Since the topic of artificial intelligence has garnered so much interest, and there were many excellent follow up questions, I thought I would dedicate my blog today to answering them and extending the discussion.

Noël Henderson asked: “Would a non-chemical AI unit, even with very complex processing and memory capabilities, be able to experience what we normally refer to as emotion? Is self-awareness (and in my layman’s understanding I tend to think in terms of ‘ego’) dependent upon the ability to experience emotion?”

Emotion is just one more thing that our brains do. There is no reason that AI with a different substrate cannot also create the experience of emotions. Emotions are a manifestation of how information is processed in the brain – which is why I said that an AI brain would not only have to be able to hold the information of the brain but would also need to duplicate its processing of that information. For example, the knowledge of the death of someone that the AI brain has learned to associate with positive feelings could produce the experience of sadness and loss by affecting the degree of activity in patterns of circuits that contribute to mood, focus our attention on pleasant or unpleasant details, make us anticipate our future happiness, etc. Basically, the same thing that happens in a biological brain. In other words, the experience of emotion is just as much a physical aspect of the brain as any other cognitive phenomenon.

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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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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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We have all likely had the experience that when we learn a task it becomes easier to learn a distinct but related task. Learning to cook one dish makes it easier to learn other dishes. Learning how to repair a radio helps you learn to repair other electronics. Even more abstractly – when you learn anything it can improve your ability to learn in general. This is partly because primate brains are very flexible – we can repurpose knowledge and skills to other areas. This is related to the fact that we are good at finding patterns and connections among disparate items. Language is also a good example of this – puns or witty linguistic humor is often based on making a connection between words in different contexts (I tried to tell a joke about chemistry, but there was no reaction).

Neuroscientists are always trying to understand what we call the “neuroanatomical correlates” of cognitive function – what part of the brain is responsible for specific tasks and abilities? There is no specific one-to-one correlation. I think the best current summary of how the brain is organized is that it is made of networks of modules. Modules are nodes in the brain that do specific processing, but they participate in multiple different networks or circuits, and may even have different functions in different networks. Networks can also be more or less widely distributed, with the higher cognitive functions tending to be more complex than specific simple tasks.

What, then, is happening in the brain when we exhibit this cognitive flexibility, repurposing elements of one learned task to help learn a new task? To address this question Princeton researchers looked at rhesus macaques. Specifically they wanted to know if primates engage in what is called “compositionality” – breaking down a task into specific components that can then be combined to perform the task. Those components can then be combined in new arrangements to compose a new task, like building with legos.

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This article is from a year ago, but it was just sent to me as it is making the rounds in climate change denying circles. It is by Paul Macrae, who is an ex-journalist who now seems to be primarily engaged in climate change denial. The article (a chapter from his book on the subject) is full of the standard climate denial tropes – for the sake of space, I would like to focus on three specific points. The first is the claim that climate science is “settled”, the second is the notion of “post-normal science”, and the third is a factual claim about the accuracy of prior climate models.

Of course, if there is a consensus among climate scientists that global warming (I will get into more details on what this means) is “settled”, that makes it difficult, especially for  a non-scientists, to question the conclusion. So order number one – deny that there is a consensus, deny that consensus is even a thing in science, and deny that science can ever be settled.  I don’t suspect that I will ever be able to slay this dragon, it is simply too useful rhetorically, but for those who are open to argument, here is my analysis.

First – consensus is absolutely a thing in the regular operations of science. A consensus can be built in a number of ways, but often panels of recognized world experts are assembled to review all existing scientific data and make a consensus statement about what the data shows. This is often done when there is a policy or practice question. For example, in medicine, practitioners need to know how to practice, and these consensus statements are used as practice guidelines. They also set the standard of care, so as a practitioner you should definitely be aware of them and not violate them unless you have a good reason. Obviously, the question of global warming is a serious policy question, and so providing scientific guidance to policy makers is the point, such as with the IPCC. Consensus is also used to set research and funding priorities, to establish terminology, and resolve controversies. But to be clear – these mechanisms of consensus do not determine what the science says. That is determined by the actual science. The point is to provide clarity regarding complex scientific evidence, especially when a practice or policy is at issue.

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I am currently in Dubai at the Future Forum conference, and later today I am on a panel about the future of the mind with two other neuroscientists. I expect the conversation to be dynamic, but here is the core of what I want to say.

As I have been covering here over the years in bits and pieces, there seems to be several technologies converging on at least one critical component of research into consciousness and sentience. The first is the ability to image the functioning of the brain, in addition to the anatomy, in real time. We have functional MRI scanning, PET, and EEG mapping which enable us to see cerebral blood flow, metabolism and electrical activity. This allows researchers to ask questions such as: what parts of the brain light up when a subject is experiencing something or performing a specific task. The data is relatively low resolution (compared to the neuronal level of activity) and noisy, but we can pull meaningful patterns from this data to build our models of how the brain works.

The second technology which is having a significant impact on neuroscience research is computer technology, including but not limited to AI. All the technologies I listed above are dependent on computing, and as the software improves, so does the resulting imaging. AI is now also helping us make sense of the noisy data. But the computing technology flows in the other direction as well – we can use our knowledge of the brain to help us design computer circuits, whether in neural networks or even just virtually in software. This creates a feedback loop whereby we use computers to understand the brain, and the resulting neuroscience to build better computers.

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I was away on vacation the last week, hence no posts, but am now back to my usual schedule. In fact, I hope to be a little more consistent starting this summer because (if you follow me on the SGU you already know this) I am retiring from my day job at Yale at the end of the month. This will allow me to work full time as a science communicator and skeptic. I have some new projects in the works, and will announce anything here for those who are interested.

On to today’s post – I recently received an e-mail from Janyce Boynton, a former facilitator who now works to expose the pseudoscience of facilitated communication (FC). I have been writing about this for many years. Like many pseudosciences, they rarely completely disappear, but tend to wax and wane with each new generation, often morphing into different forms while keeping the nonsense at their core. FC has had a resurgence recently due to a popular podcast, The Telepathy Tapes (which I wrote about over at SBM). Janyce had this to say:

I’ll be continuing to post critiques about the Telepathy Tapes–especially since some of their followers are now claiming that my student was telepathic. Their “logic” (and I use that term loosely) is that during the picture message passing test, she read my mind, knew what picture I saw, and typed that instead of typing out the word to the picture she saw.

I shouldn’t be surprised by their rationalizations. The mental gymnastics these people go through!

They’re also claiming that people don’t have to look at the letter board because of synesthesia. According to them, the letters light up and the clients can see the “aura” of each color. Ridiculous. I haven’t been able to find any research that backs up this claim. Nor have I found an expert in synesthesia who is willing to answer my questions about this condition, but I’m assuming that, if synesthesia is a real condition, it doesn’t work the way the Telepathy Tapes folks are claiming it does.

For quick background, FC was created in the 1980s as a method for communicating to people, mostly children, who have severe cognitive impairment and are either non-verbal or minimally verbal. The hypothesis FC is based on is that at least some of these children may have more cognitive ability than is apparent but rather have impaired communication as an isolated deficit. This general idea is legitimate, and in neurology we caution all the time about not assuming the inability to demonstrate an ability is due purely to a cognitive deficit, rather than a physical deficit. To take a simple example, don’t assume someone is not responding to your voice because they have impaired consciousness when they could be deaf. We use various methods to try to control for this as much as possible.

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Humans (assuming you all experience roughly what I experience, which is a reasonable assumption) have a sense of self. This sense has several components – we feel as if we occupy our physical bodies, that our bodies are distinct entities separate from the rest of the universe, that we own our body parts, and that we have the agency to control our bodies. We can do stuff and affect the world around us. We also have a sense that we exist in time, that there is a continuity to our existence, that we existed yesterday and will likely exist tomorrow.

This may all seem too basic to bother pointing out, but it isn’t. These aspects of a sense of self also do not flow automatically from the fact of our own existence. There are circuits in the brain receiving input from sensory and cognitive information that generate these senses. We know this primarily from studying people in whom one or more of these circuits are disrupted, either temporarily or permanently. This is why people can have an “out of body” experience – disrupt those circuits which make us feel embodied. People can feel as if they do not own or control a body part (such as so-called alien hand syndrome). Or they can feel as if they own and control a body part that doesn’t exist. It’s possible for there to be a disconnect between physical reality and our subjective experience, because the subjective experience of self, of reality, and of time are constructed by our brains based upon sensory and other inputs.

Perhaps, however, there is another way to study the phenomenon of a sense of self. Rather than studying people who are missing one or more aspects of a sense of self, we can try to build up that sense, one component at a time, in robots. This is the subject of a paper by three researchers, a cognitive roboticist, a cognitive psychologist who works with robot-human interactions, and a psychiatrist. They explore how we can study the components of a sense of self in robots, and how we can use robots to do psychological research about human cognition and the self of self.

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This is a great scientific question because it challenges how we ask and answer scientific questions. Are animals conscious? This is a question discussed in a recent BBC article that piqued my interest. They eventually get to a question that they should have opened with – how do we specifically define “consciousness”? We can’t answer questions about an alleged phenomenon unless we know what it is. Ideally we would have an operational definition, a list of inclusion and exclusion criteria that need to be met to fit the definition.

So I am going to start with this question – how do we define consciousness? I think there are at least two different contexts here. In medicine we use the term to refer to different states in people. We know, from our own experience, that humans are conscious, and from one point of view we define consciousness as what humans experience. We assume other fully functioning humans are conscious because we are, and there is no reason to think that other beings with brains similar to our own have a fundamentally different phenomenon driving their behavior. In fact part of consciousness is a theory of mind, which is the ability to think about what other beings think and feel.

So when we talk about consciousness in humans the question revolves around the health and functioning of the brain. Someone might be unconscious, or comatose, or vegetative. We label these as “disorders of consciousness”. We might also discuss consciousness in the context of healthy altered states, such as sleeping. Here we do have very specific technical definitions, based upon neurological examination. However, even here our definition is being challenged by new technology, such as functional MRI scanning, which may shows signs of subtle consciousness in someone who does not show signs on exam.

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