The details of the conspiracy theory itself are not the most interesting thing about QAnon. The core of this particular conspiracy is that Trump is secretly very competent, that he is investigating a world-wide sex-trafficking, demonic pedophilia ring run by the Democrats, and that Robert Mueller is secretly working with him and the whole Russia investigation is just a cover for this. Further, JFK Jr. faked his death in order to join Trump’s efforts, and is now the real person behind Q, the insider who is leaking information to the public in order to summon the faithful in this epic struggle.

This is all transparent nonsense, but it is no more nonsensical than the notion that the entire Apollo program was faked, that 9/11 was an inside job, or that the Earth is actually flat.

Some have argued that what is different about QAnon is that the deep state faction secretly running the government is this fantasy are the good guy, when is most conspiracies they are the bad guys. But this, I think, is a superficial narrative point. In grand conspiracies the conspiracy theorists are part of a small “woke” army of light trying to expose an even deeper malevolence, and QAnon fits that mold perfectly.

What’s different about QAnon is that it appears to be an evolution of the conspiracy theory into a new kind of phenomenon, one that combines elements from social media, video games, and live-action role playing. Like all conspiracy theories, QAnon offers an alternate version of reality. But in this case believers are more actively engaged. They are just reading and talking about the conspiracy, they are actively engaging in it. The mysterious person Q (not sure if it is actually one person at this point) will drop hints to followers about what is going on or what is about to happen. The Q conspiracy theorists then have to decode these secret messages. But further, Q will give tasks to its followers. These are usually small tasks, such as posting something on Facebook or Tweeting a message. But they could be bigger. They could involve action in meat-space, and even involve violence.

This is part of a more general phenomenon, called internet role-play. This is just another form of fantasy role-playing using a new medium. In the 1970s and 80s table-top roleplaying became popular, with five or so people sitting around a table rolling dice. Then live-action roleplaying took off, with tens to hundreds of people gathering at a camp site or other venue, dressed as their characters, for a weekend of immersive roleplaying. Now, you can engage in a roleplaying game without leaving your computer chair.

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Recent mass shootings have once again fueled discussion about the role of video game violence (VGV) and aggressive behavior. This is an enduring controversy, which is a real scientific controversy (not just a political one) because the research is highly complex.

Part of that complexity is that there is just one question, does VGV cause aggressive behavior – there are many subquestions, and many ways to measure outcomes. Research can focus on whether or not VGV is correlated with aggressive attitudes, aggressive behavior, or with diminished prosocial attitudes or behavior, or empathy towards the victims of violence, or normalizing aggressive or violent behavior. If there is a correlation, then research needs to tease apart what is cause and what is effect. Researchers also have to decide how to measure all of these things, and to consider demographic variables as well as duration and intensity of exposure and duration of any potential effects. Finally there is the issue of confounding factors, always an issue with psychological research – how do we establish the true lines of cause and effect.

Right now there appears to be two basic schools of thought. Anderson and colleagues champion the view that there is strong evidence for not only a correlation between VGV and aggressive behavior, experimental studies have shown that VGV causes aggressive ideation and behavior, and reduces empathy and prosocial behavior. A 2018 meta-analysis shows that these correlations are indeed strong, and exist across experimental and observational studies. These effects are greatest for males and for whites, less so for Asians, and not significant for Hispanics.

The other school is championed by Ferguson and others, who argue that these results are spurious and due to poor research designs. Specifically he argues that the effects are inflated by including measures of aggression that are too mild, and not ultimately meaningful. There is only an effect if you include things like aggressive language, but not if you restrict the definition of aggressive behavior to actual violence. Further, he argues, that confounding factors are not adequately controlled for, and when you do, the effect disappears.

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Are we eating apples wrong? An ABC news headline reads, “If you aren’t eating the whole apple, you might be eating it the wrong way, a study finds.” This reporting is based on this study, which is a comparison of the bacterial content of different parts of apples, and comparing organically grown to conventionally grown apples. The study found that there was a different bacterial composition between the conventional and organic apples, and that the core and seeds of the apples contained about 10 times the bacteria as the flesh. The authors also conclude:

“Moreover, organic apples conceivably feature favorable health effects for the consumer, the host plant and the environment in contrast to conventional apples, which were found to harbor potential food-borne pathogens.”

All the usual problems I often complain about are present with this conclusion, starting with the fact that it is absolutely not justified by the actual data. First, this is a small preliminary study, of the sort that the media should not even report on. At best this type of study can generate a hypothesis to be tested. The researchers compared a grand total of four apples each from two orchards, one organic and one conventional. Right there you can probably see the problem.

All the apples were of one cultivar, so we cannot generalize the findings to other cultivars. But even worse, only two orchards were compared. Even if you sampled a thousand apples from each orchard, you are still only comparing two orchards.  Elisabeth M Bik, a microbiota researcher, already commented on the study, pointing out that –

For example, the orchards could also have different sun exposures, soil characteristics, age of trees, harvesting techniques and storage conditions, etc. These are all parameters that are not associated with organic vs conventional farming, but that could still have a big impact on the microbiome composition.

Exactly. There are many variables, and absolutely no way for the researchers to isolate the organic farming as the one variable that correlates with the changes they saw. Even worse, and I have no idea why they did this, the organic apples were picked and sampled fresh, while the conventional apples were stored in plastic prior to examination. Why introduce another variable like that? Bacterial populations are certain to change over time after harvesting, and based on storage conditions. So – do I have to say it – this study was not comparing apples to apples while they were comparing apples to apples.

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The BBC reports a case in which an artificial intelligence (AI) system is named as a possible patent holder for a new invention, and interlocking food container. Apparently none of the people involved with the invention meet the criteria for being a patent holder, since they did not come up with the actual innovation.

As a result, two professors from the University of Surrey have teamed up with the Missouri-based inventor of Dabus AI to file patents in the system’s name with the relevant authorities in the UK, Europe and US.

That’s an interesting solution. It does seem that international patent law needs to evolve in order to deal with the product of machine learning creativity. I think this reveals what a true game-changer current AI can be. It’s breaking our existing categories and legal framework.

But I don’t want to talk about patent law, about which I have no expertise – I want to talk about AI, about which I also have no expertise (but I do have a keen interest and pay attention to the news). Over the last few years there have been numerous developments that show how powerful machine learning algorithms are becoming. Specifically, they are able to create solutions that the AI programmers themselves don’t fully understand. The Dabus system itself uses one component to generate new ideas, based on being fed noisy input. But then a second component evaluates those ideas and gives the first component feedback. This idea, having two AI systems play off each other, creates a feedback loop that can rapidly iterate and improve a design or solution. So essentially we have two AIs talking to each other, and humans are largely out of the loop.

AI systems have come up with simulations and other solutions that the scientists using the system do not understand. Sometimes they don’t even know how it was possible for the AI to come up with the solutions it did.

Even more interesting, AI systems have developed their own language that they use to communicate with each other, and no human currently understands that language.

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A 2015 Pew survey found that 88% of AAAS scientists believe that GMOs (genetically modified organisms) are generally safe to eat, while only 37% of the general public did. This was the biggest gap, 51%, of any science attitude they surveyed – greater than evolution or climate change. This hasn’t changed much since. A 2018 Pew survey found that 49% of US adults think that GMOs are worse for your health. These numbers are also similar in other countries.

An important underlying question for science communicators is – what is the source and therefore potential solution to this disconnect between experts and the public? In other words – what drives anti-scientific or pseudoscientific attitudes in the public? The classic answer is the knowledge deficit model, that people reject science because they don’t understand it. If true, then the answer is science education and fostering greater scientific literacy.

However, psychological research over the last two decades has called into question the knowledge deficit model. Studies have found that giving facts often has not result, or may even create a backfire effect (although to scope of this is still controversial). Some research suggests you have to confront a person’s explanatory narrative and replace it with another. Others indicate that ideological beliefs are remarkably resistant to alteration with facts alone.

But the knowledge deficit model is not dead yet. It seems that we have to take a more nuanced approach to unscientific beliefs in the public. This is a heterogeneous phenomenon, with multiple causes and therefore multiple potential solutions. For each topic we need to understand what is driving that particular belief, and then tailor an approach to it.

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