When the Brain Speaks Without Words: Decoding Tacit Knowledge and Intelligence Through Neural Connectivity

In neuroscience and cognitive science, we are often taught that what matters is what can be measured. But some forms of human expertise—like intuition, skill, or insight—have long eluded direct observation. We call this tacit knowledge: knowing how to act, without necessarily being able to say why.

A recent study by Zhang et al. brings this elusive domain into sharper focus. By analyzing EEG-derived functional brain networks (FBNs) during a complex industrial control task, the researchers were able to distinguish between high- and low-proficiency operators with over 94% accuracy—not by observing behavior, but by examining the topology of brain connectivity.

The most proficient individuals exhibited more densely connected and efficient networks, particularly in the beta and gamma bands. In effect, tacit skill—long thought to reside beneath the surface of consciousness—was made visible through graph-theoretic metrics applied to real-time neural data.

Reading this work inevitably brought to mind earlier research by Thatcher et al., who examined the relationship between brain connectivity and intelligence. Using the Phase Slope Index (PSI)—a measure of effective connectivity also referred to as Information Flow—they found that higher IQ was inversely correlated with long-range connectivity and positively associated with short-range, localized connectivity. This pattern suggests a more efficient mode of neural processing for high IQ individuals, where cognitive demands are met through well-organized local circuits, rather than relying on compensatory long-distance connections that place greater strain on the network. The findings align with the small-world model of brain organization, in which optimal performance arises from a balance of local specialization and global integration.

While Thatcher’s findings sparked both interest and debate in the community, they remain an important reference point in the history of EEG-based cognitive research. And notably, they resonate with newer findings like those of Zhang et al., and many others, which are emerging from task-driven, applied neurotechnology contexts.

In fact, Zhang’s emphasis on beta-band connectivity—a frequency range often associated with sensorimotor integration and cognitive control—may reflect the activation of precisely the kind of local, high-efficiency networks that Thatcher described. In both cases, we see converging evidence that skill and intelligence are not simply distributed traits, but are emergent properties of how efficiently the brain manages its own internal communication.

Though the questions and methods have evolved, a common theme is becoming clear:

Cognition—whether learned or innate—lives in the dynamics of networks.

And this has implications.

For those of us working at the intersection of neuroscience, technology, and real-world performance, this convergence is more than theoretical. It speaks to a future in which cognitive states, latent skills, and potential can be inferred not just through observation or testing, but through the real-time language of the brain itself. In our own work, we are increasingly focused on how these network-based biomarkers can inform adaptive systems, training, and interventions—grounded in neurophysiology, yet tuned for practical impact.

As the field evolves, it's becoming clear: The brain doesn’t need to tell us what it knows. It shows us—if we know how to listen.

Network-level EEG metrics of connectivity are biomarkers of cognitive states and latent performance capacity. Whether in high-performance environments, cognitive training, or neuroadaptive systems, connectivity is emerging as a core signal of how the brain self-organizes under pressure, expertise, drugs, or any triggered change. And with the right tools, we can now listen to that signal—in real time.

 References:

Thatcher, R. W., Palmero-Soler, E., North, D. M. & Biver, C. J. Intelligence and eeg measures of information flow: efficiency and homeostatic neuroplasticity. Sci Rep 6, 38890 (2016).

Zhang, T., Hua, C., Chen, J., He, E. & Wang, H. Study of Human Tacit Knowledge Based on Electroencephalogram Signal Characteristics. Front Neurosci 15, 690633 (2021).