Archive for the 'Neuroscience' Category

Sep 26 2022

The AI Renaissance

We appear to be in the middle of an explosion of AI (artificial intelligence) applications and ability. I had the opportunity to chat with an AI expert, Mark Ho, about what the driving forces are behind this rapid increase in AI power. Mark is a cognitive scientist who studies how AI’s work out and solve problems, and compares that to how humans solve problems. I was interviewing him for an SGU episode that will drop in December. The conversation was far-ranging, but I did want to discuss this one question – why are AIs getting so much more powerful in recent years?

First let me define what we mean by AI – this is not self-aware conscious computer code. I am referring to what may be called “narrow” AI, such as deep learning neural networks that can do specific things really well, like mimic a human conversation, reconstruct art images based on natural-language prompts, drive a car, or beat the world-champion in Go.  The HAL-9000 version of sentient computer can be referred to as Artificial General Intelligence, or AGI. But narrow AI does not really think, it does not not understand in a human sense. For the rest of this article when I refer to “AI” I am referring to the narrow type.

In order to understand why AI is getting more powerful we have to understand how current AI works. A full description would take a book, but let me just describe one basic way that AI algorithms can work. Neural nets, for example, are a network of nodes which also act as gates in a feed forward design (they pass information in one direction). The gates receive information and assign a weight to that information, and if it exceeds a set threshold it then passes that along to the next layers of nodes in the network. decide whether or not to pass information onto the next node based on preset parameters, and can give different weight to this information. The parameters (weights and thresholds) can be tuned to affect how the network processes information. These networks can be used for deep machine learning, which “trains” the network on specific data. To do this there needs to be an output that is either right or wrong, and that result is fed back into the network, which then tweaks the parameters. The goal is for the network to “learn” how it needs to process information by essentially doing millions of trials, tweaking the parameters each time and evolving the network in the direction of more and more accurate output.

So what is it about this system that is getting better? What others have told me, and what Mark confirmed, is that the underlying math and basic principles are essentially the same as 50 years ago. The math is also not that complicated. The basic tools are the same, so what is it that is getting better? One critical component of AI that is improving is the underlying hardware, which is getting much faster and more powerful. There is just a lot more raw power to run lots of training trials. One interesting side point is that computer scientists figured out that graphics cards (graphics processing unit, or GPU), the hardware used to process the images that go to your computer screen, happen to work really well for AI algorithms. GPUs have become incredibly powerful, mainly because of the gaming industry. This, by the way, is why graphics cards have become so expensive recently. All those bitcoin miners are using the GPUs to run their algorithms. (Although I recently read they are moving in the direction of application specific integrated circuits.)

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Sep 13 2022

Children Are Natural Skeptics

There is ongoing debate as to the extent that a skeptical outlook is natural vs learned in humans. There is no simple answer to this question, and human psychology is complex and multifaceted. People do demonstrate natural skepticism toward many claims, and yet seem to accept with abject gullibility other claims. For adults it can also be difficult to tease out how much skepticism is learned vs innate.

This is where developmental psychology comes in. We can examine children of various ages to see how they behave, and this may provide a window into natural human behavior. Of course, even young children are not free from cultural influences, but it at least can provide some interesting information. A recent study looked at two related questions – to children (ages 4-7) accept surprising claims from adults, and how do they react to those claims. A surprising claim is one that contradicts common knowledge that even a 4-year old should know.

In one study, for example, an adult showed the children a rock and a sponge and asked them if the rock was soft or hard. The children all believed the rock was hard. The adult then either told them that the rock was hard, or that the rock was soft (or in one iteration that the rock was softer than the sponge). When the adult confirmed the children’s beliefs, they continued in their belief. When the adult contradicted their belief, many children modified their belief. The adult then left the room under a pretense, and the children were observed through video. Unsurprisingly, they generally tested the surprising claims of the teacher through direct exploration.

This is not surprising – children generally like to explore and to touch things. However, the 6-7 year-old engaged in (or proposed during online versions of the testing) more appropriate and efficient methods of testing surprising claims than the 4-5 year-olds. For example, they wanted to directly compare the hardness of the sponge vs the rock.

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Sep 02 2022

Algorithms Still Reinforce Echochambers

Why do societies collapse? This is an interesting question, and as you might imagine the answer is complex. There are multiple internal and external reasons, but a core features seems to be that a combination of factors were simultaneously at work – a crisis that the society failed to deal with adequately because of dysfunctional institutions and political infrastructure. All societies face challenges, but successful ones solve them, or at least make significant adjustments. There are also multiple ways to define “collapse”, which does not have to involve complete extinction. We can also add political or institutional collapse, where, for example, a thriving democracy collapses into a dictatorship.

There are many people concerned that America is facing a real threat that could collapse our democracy. The question is – do we have the institutional vigor to make the appropriate adjustments to survive these challenges? Sometimes, by the time you recognize a serious threat it’s too late. At other times, the true causes of the threat are not recognized (at least not by a majority) and therefore the solutions are also missed. So the question is, to the extent that American democracy is under threat, what are the true underlying causes?

This is obviously a complex question that I am not going to be able to adequately address in one blog post. I would like to suggest, however, that social media algorithms are at least one factor contributing to the destabilizing of democracy. It would be ironic if one of the greatest democracies in world history were brought down in part by YouTube algorithms. But this is not implausible.

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Aug 23 2022

Do We Need a New Theory of Decision Making?

Published by under Neuroscience

How people make decisions has been an intense area of study from multiple angles, including various disciplines within psychology and economics. Here is a fascinating article that provides some insight into the state of the science addressing this broad question. It is framed as a meta-question – do we have the right underlying model that properly ties together all the various aspects of human decision-making? It is not a systematic review of this question, and really just addresses one key concept, but I think it helps frame the question.

The title reflects the author’s (Jason Collins) approach – “We don’t have a hundred biases, we have the wrong model.” The article is worth a careful read or two if you are interested in this topic, but here’s my attempt at a summary with some added thoughts. As with many scientific phenomena, we can divide the approach to human decision making into at least two levels, describing what people do and an underlying theory (or model) as to why they behave that way. Collins is coming at this mostly from a behavioral economics point of view, which starts with the “rational actor” model, the notion that people generally make rational decisions in their own self-interest. This model also includes the premise the individual have the computational mental power to arrive at the optimal decision, and the willpower to carry it out. When research shows that people deviate from a pure rational actor model of behavior, those deviations are deemed “biases”. I’ve discussed many such biases in this blog, and hundreds have been identified – risk aversion, sunk cost, omission bias, left-most digit bias, and others. It’s also recognized that people do not have unlimited computational power or willpower.

Collins likens this situation to the Earth-centric model of the universe. Geocentrism was an underlying model of how the universe worked, but did not match observations of the actual universe. So astronomers introduced more and more tweaks and complexities to explain these deviations. Perhaps, Collins argues, we are still in the “geocentrism” era of behavioral psychology and we need a new underlying model that is more elegant, accurate, and has more predictive power – a heliocentrism for human decision-making. He acknowledges that human behavior it too complex and multifaceted to follow a model as simple and elegant as, say, Kepler’s laws of planetary motion, but perhaps we can do better than the rational actor model tweaked with many biases to explain each deviation.

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Aug 08 2022

The Psychology of FOMO

Published by under Neuroscience

One of the many unintended consequences of social media is what is popularly referred to as FOMO – fear of missing out. People see all the wonderful things people are doing and buying in their social media profiles, and fear that they are missing out on the good life, or the latest trend, or perhaps some investment opportunity. This is the social media equivalent of “keeping up with the Joneses”. FOMO results from a basic human psychological tendency, to determine our own happiness by comparing ourselves to some relative standard, whether that’s our neighbors, our social group, or what we see on TV or on people’s Facebook pages.

This phenomenon also interacts with another, that we determine our happiness relative to our own current state, meaning that we habituate to our current situation. Functionally what this means is that if we want to remain happy we constantly need more – more than we have now, and more that other people have. The habituation phenomenon was humorously depicted in the video game, Portal 2 (an excellent game, highly recommended if you like video games). The main antagonist is an AI that is programmed to run the player through various testing scenarios. Each time the player completes a test the AI gets the silicon version of a dose of dopamine, but the digital Nirvana is short-lived and it has to run another test to maintain the good feeling. But it rapidly habituates to this feedback, with shorter and less intense reward meaning it has to test faster and harder.

This is essentially how humans function as well. We are never content. We cannot remain happy by standing still. We need whatever other people have, and we need more than we currently have. This lines up with research into happiness. Making more money does make people happier, up to the level where basic needs and security are met (in the US this is now about 75k per year). Some researchers frame this not as money making people happy, but rather not having enough money to meet basic needs is stressful and makes people unhappy. Beyond this basic level, increasing income does not correlate with happiness. Whether you make 75 thousand a year or 75 million a year does not matter. Further, everyone thinks that they would be happy if they just made 20% more than they currently make – regardless of how much that is. We habituate to our current situation and then think we need a little more to be happy.

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Aug 02 2022

Political Ideology and the Brain

Published by under Neuroscience

Political neuroscientists are trying to answer a basic question – what is the relationship between political ideology and brain function? Actually this is a horrifically complex question, but we are making some incremental progress, and a recent study adds a new layer of information. But let me first back up and give some thoughts on the entire enterprise.

Obviously political ideology is a brain phenomenon, in the way that all cognitive function is a brain phenomenon. But there are interesting deeper questions. How much of political ideology is learned or absorbed from the environment, and how much is a function of our genetic neurological predisposition (nature vs nurture)? The number one predictor of political ideology is the ideology of one’s parents. But this, of course, cuts both ways – we inherit genes from our parents, but they also dominantly affect the environment of our childhood. Twin studies (looking at the political ideology of twins separated at birth) suggest that political ideology is at least partly genetic. So unsurprisingly the answer is that genetic and environmental factors are likely working together to influence political ideology.

Another layer to consider is how we define political ideology? For studies based in the US, a typical liberal vs conservative scale is used. But we have to ask – is American liberal vs conservative politics fundamental to human psychology, or is it a particular cultural manifestation that may only indirectly relate to basic cognitive function? Perhaps, in other words, we’re looking in the wrong place, where the lighting is good but not necessarily where the phenomenon is really located. All research that looks for neuroanatomic correlates suffers from this fundamental question. If we look for the neuroanatomical correlates of depression, for example, we have to ask what depression is, and if it is a foundational phenomenon or an epiphenomenon. Is it a fundamental property of neurological functioning, or just a manifestation of a deeper neurological functioning?

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Jul 25 2022

The Alzheimer’s Research Fraud Case

Published by under Neuroscience

We are still relatively early in the investigation of possible fraud or misconduct relating specifically to amyloid beta (Aβ) in Alzheimer’s disease, so consider this all preliminary. However, independent analysis does allegedly find some highly suspect data in a series of images used in publications by one particular researcher, Sylvain Lesné of the University of Minnesota.  Science magazine has done a good review of what we know at this time. I will quickly review the status of this investigation and what the whole episode means for scientific research in general, and Alzheimer’s research in particular.

For background, Alzheimer’s disease (AD) is a complex neurodegenerative disease and the major cause of dementia, a chronic loss of global cognitive function, especially memory. AD has been extremely frustrating from a scientific and medical point of view. While there is a great deal of research and it has made impressive progress in understanding the pathophysiology of the disease, we remain without a single coherent “smoking gun” cause of the disease. The problem is (as with other neurodegenerative diseases) that there is a lot happening when brain cells age and die. The trick has been to not only identify markers of AD in the brain, but to understand what role those markers play in the disease. Specifically, are they driving the disease, or are they just a consequence of it?

In medicine there are two main ways to test a causal hypothesis for a correlational marker – can we transfer the disease by transferring the marker, and can we cure the disease by treating the marker? That last question is the ultimate goal of medical research, to find a cure, or at least a disease-modifying treatment. If we can prevent, slow down, stop the progression, or even reverse AD by interfering with one aspect of the disease, then that aspect is likely what’s driving it. But also – we have effective treatments, and everyone is happy.

This is where AD research gets very frustrating. Despite having many pathological clues to follow, researchers have been unable to close the loop – to find a disease-modifying treatment based on our basic science knowledge of AD. This leads to a lot of head-scratching and debate – is AD caused many by the build up of toxic proteins, by impaired neuronal function, by inflammation, or something else? One of the prominent theories of AD is that a major contributor to the disease is a build up of toxic protein, specifically amyloid beta (Aβ). But the Aβ theory has not led to a cure, which has led to many Aβ skeptics in the Alzheimer’s research community.

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Jul 22 2022

Overconfidence and Opposition to Scientific Consensus

There has been a lot of research exploring the phenomenon of rejection of established science, even to the point of people believing demonstrably absurd things. This is a complex phenomenon, involving conspiracy thinking, scientific illiteracy, group identity, polarization, cognitive styles, and media ecosystems, but the research has made significant progress unpacking these various contributing factors. A recent study adds to the list, focusing on the rejection of scientific consensus.

For most people, unless you are an expert in a relevant field, a good first approximation of what is most likely to be true is to understand and follow the consensus of expert scientific opinion. This is just probability – people who have an understanding of a topic that is orders of magnitude beyond yours are simply more likely to have an accurate opinion on that topic than you do. This does not mean experts are always right, or that there is no role for minority opinions. It mostly means that non-experts need to have an appropriate level of humility, and at least a basic understanding of the depth of knowledge that exists. I always invite people to consider the topic they know the best, and consider the level of knowledge of the average non-expert. Well, you are that non-expert on every other topic.

This is also why humility is the cornerstone of good scientific skepticism and critical thinking. We are all struggling to be just a little less wrong. As a science enthusiast we are trying to understanding a topic at a generally superficial technical level. This can still be a very meaningful and generally accurate understanding – just not technically deep or rigorous. It’s one thing to say – yeah, I get the basic concept of quantum computers, how they work, and why they can be so powerful. It’s another to be able to read and understand the technical literature, let alone contribute to it. Often people get into trouble when they confuse their lay understanding of a topic for a deep expert understanding, usually resulting in them becoming cranks.

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Jun 30 2022

Is Music Universal?

From a neurological and evolutionary perspective, music is fascinating. There seems to be a deeply rooted biological appreciation for tonality, rhythm, and melody. Not only can people find certain sequences of sounds to be pleasurable, they can powerfully evoke emotions. Music can be happy, sad, peaceful, foreboding, energetic or comical. Why is this? Music is also deeply cultural, with different cultures independently developing forms of music that are very different from each other. All human cultures have music, so the question is – to what extent are the details of musical appreciation universal vs culturally specific?

In Western music, for example, there are minor and major scales, chords, and keys. This refers to the combinations of notes or intervals between them. Music in a minor key tends to evoke emotions of sadness or foreboding, while those in a major key tend to evoke happiness or brightness. Would anyone from any culture interpret major and minor key music the same way? Research suggests that major and minor emotional effects are universal, but a recent study casts a little doubt on this conclusion.

The researchers looked at different subpopulations of people in Papua New Guinea, and both musicians and non-musicians in Australia. They chose Papua New Guinea because the people there share a common musical tradition, but vary in their exposure to Western music and culture. The experiment was simple – subjects were exposed to major and minor music and were asked to indicate if it made them feel happy or sad (the so-called emotional “valence”). Every group had the same emotional valence in response to major and  minor music – that is, except one. The one group that had essentially no exposure to Western culture and music did not have the same emotional reaction to music.

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Jun 27 2022

Flu Vaccines Associated With Reduced Alzheimer’s Risk

Published by under Neuroscience

A recent large population-based study shows a significant reduction in the risk of developing Alzheimer’s disease (AD) in older adults who received one or more flu vaccines. This follows previous studies showing a similar protective effect for other adult vaccines, and raises interesting questions regarding possible mechanisms.

AD is a degenerative neurological disease which is the most common cause of dementia, which is a syndrome of chronic global cognitive impairment. The prevalence of AD is about 6.5 million people in the US. The risk of developing AD increases with age, with 10.7% of those 65 and older being affected. AD is more than just having dementia, it is a specific disease that can only be confirmed at this time by looking at the brain. There are pathological changes including plaques and tangles. Imaging can also reveal diffuse cortical atrophy (shrinkage of the brain). Electroencephalogram typically shows slowing, indicating reduced cortical activity. There are many markers of AD that can be identified in the blood or spinal fluid, but none are good enough to be used in routine clinical evaluation.

The causes of AD are complex and not yet fully understood. In brief, a lot of stuff happens on the way towards brain cells dying but it’s hard to known which stuff is driving the process and which are a result of the process. It therefore has been difficult to device treatments to slow or prevent progression. For my entire career as a neurologist it seemed as if we were getting close to a significant clinical breakthrough, but we’re still waiting. Tremendous progress has been made, but nothing that amounts to a disease-modifying treatment.

Prevention before the disease becomes clinically apparent would be ideal. We know that controlling blood pressure and other markers of cardiovascular health are extremely important in reducing the risk of AD. We also know that physical exercise is important, as well as keeping mentally active. Sometimes, however, it is difficult to separate preventive measures that actually delay or slow the onset of AD vs those that mask the onset through creating a higher baseline of cognitive function. Regardless, all of the above are good lifestyle choices.

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