disrupts this model by combining ultrafast processing with near-negligible thermal dissipation, forging a sustainable pathway for green AI implementations in medicine. This breakthrough aligns with expanding global commitments to environmentally responsible technologies without sacrificing computational power.

Acknowledging the infancy of current implementations, the authors outline avenues for scaling. Presently limited to a single processing layer realized through two modulators, the system’s complexity and image resolution capacity necessitate expansion. A promising strategy involves wavelength-division multiplexing (WDM), exploiting the device’s ∼30 nm bandwidth resonances to channel up to 40 distinct wavelengths. This parallelism would amplify computational density approximately 40-fold while maintaining the existing clock rate, facilitating thousands of multiplications per network layer—an enhancement critical to tackling high-dimensional medical image data.

Device longevity and stability pose additional challenges, given the sensitivity of BP to environmental degradation. While the MoS2 layer affords interim encapsulation, the team plans industrial collaboration to pioneer large-scale protective coatings via atomic layer deposition of Al2O3 and scalable chemical vapor deposition processes. These advancements will bolster device durability, uniformity, and manufacturability, accelerating clinical adoption and commercial viability.

The study also signifies the maturation of fiber-based photonic neural networks into practical diagnostic platforms rather than experimental novelties. Optical fibers’ inherently low transmission loss (<0.2 dB/km), synergized with the modulator’s efficient 0.25 V·cm electro-optic response and expanded linear range, addresses critical bottlenecks impeding prior photonic computing endeavors. Consequently, the technology promises to empower future applications in drug discovery, genomics, and real-time clinical imaging with unmatched energy footprints.

The pioneering work led by Professor Han Zhang not only foregrounds interdisciplinary collaboration across physics, materials science, and biomedicine but also sets a precedent for industry-academia partnerships catalyzing the commercialization of ultrafast photonic devices poised to transform healthcare AI. As research progresses towards integrated multi-layer architectures and industrial encapsulation, the healthcare sector stands on the cusp of embracing a new generation of diagnostic tools that are faster, greener, and broadly accessible.

This heralds a paradigm shift where medical AI transcends the limitations inherent to electronic systems, embracing photon-driven computing as a sustainable, high-performance alternative that aligns with the twin imperatives of clinical excellence and environmental stewardship.

Subject of Research: People

Article Title: Tunable phosphorene modulators – accelerating medical diagnosis with ultra-efficient photonic platforms

News Publication Date: 28-May-2026

References:
Zhang, H., Liu, Y., Wang, H., Zhu, H., et al. (2026). Tunable phosphorene modulators – accelerating medical diagnosis with ultra-efficient photonic platforms. Opto-Electronic Advances. DOI: 10.29026/oea.2026.250332

Image Credits: Professor Han Zhang of Shenzhen University

Keywords: photonic computing, black phosphorus, molybdenum disulfide, van der Waals heterostructure, microfiber knot resonator, electro-optic modulator, photonic neural network, medical imaging AI, energy efficiency, wavelength-division multiplexing, ultrafast diagnostics, sustainable healthcare