The Nobel Prize is often awarded not to researchers who made a huge discovery about how the world works, but to those who have developed techniques that improve research itself. The downstream effects of such developments are likely to be dozens or hundreds, even thousands, of discoveries This is the case for the 2026 Nobel Prize in physiology or medicine – awarded to Karl Deisseroth, Peter Hegemann, and Georg Nagel for their work developing optogenetics. This is a powerful tool that allows us to create a functional map of the brain.

The story begins with Hegemann who in the 1990s was studying the free swimming algae, Chlamydomonas. The algae swims towards light, which anyone can reproduce by shining a light on a petri dish full of the green organisms. They have a small eyespot which can detect light, but Hegemann was fascinated with how quickly they were able to respond to a light signal. This lead him to hypothesize that the algae may be using a different method for turning light into a signal that can affect their behavior than vertebrate eyes. When he attached electrodes to Chlamydomonas and shined a light on them, he was able to detect an electrical signal in half a millisecond – incredibly fast. This is simply too fast to conduct any signal, so he thought it must be the same protein that detects the light and creates the resulting electrical pulse. Now all he had to do was identify that protein.

A Japanese group had already mapped the DNA of Chlamydomonas, and of the genes identified, two had properties that might fit the bill. But this was now a bit out of the field of expertise of Hegemann, so he contacted Nagel for help and sent him the code for the two candidate genes. Nagel injected the genes into separate batches of frog eggs, which began to express the proteins. He could then study how the proteins functioned, and was able to confirm Hegemann’s hypothesis. Both proteins, named channelrhodopsin-1 and channelrhodopsin-2, were ion channels that respond to light by opening up, allowing positive ions to flow through,  and creating an electrical potential. Channelrhodopsin-2 was particularly powerful, creating an electrical signal in 0.2 milliseconds. They were able to incorporate the gene for this protein into human and hamster kidney cells and show that they become light sensitive.

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Dog owners (of which I am one) tend to develop a strong sense that their dogs understand what they are saying. Research actually supports this conclusion. In a 2016 study dogs were able to respond to praise words without intonation, to intonation with neutral words, but most strongly to praise words with intonation. Dogs seemed to understand the words themselves – even absent the intonation. This does not mean they understand words the way we do, or that they process language the way humans do. But they can recognize words and associate them with something at least good or bad.

There is also evidence that dogs have more neurons and a somewhat higher neuronal density for a carnivore (although nothing like racoons, which are the real outliers in terms of neuronal density). And there is evidence that dogs evolved to socialize with humans, showing clear differences even from wolves raised with humans. Dogs also evolved greater expressiveness, (like the eyebrow thing that they can do and no wolves), which further supports the notion that their domestication involved them gaining more smarts so that they can socialize and communicate with humans.

Such studies are always just partial slices of reality, and often raise even more questions. One question that a recent study seeks to address is whether or not a dog’s increased ability to parse words is innate or learned, and what are the mechanisms for this. The research paradigm they used was to look at human and dog brains with an EEG while they were listening to fake speech, to see how well their brains synchronized with different parts of speech. Synchronizing means that the EEG shows a repeating rhythm that is the same as a repeating rhythm in the speech. They were specifically interested in consonant bias – humans are the only animals tested so far whose brains respond more to consonants than vowels (not even our closest primate relatives do). This consonant bias likely results from the fact that consonants tend to be more distinctive in parsing words than vowels, so our brains pay attention more to the consonants when trying to discern words. Put another way, there is no reason for a brain to synchronize more with consonants than vowels unless it was trying to parse speech. So it is a very interesting question, whether dog brains show a consonant bias.

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How much attention do you pay to…well, attention? Attention is one of those many brain functions that you don’t notice or think about when it is working fine, but can become debilitating if it is impaired in any way, and we notice when it is stressed to the point of failure. Otherwise we don’t have much reason to contemplate the incredibly demanding and complex neurological process of managing attention. Recently neuroscientists have added one more piece to the puzzle of what we call the neuroanatomical correlates of attention – which parts of the brain are doing what.

To review, attention is a critical neurological function the primary purpose of which is to allocate limited brain resources for processing external stimuli and internal thoughts. When you attend to something you process more information about that thing more robustly while simultaneously actively suppressing other (distracting) information. There are basically two types of attention – a top-down goal oriented attention and a bottom-up stimulus response attention. So, when reading a book, for example, you are focused on the page and processing the squiggles into words and the words into meaning. If a loud noise occurs, that will involuntarily grab your attention.

It has long been know that the frontal lobes are critical to attention, particularly goal-oriented attention. But attention is also widely distributed throughout the brain, particular in the frontoparietal attention networks. Frontal lobe executive function is a sot of master control, directing attention and focus, and critical for switching tasks. Attention has wide-ranging effects throughout the brain, however, which makes sense given it can affect so many functions.

It has also been known that the superior colliculus is a critical attention hub. This is a primitive subcortical structure that initially was thought to only be involved in vision and eye movements. However, we now know it also overlays information from auditory and visuospatial centers of the brain. These overlapping maps of the world allow the superior colliculus to focus attention on one thing and then actively suppress all other sensory information. This is partly why phenomena like inattentional blindness can be so profound – when focusing your attention on one thing, you can entirely miss even large objects in your visual field (see here for a classic demonstration). You literally become blind to such things because at a basic neural level the information is being suppressed.

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Richard Dawkins is a public intellectual of some renown, although not without his controversies. So it is noteworthy when he writes an article claiming that the chatbot Claude is likely conscious. I found the article fascinating, not because I agree with his core claim or feel that he has contributed anything significant to the conversation, but because it seems to represent a scholar and deep thinker writing about a topic in which he lacks specific expertise. I also see no evidence in the article that he engaged meaningfully, or at least adequately, with a topic expert. As a result he makes some thoughtful and instructive errors.

He begins with a discussion of the Turing test, which has long been discussed as an early thought experiment about how we might determine if an AI is actually conscious. Dawkins essentially accepts the Turing test and write:

“It was one thing to grant consciousness to a hypothetical machine that — just imagine! — could one day succeed at the Imitation Game. But now that LLMs can actually pass the Turing Test? “Well, er, perhaps, um… Look here, I didn’t really mean it when, back then, I accepted Turing’s operational definition of a conscious being…””

He feels saying that LLMs have passed the Turing test but still not accepting them as conscious is moving the goalpost. However, the Turing test was never generally accepted by AI experts or philosophers as a true test of consciousness. Rather, it was understood that such a test really is only a measure of a machine’s ability to imitate human speech. I wrote about it in 2008, writing: “Ever since Alan Turing proposed his test it has provoked two still relevant questions: what does it mean to be intelligent, and what is the Turing test actually testing.” I went on to write:

“But I can imagine a day in the not-too-distant future when such AI can pass a Turing test. The algorithms will have to become much more complex, allow for varying answers to the same question, and make what seem to be abstract connections which take the conversation is new and unanticipated directions. You can liken computer AI simulating conversation to computer graphics (CG) simulating people. At first they appeared cartoonish, but in the last 20 years we have seen steady progress. Movement is now more natural, textures more subtle and complex. One of the last layers of realism to be added was imperfection. CG characters still seem CG when they are perfect, and so adding imperfections adds to the sense of reality. Similarly, an AI conversation might want to sprinkle some random quirkiness into the responses.

The questions is – will sophisticated-enough algorithms running on powerful-enough computers ever be conscious? What Loebner is saying, and I agree, is that the answer is no. Something more is needed.”

Basically, the limitation of the Turing test is that it is looking only at output, and therefore there is no way to distinguish the output of true consciousness from a really good simulation. This is not a new idea, and no one is moving the goalpost. We need to know something about how a computer is working to conclude whether or not it is conscious. What LLM experts will tell you is that these chatbots are just really good autocompletes – they are mimicking language, and since language is how we communicate thoughts, this creates the powerful illusion that they are mimicking thought, but they aren’t. They do not think, they do not truly understand.

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This interesting case was reported in the literature in 2007. For some reason it was then widely published in the mainstream media in 2015. Now it is making the rounds again on social media to support a false narrative about brain function. The story is of a 20 year old German woman who suffered a traumatic brain injury in a car accident. Over the next several months she started to slowly lose her vision – which is an important detail, it was not a sudden loss as a result of the physical trauma. After evaluation she was diagnosed with psychogenic blindness, meaning that it was not due to any physical damage to her visual system but was rather due to psychological stress. This patient also has what is now called dissociative disorder, or multiple personality, with 10 distinct personalities.

What makes the case even more interesting is that, with therapy, some of her personalities regained vision while others did not. Eventually eight of her ten personalities regained vision. This presented a rare, perhaps unique, opportunity to study the underlying neuroanatomical correlates of psychogenic blindness – what is happening in the brain when someone loses the ability for conscious sight despite their visual system working?

Psychogenic or functional neurological disorders are a complex and poorly understood phenomenon in which emotional stress and trauma presents as physical neurological symptoms. Common presentations include paralysis, language difficulty, sensory loss, and blindness. The diagnosis is mostly one of exclusion, which means sufficient examination and study is done to rule out any demonstrable damage, lesion, or other physical cause. This does not mean the patient is faking (technically called malingering) – that is a distinct condition that can usually be distinguished from a functional disorder. Usually patients with a functional disorder are very distressed by their symptoms and want further examination to find out what is wrong. In addition to simply ruling out physical causes, the diagnosis of a functional disorder can be supported by some positive evidence from the neurological exam. With psychogenic blindness, for example, patients will have normal pupillary responses (assuming no separate baseline deficit), and will have a normal reaction to optokinetic testing.  This involves moving vertical black and white stripes horizontally across their vision. This will cause an involuntary response of tracking the stripes with eye movements. If this happens then we know that visual information is getting in and making its way to the visual cortex.

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Remember The Last Starfighter from 1984? In that movie a trailer-park kid with limited prospects spends his time on an arcade-style video game, Starfighter. He plays the game so much that he beats the final level, and it turns out he is the first person to ever do so. He is heavily criticized for spending so much time playing a game, which is seen as a sign of boredom and lack of ambition – a waste of time. The twist (42 year old spoiler incoming) is that the game was actually a test (the Excalibur test – a deliberate reference to King Arthur) to find a skilled pilot for an actual real-life starfighter. He goes on to save the galaxy from invasion.

The interesting premise of the movie is that playing a video game is not only a test of real-life skill, but can be used to train such skill. In 1984 this was  kind of a new idea, and appealing to a generation of kids newly hooked on video games. Video games have been significantly mainstreamed over the last half century, but there is still a bit of a cultural stigma attached to them – they are seen as the realm of dorks and geeks, with inevitable jokes about how avid video gamers with “never get laid” (or something to that effect). Since the beginning of their popularity parents have worried, with such worry being fed by a sensationalist media, that video games were going to “rot” their kids’ brains, turn them into losers who can never get a skilled job, and might even cause violent behavior. Every mass shooting someone brings up violent video games.

But the evidence simply does not support these concerns. One big problem with the research is that it shows correlation only, not causation. Sure, people who play aggressive video games tend to be more aggressive, but that doesn’t mean the game is the cause. Further, there are many confounding factors, and more recent research shows that violence in the game is not the key feature. It has more to do with the level of difficulty and the resulting frustration that seems to raise aggression, not violence in the game. More competitive and difficult games tend to be more stimulating, regardless of the level of violence. The bottom line – after decades of research, systematic reviews conclude: “There is insufficient scientific evidence to support a causal link between violent video games and violent behavior.”

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Many people might find this to be an easy question and simple concept – what is your favorite color? In fact it was used as the quintessential easy question by the bridge guardian in Monty Python and the Holy Grail. But it is a good rule of thumb that everything is much more complicated than you think or than it may at first appear, and this is no exception. We recently had a casual discussion about this topic on the SGU, and it left me unsatisfied, so I thought I would do a deeper dive. Perhaps there is a neuroscientific answer to this question.

The panel differed in their reactions to the question of favorite color (we were just giving our subjective feelings, not discussing research or evidence). Cara felt that “favorite color” is largely arbitrary. Kids are asked to pick a favorite color, which they do (under pressure) and then often just stick with that answer as they get older. She also felt the question was meaningless without context – are you referring to clothes, cars, house color, or something else? Jay was at the other end of the spectrum – he has a strong affiliation for the color orange which gives him a pleasant feeling. The rest were somewhere in between these two extremes.

I knew there had to be a science of “favorite color”, which I thought might be interesting. Indeed there is – and it is interesting.

First, what is the distribution of favorite color, across the world and demographically? Blue is, far and away, the most favorite color, in most countries across the world, so it seems to be very cross-cultural. It is also the favorite across age groups and gender. The second-most favorite color is either green, red, or purple. Brown is almost universally the least favorite color. Gender has an effect on favorite color, with more women favoring pink, and reds in general (but still preferring blue overall). Republicans still prefer blue over red, but more Republicans prefer red than Democrats. There are country-specific differences as well. Red is a higher preference in China than many other countries, for example.

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I have a love-hate relationship with TikTok, as I do social media in general. It is a great communication tool and allows scientists and science communicators to get their content out to a larger audience cheaply and easily. If you know how to use the internet and social media as a resource, you can find a video about almost any topic. I particularly love the “how to” videos. And yet these applications are also used (mostly used) to spread nonsense and misinformation, or at least inaccurate, misleading, or overly generalized information. The low bar of entry cuts both ways.

As a result I spend part of my time as a communicator with my finger in the dike of social media pseudoscience and science denial. For example, this individual feels his insights into the workings of the human brain need to be shared with the world. His musings are based entirely on a false premise, his apparent misunderstanding of what neuroscientists understand about brain function. He begins with the nicely vague statement, “scientists have discovered”, followed by a completely incorrect statement – that thoughts come to our brain from outside the brain.

Before I get into this old “brain as receiver” claim, I want to point out that this format is extremely common on TikTok in particular and social media in general. This is more worrying than any individual claim – the culture is to present some random nonsense in the format of “isn’t this crazy”, or with with a cynical tone implying something nefarious is going on. Such authors may or may not believe what they say, they may just be trying to amplify their engagement with a total disregard toward whether what they are saying is true or not. They may even be a full Poe – knowing that what they say is nonsense. Either way, they feel it is appropriate to spend the time to record and upload a video without spending the few minutes that would be needed to check to see if what they are saying is even true. The very platform they are using to spread their nonsense often has all the information they need to answer their alleged questions. The culture is profoundly incurious, intellectually vacuous, lacking all scholarship or quality control, and seems to value only engagement. Thrown into the mix are true believers, grifters, and those who display classic symptoms of some form of thought disorder. This is “infotainment” taken to its ultimate expression.

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It’s not easy being a futurist (which I guess I technically am, having written a book about the future of technology). It never was, judging by the predictions of past futurists, but it seems to be getting harder as the future is moving more and more quickly. Even if we don’t get to something like “The Singularity”, the pace of change in many areas of technology is speeding up. Actually it’s possible this may, paradoxically, be good for futurists. We get to see fairly quickly how wrong our predictions were, and so have a chance at making adjustments and learning from our mistakes.

We are now near the beginning of many transformative technologies – genetic engineering, artificial intelligence, nanotechnology, additive manufacturing, robotics, and brain-machine interface. Extrapolating these technologies into the future is challenging. How will they interact with each other? How will they be used and accepted? What limitations will we run into? And (the hardest question) what new technologies not on that list will disrupt the future of technology?

While we are dealing with these big question, let’s focus on one specific technology – controllable robotic prosthetics. I have been writing about this for years, and this is an area that is advancing more quickly than I had anticipated. The reason for this is, briefly, AI. Recent advances in AI are allowing for far better brain-machine interface control than previously achievable. Recent advances in AI allow for technology that is really good at picking out patterns from tons of noisy data. This includes picking out patterns in EEG signals from a noisy human brain.

This matters when the goal is having a robotic prosthetic limb controlled by the user through some sort of BMI (from nerves, muscles, or directly from the brain). There are always two components to this control – the software driving the robotic limb has to learn what the user wants, and the user has to learn how to control the limb. Traditionally this takes weeks to months of training, in order to achieve a moderate but usable degree of control. By adding AI to the computer-learning end of the equation, this training time is reduced to days, with far better results. This is what has accelerated progress by a couple of decades beyond where I thought it would be.

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There are many ways in which our brains can be hacked. It is a complex overlapping set of algorithms evolved to help us interact with our environment to enhance survival and reproduction. However, while we evolved in the natural world, we now live in a world of technology, which gives us the ability to control our environment. We no longer have to simply adapt to the environment, we can adapt the environment to us. This partly means that we can alter the environment to “hack” our adaptive algorithms. Now we have artificial intelligence (AI) that has become a very powerful tool to hack those brain pathways.

In the last decade chatbots have blown past the Turing Test – which is a type of test in which a blinded evaluator has to tell the difference between a live person and an AI through conversation alone. We appear to still be on the steep part of the curve in terms of improvements in these large language model and other forms of AI. What these applications have gotten very good at is mimicking human speech – including pauses, inflections, sighing, “ums”, and all the other imperfections that make speech sound genuinely human.

As an aside, these advances have rendered many sci-fi vision of the future quaint and obsolete. In Star Trek, for example, even a couple hundred years in the future computers still sounded stilted and artificial. We could, however, retcon this choice to argue that the stilted computer voices of the sci-fi future were deliberate, and not a limitation of the technology. Why would they do this? Well…

Current AI is already so good at mimicking human speech, including the underlying human emotion, that people are forming emotional attachments to them, or being emotionally manipulated by them. People are, literally, falling in love with their chatbots. You might argue that they just “think” they are falling in love, or they are pretending to fall in love, but I see no reason not to take them at their word. I’m also not sure there is a meaningful difference between thinking one has fallen in love and actually falling in love – the same brain circuits, neurotransmitters, and feelings are involved.

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