There is something extraordinarily rich in the intersection of computer science and journalism. It feels like there’s a nascent field in the making, tied to the rise of the internet. The last few years have seen calls for a new class of  “programmer journalist” and the birth of a community of hacks and hackers. Meanwhile, several schools are now offering joint degrees. But we’ll need more than competent programmers in newsrooms. What are the key problems of computational journalism? What other fields can we draw upon for ideas and theory? For that matter, what is it?

I’d like to propose a working definition of computational journalism as the application of computer science to the problems of public information, knowledge, and belief, by practitioners who see their mission as outside of both commerce and government. This includes the journalistic mainstay of “reporting” — because information not published is information not known — but my definition is intentionally much broader than that. To succeed, this young discipline will need to draw heavily from social science, computer science, public communications, cognitive psychology and other fields, as well as the traditional values and practices of the journalism profession.

“Computational journalism” has no textbooks yet. In fact the term barely is barely recognized. The phrase seems to have emerged at Georgia Tech in 2006 or 2007. Nonetheless I feel like there are already important topics and key references.

Data journalism
Data journalism is obtaining, reporting on, curating and publishing data in the public interest. The practice is often more about spreadsheets than algorithms, so I’ll suggest that not all data journalism is “computational,” in the same way that a novel written on a word processor isn’t “computational.” But data journalism is interesting and important and dovetails with computational journalism in many ways.

• The Nieman Journalism Lab’s interview with Guardian Data Blog editor Simon Rogers remains a solid introduction to (one kind of) contemporary practice.
• The best practical guides I know are Rogers’ “How to: get to grips with data journalism” and Dan Nguyen’s series of data-scraping tutorials at ProPublica.
• Stanford’s Journalism in the Age of Data is an hour-long documentary on data journalism and visualization.
• The web is a linked system of human-readable documents. Now Tim Berners-Lee wants to create a web of machine-readable linked data. The full potential is unclear, but it’s a big idea that may come to be the backbone of semantic web visions. The New York Times, The Guardian, and others are experimenting with open data APIs.
• Everyblock creator Adrian Holovaty seems to have been the first to suggest that reporters file structured data in his 2006 “A Fundamental Way Newspaper Websites Need to Change.” This idea is beautifully expanded in Stijn Debrouwere’s “Information Architecture for News Websites” series.

Visualization
Big data requires powerful exploration and storytelling tools, and increasingly that means visualization. But there’s good visualization and bad visualization, and the field has advanced tremendously since Tufte wrote The Visual Display of Quantitative Information. There is lots of good science that is too little known, and many open problems here.

• Tamara Munzner’s chapter on visualization is the essential primer. She puts visualization on rigorous perceptual footing, and discusses all the major categories of practice. Absolutely required reading for anyone who works with pictures of data.
• Ben Fry invented the Processing language and wrote his PhD thesis on “computational information design,” which is his powerful conception of the iterative, interactive practice of designing useful visualizations.
• How do we make visualization statistically rigorous? How do we know we’re not just fooling ourselves when we see patterns in the pixels? This amazing paper by Wickham et. al. has some answers.
• Is a visualization a story? Segal and Heer explore this question in “Narrative Visualization: Telling Stories with Data.”

Computational linguistics
Data is more than numbers. Given that the web is designed to be read by humans, it makes heavy use of human language. And then there are all the world’s books, and the archival recordings of millions of speeches and interviews. Computers are slowly getting better at dealing with language.

• Word frequency techniques like tf-idf and the vector space document model are very simple and very useful. See also stemming. Lots more in the wonderful (and free!) Introduction to Information Retrieval. This book explains how search engines are built, and  discusses tf-idf etc. in great technical detail.
• Statistical language models are increasingly important for all kinds of applications. Michael Nielsen has a great introduction to statistical machine translation. Google’s Peter Norvig discusses how he implemented statistical spelling correction on his laptop during a long plane flight. For the full deal, see the book Foundations of Statistical Natural Language Processing.
• On a related note, Google N-gram viewer lets you look at the frequency of short phrases within 4% of all books published, ever. The excellent paper gives examples of how to use this for cultural research. Dan Cohen has important criticisms.
• Speech-to-text algorithms enable automated transcription, and Matt Thompson explores the huge implications for journalism.
• Reuters maintains the OpenCalais entity extraction service, which parses text to contextually determine who and what is referenced.
• IBM’s Watson project built a question-answering system that reads reference books and wins at Jeopardy. Imagine how useful to journalists and curious readers this could be! This paper on the DeepQA system describes how they did it.

Communications technology and free speech
Code is law. Because our communications systems use software, the underlying mathematics of communication lead to staggering political consequences — including whether or not it is possible for governments to verify online identity or remove things from the internet. The key topics here are networks, cryptography, and information theory.

• The Handbook of Applied Cryptography is a classic, and free online. But despite the title it doesn’t really explain how crypto is used in the real world, like Wikipedia does.
• It’s important to know how the internet routes information, using TCP/IP and BGP, or at a somewhat higher level, things like the BitTorrent protocol. The technical details determine how hard it is to do things like block websites, suppress the dissemination of a file, or remove entire countries from the internet.
• Anonymity is deeply important to online free speech, and very hard. The Tor project is the outstanding leader in anonymity-related research.
• Information theory is stunningly useful across almost every technical discipline. Pierce’s short textbook is the classic introduction, while Tom Schneider’s Information Theory Primer seems to be the best free online reference.

Tracking the spread of information (and misinformation)
What do we know about how information spreads through society? Very little. But one nice side effect of our increasingly digital public sphere is the ability to track such things, at least in principle.

• Memetracker was (AFAIK) the first credible demonstration of whole-web information tracking, following quoted soundbites through blogs and mainstream news sites and everything in between. Zach Seward has cogent reflections on their findings.
• The Truthy Project aims for automated detection of astro-turfing on Twitter. They specialize in covert political messaging, or as I like to call it, computational propaganda.
• We badly need tools to help us determine the source of any given online “fact.” There are many existing techniques that could be applied to the problem, as I discussed in a previous post.
• If we had information provenance tools that worked across a spectrum of media outlets and feed types (web, social media, etc.) it would be much cheaper to do the sort of information ecosystem studies that Pew and others occasionally undertake. This would lead to a much better understanding of who does original reporting.

Filtering and recommendation
With vastly more information than ever before available to us, attention becomes the scarcest resource. Algorithms are an essential tool in filtering the flood of information that reaches each person. (Social media networks also act as filters.)

• The paper on preference networks by Turyen et. al. is probably as good an introduction as anything to the state of the art in recommendation engines, those algorithms that tell you what articles you might like to read or what movies you might like to watch.
• Before Google News there was Columbia News Blaster, which incorporated a number of interesting algorithms such as multi-lingual article clustering, automatic summarization, and more as described in this paper by McKeown et. al.
• Anyone playing with clustering algorithms needs to have a deep appreciation of the ugly duckling theorem, which says that there is no categorization without preconceptions. King and Grimmer explore this with their technique for visualizing the space of clusterings.
• Any digital journalism product which involves the audience to any degree — that should be all digital journalism products — is a piece of social software, well defined by Clay Shirky in his classic essay, “A Group Is Its Own Worst Enemy.” It’s also a “collective knowledge system” as articulated by Chris Dixon.

Measuring public knowledge
If journalism is about “informing the public” then we must consider what happens to stories after publication — this is the “last mile” problem in journalism. There is almost none of this happening in professional journalism today, aside from basic traffic analytics. The key question here is, how does journalism change ideas and action? Can we apply computers to help answer this question empirically?

• World Public Opinion’s recent survey of misinformation among American voters solves this problem in the classic way, by doing a randomly sampled opinion poll. I discuss their bleak results here.
• Blogosphere maps and other kinds of visualizations can help us understand the public information ecosystem, such as this interactive visualization of Iranian blogs. I have previously suggested using such maps as a navigation tool that might broaden our information horizons.
• UN Global Pulse is a serious attempt to create a real-time global monitoring system to detect humanitarian threats in crisis situations. They plan to do this by mining the “data exhaust” of entire societies — social media postings, online records, news reports, and whatever else they can get their hands on. Sounds like key technology for journalism.
• Vox Civitas is an ambitious social media mining tool designed for journalists. Computational linguistics, visualization, and more.

Research agenda
I know of only one work which proposes a research agenda for computational journalism.

• “Computational Journalism: A Call to Arms for Database Researchers” by Sarah Cohen et. al. raises the very intriguing possibility of building systems that automatically or semi-automatically scan databases for stories, document the rationale for believing certain facts, etc.

This paper presents a broad vision and is really a must-read. However, it deals almost exclusively with reporting, that is, finding new knowledge and making it public. I’d like to suggest that the following unsolved problems are also important:

• Tracing the source of any particular “fact” found online, and generally tracking the spread and mutation of information.
• Cheap metrics for the state of the public information ecosystem. How accurate is the web? How accurate is a particular source?
• Techniques for mapping public knowledge. What is it that people actually know and believe? How polarized is a population? What is under-reported? What is well reported but poorly appreciated?
• Information routing and timing: how can we route each story to the set of people who might be most concerned about it, or best in a position to act, at the moment when it will be most relevant to them?

This sort of attention to the health of the public information ecosystem as a whole, beyond just the traditional surfacing of new stories, seems essential to the project of making journalism work.

It is now possible to see what a person is looking at by scanning their brain. The technique, published last November by a team of Japanese neuroscientists, uses FMRI to reconstruct a digital image of the picture entering the eye, albeit at very low resolution and only after hundreds of training runs. Still, it’s an awesome development, and many articles covering this research have called it “mind reading” (1, 2, 3, 4, 5). But it really isn’t, and it’s fun to explore what real “mind reading” would imply.

When I hear “mind reading” I want psychic abilities. I want to be able to know what number you’re thinking of, where you were on the night of March 4th, and what you actually think of my souffle. This is the sort of technology that could be badly misused, as the comments on one blog note:

Am I the only one finding this DEEPLY disturbing? It opens the doors to some of the scariest 1984-style total-control future predictions. Imagine you can’t hide your f#&%!ng MIND!

Fortunately, we’re not there yet. Morover, if we did have the technology to read minds, we’d have much bigger societal issues than privacy to deal with. The existence of “mind reading machines” would imply that we possessed good formal models of the human mind, and that is a can of worms.

But back to today. The paper by Yoichi Miyawaki and colleagues describes a technique for exploiting retinotopy, the fact that certain areas of the visual cortex are direct “maps” of the retina. First, a series of 10×10 black and white test images are shown to a someone while their neural activation is recorded by FMRI. The responses to these test images are used to ascertain which areas of the visual cortex correspond to which areas of the subject’s field of vision. When the neural map is complete, it can be read “backwards,” going from neural scanner results to a low resolution representation of whatever the subject is currently looking at.

This is a long way from a tool for the thought-police. First, the algorithm requires training on each new person. Also, an MRI machine is a huge, expensive, complicated piece of machinery which requires the subject to stay very still over a period of minutes — widespread brain scanning is, for the moment, completely out of the question. But most fundamentally, the information recovered is nothing more than what the eye is currently looking at. You might as well just tape a digital camera to the subject’s head. The pictures would be a lot better.

What is it that we imagine for a mind reading machine? Perhaps a printout, in words, of every thought that goes through someone’s mind. But do people really think exclusively in words? What about their emotions, or their unconscious responses, or even the complete set of minor joint aches and temperature sensations all over their body? Or how about a video playback of the events of yesterday evening? Impossible, because that’s not how human memory works. When we think about it carefully, we realize that we have an extremely poor conception of what is actually “in someone’s head.”

Compounding this problem is the fact we can’t even say what’s in our own heads. We think we can, but we can’t. Decades of psychological experiments show that access to the contents of our own minds and the working of our own thought processes is very limited. Consequently, we cannot answer the question “what would a mind-reader read?” through introspection.

This is why, before we could build a mind-reading machine, we would first need formal models of a “mind.” We need the sort of mathematical models that one can manipulate with a computer, because computers will surely be intensely involved in any mind reading technology. If recent developments in linguistics and artificial intelligence research are any guide, these models will be huge, associative, and statistical in nature, nothing like the structured logic we think we possess. For example, Google translates web pages between different languages without using anything like formal grammar models.

In other words, we cannot “read minds” because we have very little idea of how minds might be stored on a computer. This problem is known in AI as “knowledge representation,” and we still know very little about it.

Good formal models of the mind, if possible, are the technological precursor to entire fields of information engineering, and this is why I’m not worried about mind-reading technology per se. We’ll get beneficial things like accurate machine translation and computers that respond to voice queries — no more fighting with software that just doesn’t understand what you want. (Think also of the possibilities for art and expression.) We’ll also get uncomfortable technologies like sickeningly effective advertisements that exploit behavioral quirks we didn’t know we had, and NSA-funded conversation snooping programs that make existing keyword scanners look like the toys that they are. Finally, it would be possible to use accurate human mind models for pure evil: imagine a computer virus that was designed to read your personal files and figure out how best to convince you that the Dictator was beneficent. All of this may sound very far-fetched, but we’re going to build these things if we possibly can: think of how much money Google makes from each percentage point of improvement in ad clickthroughs.

If the Japanese FMRI technique seems positively simplistic in this light, that’s because it is. They have read retinas, not minds. They are extracting a representation we already have abundant experience with: images. Saying that we’ve made a step towards reading minds is ridiculous; Thomas Edison might just as well have claimed to “record thoughts” when he announced the phonograph.

I bother with all of this both because I think science journalism is often done badly, and because I believe that it’s important to get hysterical about the right thiings. One comment posted to a video of the research reads, “this is the beginning of the end of free thought.” Perhaps the continuation of this type of FMRI research really will one day lead to the ability to determine what someone is thinking without invoking their consent, but torture already does that. To me, the ability to represent someone’s thoughts in electronic form has far greater implications than mind-reading per se, and this sort of FMRI research — as impressive as it is — contributes little to that enterprise.

The “beauty myth” holds that attractiveness is a cultural construct, a control mechanism of the patriarchy, a way to sell us cosmetics we do not need. Modern social theory says that the beauty of the face and body depends on when and whom you ask, and that we could learn to love anyone if only our iron-maiden social norms permitted it. This is a pleasing thought. It says that accidents of ancestry do not truly count. Unfortunately, when we actually do the experiments and test nature versus nurture in the case of facial attractiveness, genetics wins. Facial beauty is, quite emphatically, not culturally defined.

The best survey is a meta-analysis by Langlois and colleagues, published in the Psychological Bulletin in 2000. They did an exhaustive search of the experimental literature on facial “attractiveness” and ended up including 919 separate experiments on how attractiveness is perceived, how we judge ourselves and others based on attractiveness, and how attractive versus unattractive people actually behave. It turns out that facial beauty is “real” in the sense that it does not depend on who you ask; ratings of attractiveness are very stable across ethnicities, cultures, age, and gender:

Both our cross-cultural and cross-ethnic agreement effect sizes are more than double the size necessary to be considered large, suggesting a possibly universal standard by which attractiveness is judged. These analyses seriously question the common assumption that attractiveness ratings are culturally unique and merely represent media-induced standards. These findings are consistent with the fact that even young infants prefer the same faces as adults.

If beauty was a cultural construct, we would expect different cultures to rate the same people differently. However, we might imagine that global media has erased the diversity that used to exist. This is why the experiments with children and babies are so important: if beauty is learned, then children would have to learn it, presumably beginning in early childhood. Yet the age of the beholder seems not to matter at all in subjective rankings of the attractiveness of others, and even newborn babies will look longer at photographs of people that adults have judged “attractive.” This cannot be learned behavior.

When we check the evidence, (facial) beauty standards seem to be universal and innate (biological). Doubtless, this is going to upset some people.

Here is what I think happened: we saw beauty used as a weapon. We saw women especially destroying their self-image under the assumption that they were ugly. Capitalism grew up around selling to our insecurities, and yes, the patriarchy said that girls were only as valuable as their looks. In defense, we decided that beauty was a myth. We tried to escape from these traps by telling ourselves that the object of contention didn’t exist.

But it does. Some people really are more beautiful than others, says our universal physiological nature. Accepting this implies a new social project. Rather than trying to teach ourselves that beauty does not exist, perhaps we should be trying to decouple value from beauty. It’s not that everyone is equally attractive, it’s just that attractiveness, like intelligence, doesn’t actually make you a “better” person. The corollary is that it’s worth understanding what roles beauty actually plays in human life, both cultural and psychological. What emotions does a beautiful face trigger in others? What assumptions are made? What does beauty actually do? While beauty may be objective, our reactions to it may still be learned.

The situation is somewhat analogous to the violent impulses that everyone is capable of: rather than pretending that anger doesn’t exist, we learn to deal with it in some healthy way. Similarly, it seems to me to be far healthier in the long run to acknowledge that beauty, at least facial beauty, is real and universal. To the best of our knowledge so far, this also has the advantage of being true.

The experiments were done ages ago, and the research is still going, continuing to tease apart actual cause and psychological effect. We know now that what people tell us about their own mental processes is quite thoroughly inaccurate. We all believe that we have this magic thing called “introspection” that lets us see what is going on in our own minds, but in reality we don’t. It’s a fictional superpower.

The research on this point is really quite good. It’s not even a new finding, having been understood for at least the last fifty years. And yet this simple but important fact has never quite managed to make it into popular culture.

Perhaps no one wants to believe it.

For example, the simple act of writing an essay expounding against what you actually believe changes your mind somewhat; if you’re anti-abortion and you have to write an essay arguing for reproductive rights, you’ll be somewhat more pro-choice at the end of it (and vice versa.) It seems that merely making the right mental connections changes your beliefs. This is surprising, but the truly weird part is this: if you ask people if they’ve changed their opinion, they’ll tell you no. Even weirder, if you ask them a week later what their original belief was, they’ll tell you they’ve always been pro-choice. (This paper is the classic study.) The effect is repeatable with those who are convinced by verbal arguments, who, sure enough, will swear to experimenters that they have always held their new position.

And yet we think we know what we’re thinking.

It goes on. Most of us have heard of the halo effect where we unjustifiably infer one positive trait (such as intelligence) from another (such as attractiveness), but how many of us are conscious of doing it — and will admit to that fact? If you give insomniacs a sugar pill and tell them it will increase their heart rate and anxiety, they actually get to sleep earlier, because they falsely attribute their spinning minds to the pill and not to their insomnia. Even such basic perceptions as taste are not immune, as recent experiments show: given identical food, people (Americans anyway) will tell you that the meals labeled “full-fat” are tastier than their “low-fat” counterparts, and give adamant nonsense reasons for their preference.

It gets absurd. If you ask people to choose their favorite scent from four identical-smelling objects, they’ll mostly choose the rightmost one. No one believes this; suggesting that position was a factor produces the most thoroughly alarming looks…

When asked what goes on in our own minds, we mostly make it up. We find plausible explanations, and mistake them for an interior causality that, evidence shows, we can’t actually perceive.

The classic in this field is the 29-page review paper by Nisbett and Wilson, Telling More Than We Can Know: Verbal Reports on Mental Processes, which combs the previous 50 years of psychological research and discusses many of these examples. The authors write,

When subjects were asked about their cognitive processes, therefore, they did something that may have felt like introspection but which in fact may have been only a simple judgement of the extent to which input was a representative or plausible cause of output. It seems likely, in fact, that the subjects in the present studies, and ordinary people in their daily lives, do not even attempt to interrogate their memories about their cognitive processes when they are asked questions about them.

An important question remains. If we’re mostly telling stories about the workings of our own minds, then why do we believe them? There are many possibilities. Perhaps because we do have access to an enormous amount of introspective state — memories, emotions, sensations, goals — we mistakenly believe that we know about all of our minds, when in reality our conscious access is partial and fragmented. Perhaps we experience snippets of our reasoning processes, the fleeting words and images of deep thought, and mistake these symptoms for deeper causes. But I perfer Nisbett and Wilson’s closing comment:

It is naturally preferable, from the standpoint of prediction and subjective feelings of control, to believe that we have such access. It is frightening to believe that one has no more certain knowledge of the workings of one’s own mind than would an outsider with intimate knowledge of one’s history and the stimuli present at the time.

No one said the truth would be easy.

At the present time there are basically two technologies that can give us some idea of the activity of a working brain: positron-emission topography (PET) and functional magnetic-resonance imaging (fMRI). They both have important limitations in terms of resolution, what they actually measure, and many other things besides, but they’re also pretty amazing technologies. They produce detailed 3D maps of the “activity” of a whole brain, which are often represented like this:

This is a 2D cross section of a living brain, where more “active” regions relative to a baseline scan are represented in red, while less “active” regions are blue. For fMRI, “active” means greater oxygen consumption, which is a measure of… well, neurons doing something. We don’t really know precisely what oxygen consumption means, and this is part of the problem with fMRI. Resolution is also a serious issue. A single fMRI pixel is represents a cube of tissue a few millimeters across, containing perhaps five million neurons, and the maximum imaging rate of one frame every few seconds is far too slow to investigate anything that happens quickly. There is a recent paper in the journal Nature which discusses all of this in painful depth, but one might imaginatively compare an fMRI image to an aerial photograph of a city: you can see some broad patterns, but basically no details. Actually, it’s worse that that: you may be able to make a traffic map, but you have no idea why the people are driving. Even so, it’s an extremely interesting technology, because it allows the first ever 3D “movies” of what parts of the brain might be involved in whatever that brain is doing at the time.

Actually, it’s a little too sexy. In the paper Seeing is believing: The effect of brain images on judgments of scientific reasoning, David P. McCabe and Alan D. Castel describe an experiment performed on 156 undergraduates at Colorado State University (everyone tests their theory first on the undergraduates; psychologically speaking, we now know a truly shocking amount about them.) First they wrote three bogus scientific articles, of the kind you might find in an online news report. These were titled “Meditation Enhances Creative Thought”, “Playing Video Games Benefits Attention”, and “Watching TV is Related to Math Ability”. Each claimed that neural imaging data showed a link between two different activities.

How can this be argued from fMRI results? The general form goes something like this:

1. Put volunteers in an fMRI machine and have them do some activity, like meditating.
2. Scan them and watch what regions of the brain show increased blood flow when they start meditating. Neural anatomy is complex, so there’s almost always a cool-sounding name for the region in question, such as the “right temporal lobe.”
3. Take some other people and put them in the fMRI machine and have them do something else, like solving logic puzzles or composing a sonnet.
4. See which bits of the brain seem to be using more oxygen this time around.
5. If some of the same bits of the brain are involved in both activities, suggest that there is some relationship between them.

The critical bit of reasoning of reasoning is step 5. Sure, meditation and creative thought may both use e.g. the right temporal lobe, but what have we actually learned in terms of cause and effect? We don’t get to decide upon the casual relationships of the universe; they’re already there, and the job of a scientist — or any sort of truth-seeker, really — is to try to figure out what reality has already laid down. (I’m going to ignore the solipsistic school of thought that says we create reality with our minds. I mean, I create reality with my mind too, but somewhere in between I use my hands, and those are physical objects.)

The (better) neuroscientists are aware of this, and in the (better) literature, you will only find statements of the sort “the right temporal lobe is associated with creative activity.” That’s it. Associated. If mediation is also “associated” with the right temporal lobe, does this mean meditation is “associated” with creative activity in the brain? Well, no. That’s like saying that cleaning your windshield and filling a test-tube are similar activities because both involve something made of glass.

And yet, these types of arguments are often convincing, when it comes to the study of the mind. Accordingly, the reasoning in all three of the bogus articles used in McCabe and Castel’s research was in fact wrong. Nonetheless, they asked each of their 156 subjects to read the articles and rate how much they agreed with the statement “the scientific reasoning in the article made sense.” They used a four four-point scale: “strongly disagree”, “disagree”, “agree”, and “strongly agree”, which gives numbers from 1-4.

They found that those who read articles with brain images gave an average rating of 2.90, approximately “agree,” and those who read articles without gave an average rating of 2.73, somewhat less. This was statistically significant to p<0.05, meaning that there was thought to be a 95% chance that this wasn’t simply a random fluke of measurement, like throwing down ten dice and having them all come up six. They also got all their control group and randomization procedures right. In a later article I’ll explain exactly what p values mean and why one has controls and randomizations at all, but for the moment the point is that, unlike the faulty reasoning far too common in discussions of fMRI results, this particular piece of psychology research might actually have teased apart a real casual relationship that exists inside people’s minds: adding an image of a brain scan to an article makes otherwise intelligent people far more likely to accept its fallacious reasoning.

Here’s a graph to convince you:

Why are people so easily mislead? Figuring out what is true turns out to be sort of difficult, of course, but there’s more too this whole question, reasons not simply academic. In a similar piece of research which tested the effect of neuroscience language on perceived credibility, Weisberg and colleagues write

Research on nonneural cognitive psychology does not seem to pique the public’s
interest in the same way [as nueroscience research], even though the two fields are concerned with similar questions.
…

Because articles in both the popular press and scientific journals often focus on how neuroscientific findings can help to explain human behavior, people’s fascination with cognitive neuroscience can be redescribed as people’s fascination with explanations involving a neuropsychological component.

In other words ultimately we all just want that feeling of understanding why. But feelings are not the same thing as reality, and that’s an important lesson about the mind too.

It is a difficult and ancient fact that we as conscious beings don’t live in the real world. There are boundaries to what we know and what we can know. I am right now sitting on a couch in my house in Oakland, California. Across the ocean, there is a woman sitting on the floor of her Tokyo apartment. I have never met her, but she is just as much a part of the world as I am. Not my world though. There seem to be boundaries to the things I perceive. Figuring out those boundaries and how things get into and out of them is the process of figuring out me, and everyone else too.

And inside those limits, what is this phantom world I live in? How did I come to believe the things that I believe, and how do others believe other things? Why do I support reproductive rights, while some are staunchly anti-abortion? How come everyone in Morocco wanted me to believe in Allah? What makes me happy, and what makes other happy? Life is full of choices, and I don’t really know how anyone makes them. Not only is understanding the mind linked with the process of understanding what is real, it might just tell us some useful things about the human race.

In a sweating hotel room in Bamako my friend Matthew once made an extraordinary remark: “I don’t know what human nature is,” he said, “and neither do you.” We were talking about tribal rivalries. I had said it was evolutionary, something deep in our species. He retorted, correctly, that no one really knows. These myths we create about ourselves: they can hold us back. They’re dangerous. Better to figure out the truth, if the truth can be known.

And so we come to something that was traditionally the domain of religion, but then, all things were traditionally the domain of religion, because religion was once the organizing principle of entire societies (societies very much simpler than our modern chaoses.) I don’t buy the supernatural explanations anymore. I find them lazy. Are we really unknowable spirits, or can we just look into brains and behaviour to see if we can see what is actually going on? I may not necessarily be a materialist, but I am definitely a naturalist. I believe in looking.

So what’s left? Who purports to be studying the mind? Basically we have psychology, neuroscience, and cognitive linguistics. Those are the direct approaches, anyway. Sociology models the group, ethnology and anthropology look at human cultures in general, and economics and politics both postulate a psychology, but the three subjects I’ve mentioned are pretty much the main scientific disciplines claiming to say something about internal mental processes. (Computer Science sometimes thinks it has something to say, but I’m with Dijkstra in noting that the question of whether computers can think is much like the equally pressing question of whether submarines can swim.)

Each field starts in a different place, and their models don’t always connect. Psychology divides us into things like attention, cognition, emotion, decision-making, and memory and tries to build models of how these things interact inside of us. Cognitive Linguistics says that the way we use language and the way we think are somehow related. Neuroscience starts from the ground up, a little like understanding a machine by beginning with the study of its gears. All of them are suspect; all of them face different methodological and philosophical issues.

The truth seems to be that no one really knows what the right way is to study the mind. That’s why it’s an interesting problem, and that’s why it’s an area of human study so prone to bullshit. In subsequent articles, I am going to try to describe the basic approach of each of these fields, and what kinds of things can and cannot be learned with each methodology. Along the way, maybe we’ll learn something about what is actually known. We do know some things, it turns out, and some of them are alarmingly cool.