In 2005, a groundbreaking experiment changed how scientists understand and detect consciousness. A 23-year-old woman, left unresponsive after a car accident, was asked to imagine playing tennis while undergoing a brain scan. Remarkably, regions of her brain associated with motor planning lit up, suggesting she understood the instructions and chose to cooperate — despite showing no outward signs of awareness.
Published in Nature, this revelation, led by neuroscientist Adrian Owen and colleagues, marked the beginning of a paradigm shift. By isolating brain responses tied to specific commands, researchers developed a way to probe the minds of individuals previously believed to be entirely unconscious. Now, nearly two decades later, the science of consciousness detection has grown far more sophisticated — and far-reaching.
A 2024 study reported in Nature revealed that around one in four unresponsive patients demonstrated brain activity consistent with command-following when asked to imagine familiar scenarios, such as walking through a house. These patients — often described as having “covert consciousness” or cognitive motor dissociation — are challenging longstanding clinical assumptions about awareness in severely brain-injured individuals.
As techniques like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) become more refined, they are increasingly recommended in clinical guidelines. Yet these technologies remain mostly confined to research settings due to their complexity and cost.
“The idea that we could practically assess consciousness in unresponsive individuals was unthinkable 40 years ago,” says neuroscientist Christof Koch of the Allen Institute for Brain Science in Seattle. “That’s big progress.”
Peeling Back the Layers of Consciousness
Marcello Massimini, a neuroscientist at the University of Milan, likens detecting consciousness to peeling an onion. The first layer involves evaluating external behaviors — such as eye movement or voluntary actions — which remains standard practice in clinics. The second layer, exemplified by Owen’s tennis imagery test, focuses on brain activity in response to specific commands. A more elusive third layer observes how the brain reacts passively to stimuli, such as spoken language, without requiring active participation from the patient.
One 2017 Nature study used JFK’s inaugural address — and a reverse version of the same audio — to identify subtle neural differences in brain-injured patients. The results showed that even in the absence of outward behavior, some individuals retained linguistic processing ability.
The fourth and deepest layer, according to Massimini, is perhaps the most mysterious: intrinsic consciousness. This refers to an inner mental life occurring independently of any external input — akin to dreaming in a sensory void. Researchers are now exploring ways to detect this state using transcranial magnetic stimulation combined with EEG, calculating a brain’s “perturbational complexity index” (PCI). A higher PCI suggests a richer internal dialogue among brain regions — and possibly the presence of consciousness.
Beyond Humans: Animal Minds and AI Awareness
Detecting consciousness isn’t just a clinical issue; it’s reshaping animal welfare and sparking philosophical debates about artificial intelligence. Some researchers are applying techniques like the PCI to animals such as rats, while behavioral tests — including experiments with octopuses — provide growing evidence of sentience in invertebrates.
These insights are already influencing policy. In the UK, a 2022 amendment to the Animal Welfare (Sentience) Act extended legal protections to octopuses, lobsters, and crabs after research led by philosopher Jonathan Birch showed they exhibit complex pain-related behavior.
Meanwhile, the question of AI consciousness is gaining urgency. Last year, a coalition of philosophers and computer scientists called for standardized methods to test AI systems for signs of awareness. While current large language models (LLMs) can simulate responses to questions about consciousness, experts caution against interpreting these outputs as proof of sentience.
“We don’t think verbal responses alone are valid evidence of consciousness in machines,” says philosopher Tim Bayne of Monash University, who co-authored a 2020 proposal for a universal consciousness detection framework.
Integrated Information Theory (IIT), one of the most discussed theories in the field, suggests that current AI cannot be conscious — though future technologies like quantum computing might change that equation.
Toward a Universal Test
Bayne and colleague Nicholas Shea have proposed a “convergence-based” strategy: start by validating tests on healthy humans, then gradually apply them to more diverse populations, including brain-injured patients, animals, and potentially AI. The idea is to iteratively identify which measures consistently align with conscious awareness across domains.
Though still theoretical, this approach aims to build a universal toolbox for consciousness detection. Liad Mudrik, a neuroscientist at Tel Aviv University, is working to turn the concept into a practical research program. However, the endeavor is both scientifically and financially daunting.
“There’s still a lot of conceptual and logistical work needed,” says Mudrik. “But if machine or non-human consciousness is even a possibility, we need to be prepared.”
As science continues to push the boundaries of what it means to be conscious, the implications span medicine, law, animal rights, and the future of AI. The work, as Nature highlights, is far from over — but the conversation has shifted from philosophy to pragmatism. And that, in itself, is a form of awakening.
It’s becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman’s Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with only primary consciousness will probably have to come first.
What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990’s and 2000’s. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I’ve encountered is anywhere near as convincing.
I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there’s lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.
My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar’s lab at UC Irvine, possibly. Dr. Edelman’s roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461, and here is a video of Jeff Krichmar talking about some of the Darwin automata, https://www.youtube.com/watch?v=J7Uh9phc1Ow