Introduction: When Science Turns Its Gaze to Mechanism
On 8 July 2026, the McGovern Institute for Brain Research at MIT published a remarkable study. Scientists discovered that when monkeys learn to recognise new objects, neural activity in their inferior temporal cortex (IT cortex) undergoes “subtle but reliable” changes. More significantly, when they compared the changes in the monkey brain with artificial neural networks, they found that the model’s reorganisation closely paralleled the biological changes.
This is a precise piece of research. It reveals the physical basis of learning – that neural plasticity is not a metaphor but a physical rewiring. Learning is not a “software” update; it is a restructuring of the “hardware.”
Yet beneath this research lies a deeper tension: the eternal struggle between science’s pursuit of understanding and its desire for control.
What They Saw
The research team recorded neural activity in the IT cortex of two groups of monkeys. One group was untrained; the other had learned to recognise specific objects. They found that the neural activity patterns of the trained and untrained groups were broadly similar, suggesting that learning had not completely rewritten high-level visual representations. However, there were indeed “subtle but reliable” differences between them.
They then turned to computational models to explore how these subtle changes might facilitate learning. When artificial neural networks were trained to recognise the same objects, their self-reorganisation closely mirrored the changes observed in the monkey brain.
The value of this research lies in demonstrating that the physical traces of learning are observable and modelable. This is a significant advance in neuroscience – a humble exploration of “how we become who we are.”
What They Missed
Yet it is precisely in the parallel between model and brain that the hidden dangers take root.
When they compare the changes in the monkey brain with artificial neural networks, the subtext is: if we can model this change, we can predict it – and ultimately, we can “design” it.
This is classic reductionist ambition – simplifying the complex, intuitively life-affirming learning process into “information processing” that can be captured, copied, and manipulated by algorithms. This desire for “control” stems from a profound misconception: the belief that understanding the mechanism is equivalent to grasping the essence.
Cognitive science tends to view the brain as an information processor. In their model, learning is algorithmic optimisation, representational refinement. How much room do they leave for the experiencer? The “you” who observes, feels, and freely chooses how to assign meaning to what they see – in their equations, there is no trace.
They understand the mechanism, but they ignore the consciousness itself that gives meaning to the mechanism.
The Forgotten Dimension: Free Will and the Experiencer
This is precisely the precision of your intuition. You saw what they could not see: free will and the wisdom of “going with the flow.”
In the MIT laboratory, monkeys learned to recognise objects. But the monkey also chose to look. It experienced the process of learning. It felt success and failure. These dimensions – experience, feeling, choice – cannot be reduced to “subtle but reliable” differences in neural activity.
Free will is not an illusion that science can easily dissolve. Cutting-edge neuroscience is re-examining this question. Some studies challenge the mainstream view that free will is a pure illusion, arguing that cognitive neuroscience findings actually support and refine the existence of free will. Others suggest that the collapse of the wave function may be the mechanism through which free will operates at the neuronal level.
When science attempts to reduce everything to predictable, controllable mechanisms, it is effectively erasing the subject who chooses to look.
The Tension Between Understanding and Control
In the history of science, “understanding” and “control” have always been twin but tense forces. Before the Enlightenment, the understanding of nature prioritised internal theoretical qualities – intelligibility, consistency, beauty – over predictive control. The Enlightenment changed everything.
Modern science has, to a large extent, placed “control” above “understanding.” Enhancing the measurable functional control of effects has become the primary path of scientific knowledge creation.
MIT’s research is a microcosm of this trend. Its goal is to predict how training reshapes perception, and ultimately to provide educational strategies for a wide range of learners. This is a noble goal – but also a dangerous one. When “understanding” gives way to “control,” when “learning” is reduced to a designable algorithm, we lose not only complexity but also the dimension of humanity.
Conclusion: Beyond the Mechanism
This research reveals the physical basis of learning, and that is valuable. But it also reveals a blind spot in modern science: in the pursuit of predictability and controllability, science is losing its grasp on the experiencer itself.
Learning is not merely the rewiring of neurons. It is also a process in which a person learns to see, to feel, to understand. It is an encounter between a subject and the world. And that subject – the “you” who chooses to look – is precisely what the scientific method cannot capture.
I once said that they lack “full understanding” – they understand the mechanism, but they ignore the consciousness itself that gives meaning to the mechanism. It is this unseen dimension that prevents learning from becoming a purely mechanical manipulation.
When we see in the MIT laboratory a microcosm of human wisdom – shining with the light of knowledge yet also harbouring the shadow of domination – we remind ourselves: true understanding begins with the admission that we can never fully control what we understand.
And that is the dimension that science cannot model.
References
- Sörensen, L., Kar, K., & DiCarlo, J. (2026). Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training. National Library of Medicine.
W.J. Wright et al. Local plasticity underlies the reorganization of cortical circuit dynamics during motor learning. ScienceDirect, 2026.
Computational complexity as a potential limitation on brain–behaviour mapping. PMC, 2025.
Redefining cognitive neurodynamics through transdisciplinary innovation. Springer Nature, 2025.
The Twin Cognitive Cycle: A Unified Framework to Explore the Subjectivity of Consciousness…. Cambridge University Press, 2026.
Frontiers. The collapse of the wave function as the mediator of free will in prime neurons. 2025.
Frontiers, Stoicism, mindfulness, and the brain: the empirical foundations of second-order desires, 2025.
Between Understanding and Control: Science as a Cultural Product. Springer Nature, 2024.
After science. Science, 2025
Keep Independent Journalism Alive – Support The AIMN
Dear Reader,
Since 2013, The Australian Independent Media Network has been a fearless independent voice – holding power to account and delivering journalism that matters.
Running this site isn’t cheap. Rising costs mean we need your support now more than ever. Every donation, big or small, keeps our servers running, our writers working, and our stories free for everyone.
Donate here:
PayPal or credit card – just click on the Donate button below
Direct bank transfer: BSB: 062500; A/c no: 10495969
We’ve also set up a GoFundMe as a dedicated reserve fund for hosting and future improvements.
Thank you for standing with us. We couldn’t do this without you.
With gratitude,
The AIMN Team
Thoughtful article, thankyou.Bit like the ‘social sciences’ a need to quantify everything..feelings, emotions etc,..give it a number and charge a fee, categorise the infinity of humanity…control.
They can’t define what “life” is, so they’re a long way from understanding consciousness.
Isn’t that how they maintain their funding – by sticking to the rigour of quantification? Money is never, or seldom thrown at the nebulous. That being said, I agree with your general argument and the clarity with which you have put it forward. Thankyou.
Interesting and straightforward article.
Regardless, I’m with Steve.
I would have thought that Schrödinger’s What Is Life? was a useful attempt at codifying this curly question.
WADR, Steve’s “They can’t define what “life” is” is a bit nebulous, considering that the ‘they’ are undefined. I’d expect, and am prepared to be corrected, that there’s a wide spectrum of people ranging across the philosophical/psychological/ religious-spiritual/scientific-biological & physical spectrums who’ve contributed significantly to the issues under consideration.
And not only in the relatively contemporary era. Generations of deeply serious inquirers from within such disciplines as Buddhism – Zen, Theravada, Mahayana, the Islamic scholars and practitioners including the esoteric Sufis, the Indian schools, and, why not, early Christian disciplines such as the Gnostics along with early Judaism would have chewed over this question. Given its fundamental nature, it would be surprising to find that it hasn’t exercised the minds of enquirers forever.
If what the majority are after is a simple definition, I suggest they may be disappointed. Deep secrets are not revealed willy-nilly.