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This is The Science of Winning at Life, Part 1: Scientific Self-Help: The State of Our Knowledge, published by lukeprog.

Part of the sequence: The Science of Winning at Life

Some have suggested that the Less Wrong community could improve readers' instrumental rationality more effectively if it first caught up with the scientific literature on productivity and self-help, and then enabled readers to deliberately practice self-help skills and apply what they've learned in real life.

I think that's a good idea. My contribution today is a quick overview of scientific self-help: what professionals call "the psychology of adjustment." First I'll review the state of the industry and the scientific literature, and then I'll briefly summarize the scientific data available on three topics in self-help: study methods, productivity, and happiness.

The industry and the literature

As you probably know, much of the self-help industry is a sham, ripe for parody. Most self-help books are written to sell, not to help. Pop psychology may be more myth than fact. As Christopher Buckley (1999) writes, "The more people read [self-help books], the more they think they need them... [it's] more like an addiction than an alliance."

Where can you turn for reliable, empirically-based self-help advice? A few leading therapeutic psychologists (e.g., Albert Ellis, Arnold Lazarus, Martin Seligman) have written self-help books based on decades of research, but even these works tend to give recommendations that are still debated, because they aren't yet part of settled science.

Lifelong self-help researcher Clayton Tucker-Ladd wrote and updated Psychological Self-Help (pdf) over several decades. It's a summary of what scientists do and don't know about self-help methods (as of about 2003), but it's also more than 2,000 pages long, and much of it surveys scientific opinion rather than experimental results, because on many subjects there aren't any experimental results yet. The book is associated with an internet community of people sharing what does and doesn't work for them.

More immediately useful is Richard Wiseman's 59 Seconds. Wiseman is an experimental psychologist and paranormal investigator who gathered together what little self-help research is part of settled science, and put it into a short, fun, and useful Malcolm Gladwell-ish book. The next best popular-level general self-help book is perhaps Martin Seligman's What You Can Change and What You Can't.

Two large books rate hundreds of popular self-help books according to what professional psychologists think of them, and offer advice on how to choose self-help books. Unfortunately, this may not mean much because even professional psychologists very often have opinions that depart from the empirical data, as documented extensively by Scott Lilienfeld and others in Science and Pseudoscience in Clinical Psychology and Navigating the Mindfield. These two books are helpful in assessing what is and isn't known according to empirical research (rather than according to expert opinion). Lilienfeld also edits the useful journal Scientific Review of Mental Health Practice, and has compiled a list of harmful psychological treatments. Also see Nathan and Gorman's A Guide to Treatments That Work, Roth & Fonagy's What Works for Whom?, and, more generally, Stanovich's How to Think Straight about Psychology.

Many self-help books are written as "one size fits all," but of course this is rarely appropriate in psychology, and this leads to reader disappointment (Norem & Chang, 2000). But psychologists have tested the effectiveness of reading particular problem-focused self-help books ("bibliotherapy").1 For example, it appears that reading David Burns' Feeling Good can be as effective for treating depression as individual or group therapy. Results vary from book to book.

...

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This is Decision Theory: Newcomb's Problem, Part 4: Decision theory: Why Pearl helps reduce “could” and “would”, but still leaves us with at least three alternatives, published by AnnaSalamon.

(This is the third post in a planned sequence.)

My last post left us with the questions:

Just what are humans, and other common CSAs, calculating when we imagine what “would” happen “if” we took actions we won’t take?

Is there more than one natural way to calculate these counterfactual “would”s? If so, what are the alternatives, and which alternative works best?

Today, I’ll take an initial swing at these questions. I’ll review Judea Pearl’s causal Bayes nets; show how Bayes nets offer a general methodology for computing counterfactual “would”s; and note three plausible alternatives for how to use Pearl’s Bayes nets to set up a CSA. One of these alternatives will be the “timeless” counterfactuals of Eliezer’s Timeless Decision Theory.

The problem of counterfactuals is the problem what we do and should mean when we we discuss what “would” have happened, “if” something impossible had happened. In its general form, this problem has proved to be quite gnarly. It has been bothering philosophers of science for at least 57 years, since the publication of Nelson Goodman’s book “Fact, Fiction, and Forecast” in 1952:

Let us confine ourselves for the moment to [counterfactual conditionals] in which the antecedent and consequent are inalterably false--as, for example, when I say of a piece of butter that was eaten yesterday, and that had never been heated,

`If that piece of butter had been heated to 150°F, it would have melted.'

Considered as truth-functional compounds, all counterfactuals are of course true, since their antecedents are false. Hence

`If that piece of butter had been heated to 150°F, it would not have melted.'

would also hold. Obviously something different is intended, and the problem is to define the circumstances under which a given counterfactual holds while the opposing conditional with the contradictory consequent fails to hold.

Recall that we seem to need counterfactuals in order to build agents that do useful decision theory -- we need to build agents that can think about the consequences of each of their “possible actions”, and can choose the action with best expected-consequences. So we need to know how to compute those counterfactuals. As Goodman puts it, “[t]he analysis of counterfactual conditionals is no fussy little grammatical exercise.”

Judea Pearl’s Bayes nets offer a method for computing counterfactuals. As noted, it is hard to reduce human counterfactuals in general: it is hard to build an algorithm that explains what (humans will say) really “would” have happened, “if” an impossible event had occurred. But it is easier to construct specific formalisms within which counterfactuals have well-specified meanings. Judea Pearl’s causal Bayes nets offer perhaps the best such formalism.

Pearl’s idea is to model the world as based on some set of causal variables, which may be observed or unobserved. In Pearl’s model, each variable is determined by a conditional probability distribution on the state of its parents (or by a simple probability distribution, if it has no parents). For example, in the following Bayes net, the beach’s probability of being “Sunny” depends only on the “Season”, and the probability that there is each particular “Number of beach-goers” depends only on the “Day of the week” and on the “Sunniness”. Since the “Season” and the “Day of the week” have no parents, they simply have fixed probability distributions.

Once we have a Bayes net set up to model a given domain, computing counterfactuals is easy. We just:

Take the usual conditional and unconditional probability distributions, that come with the Bayes net;

Do “surgery” on the Bayes net to plug in the...

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This is Decision Theory: Newcomb's Problem, Part 3: Decision theory: Why we need to reduce “could”, “would”, “should”, published by AnnaSalamon.

(This is the second post in a planned sequence.)

Let’s say you’re building an artificial intelligence named Bob. You’d like Bob to sally forth and win many utilons on your behalf. How should you build him? More specifically, should you build Bob to have a world-model in which there are many different actions he “could” take, each of which “would” give him particular expected results? (Note that e.g. evolution, rivers, and thermostats do not have explicit “could”/“would”/“should” models in this sense -- and while evolution, rivers, and thermostats are all varying degrees of stupid, they all still accomplish specific sorts of world-changes. One might imagine more powerful agents that also simply take useful actions, without claimed “could”s and “woulds”.)

My aim in this post is simply to draw attention to “could”, “would”, and “should”, as concepts folk intuition fails to understand, but that seem nevertheless to do something important for real-world agents. If we want to build Bob, we may well need to figure out what the concepts “could” and “would” can do for him.

Introducing Could/Would/Should agents:

Let a Could/Would/Should Algorithm, or CSA for short, be any algorithm that chooses its actions by considering a list of alternatives, estimating the payoff it “would” get “if” it took each given action, and choosing the action from which it expects highest payoff.

That is: let us say that to specify a CSA, we need to specify:

A list of alternatives a_1, a_2, ..., a_n that are primitively labeled as actions it “could” take;

For each alternative a_1 through a_n, an expected payoff U(a_i) that is labeled as what “would” happen if the CSA takes that alternative.

To be a CSA, the algorithm must then search through the payoffs for each action, and must then trigger the agent to actually take the action a_i for which its labeled U(a_i) is maximal.

Note that we can, by this definition of “CSA”, create a CSA around any made-up list of “alternative actions” and of corresponding “expected payoffs”.

The puzzle is that CSAs are common enough to suggest that they’re useful -- but it isn’t clear why CSAs are useful, or quite what kinds of CSAs are what kind of useful. To spell out the puzzle:

Puzzle piece 1: CSAs are common. Humans, some (though far from all) other animals, and many human-created decision-making programs (game-playing programs, scheduling software, etc.), have CSA-like structure. That is, we consider “alternatives” and act out the alternative from which we “expect” the highest payoff (at least to a first approximation). The ubiquity of approximate CSAs suggests that CSAs are in some sense useful.

Puzzle piece 2: The naïve realist model of CSAs’ nature and usefulness doesn’t work as an explanation.

That is: many people find CSAs’ usefulness unsurprising, because they imagine a Physically Irreducible Choice Point, where an agent faces Real Options; by thinking hard, and choosing the Option that looks best, naïve realists figure that you can get the best-looking option (instead of one of those other options, that you Really Could have gotten).

But CSAs, like other agents, are deterministic physical systems. Each CSA executes a single sequence of physical movements, some of which we consider “examining alternatives”, and some of which we consider “taking an action”. It isn’t clear why or in what sense such systems do better than deterministic systems built in some other way.

Puzzle piece 3: Real CSAs are presumably not built from arbitrarily labeled “coulds” and “woulds” -- presumably, the “woulds” that humans and others use, when considering e.g. which chess move to make, have useful properties. But it isn’t clear what those properties...

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This is Decision Theory: Newcomb's Problem, Part 2: Confusion about Newcomb is confusion about counterfactuals, published by AnnaSalamon.

(This is the first, and most newcomer-accessible, post in a planned sequence.)

Newcomb's Problem:

Joe walks out onto the square. As he walks, a majestic being flies by Joe's head with a box labeled "brain scanner", drops two boxes on the ground, and departs the scene. A passerby, known to be trustworthy, comes over and explains...

If Joe aims to get the most money, should Joe take one box or two?

What are we asking when we ask what Joe "should" do? It is common to cash out "should" claims as counterfactuals: "If Joe were to one-box, he would make more money". This method of translating "should" questions does seem to capture something of what we mean: we do seem to be asking how much money Joe can expect to make "if he one-boxes" vs. "if he two-boxes". The trouble with this translation, however, is that it is not clear what world "if Joe were to one-box" should refer to -- and, therefore, it is not clear how much money we should say Joe would make, "if he were to one-box". After all, Joe is a deterministic physical system; his current state (together with the state of his future self's past light-cone) fully determines what Joe's future action will be. There is no Physically Irreducible Moment of Choice, where this same Joe, with his own exact actual past, "can" go one way or the other.

To restate the situation more clearly: let us suppose that this Joe, standing here, is poised to two-box. In order to determine how much money Joe "would have made if he had one-boxed", let us say that we imagine reaching in, with a magical sort of world-surgery, and altering the world so that Joe one-boxes instead. We then watch to see how much money Joe receives, in this surgically altered world.

The question before us, then, is what sort of magical world-surgery to execute, before we watch to see how much money Joe "would have made if he had one-boxed". And the difficulty in Newcomb’s problem is that there is not one but two obvious world-surgeries to consider. First, we might surgically reach in, after Omega's departure, and alter Joe's box-taking only -- leaving Omega's prediction about Joe untouched. Under this sort of world-surgery, Joe will do better by two-boxing:

Expected value ( Joe's earnings if he two-boxes | some unchanged probability distribution on Omega's prediction ) >

Expected value ( Joe's earnings if he one-boxes | the same unchanged probability distribution on Omega's prediction ).

Second, we might surgically reach in, after Omega's departure, and simultaneously alter both Joe's box-taking and Omega's prediction concerning Joe's box-taking. (Equivalently, we might reach in before Omega's departure, and surgically alter the insides of Joe brain -- and, thereby, alter both Joe's behavior and Omega's prediction of Joe's behavior.) Under this sort of world-surgery, Joe will do better by one-boxing:

Expected value ( Joe's earnings if he one-boxes | Omega predicts Joe accurately) >

Expected value ( Joe's earnings if he two-boxes | Omega predicts Joe accurately).

The point: Newcomb's problem -- the problem of what Joe "should" do, to earn most money -- is the problem which type of world-surgery best cashes out the question "Should Joe take one box or two?". Disagreement about Newcomb's problem is disagreement about what sort of world-surgery we should consider, when we try to figure out what action Joe should take.

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This is Decision Theory: Newcomb's Problem, Part 1: Decision theory: An outline of some upcoming posts, published by AnnaSalamon.

Last August or so, Eliezer asked Steve Rayhawk and myself to attempt to solve Newcomb’s problem together. This project served a couple of purposes:

a. Get an indication as to our FAI research abilities.

b. Train our reduction-muscles.

c. Check whether Eliezer’s (unseen by us) timeless decision theory is a point that outside folks tend to arrive at independently (at least if starting from the rather substantial clues on OB/LW), and whether anything interestingly new came out of an independent attempt.

Steve and I (and, briefly but helpfully, Liron Shapira) took our swing at Newcomb. We wrote a great mass of notes that have been sitting on our hard drives, but hadn’t stitched them together into a single document. I’d like to attempt a Less Wrong sequence on that subject now. Most of this content is stuff that Eliezer, Nesov, and/or Dai developed independently and have been referring to in their posts, but I’ll try to present it more fully and clearly. I learned a bit of this from Eliezer/Nesov/Dai’s recent posts.

Here’s the outline, to be followed up with slower, clearer blog posts if all goes well:

0. Prelude: “Should” depends on counterfactuals. Newcomb's problem -- the problem of what Joe "should" do, to earn most money -- is the problem of which type of counterfactuals best cash out the question "Should Joe take one box or two?". Disagreement about Newcomb's problem is disagreement about what sort of counterfactuals we should consider, when we try to figure out what action Joe should take.

1. My goal in this sequence is to reduce “should” as thoroughly as I can. More specifically, I’ll make an (incomplete, but still useful) attempt to:

Make it even more clear that our naive conceptions of “could” and “should” are conceptual inventions, and are not Physically Irreducible Existent Things. (Written here.)

Consider why one might design an agent that uses concepts like “could” and “should” (hereafter a “Could/Should Agent”, or “CSA”), rather than designing an agent that acts in some other way. Consider what specific concepts of “could” and “should” are what specific kinds of useful. (This is meant as a more thorough investigation of the issues treated by Eliezer in “Possibility and Couldness”.)

Consider why evolution ended up creating us as approximate CSAs. Consider what kinds of CSAs are likely to be how common across the multiverse.

2. A non-vicious regress. Suppose we’re designing Joe, and we want to maximize his expected winnings. What notion of “should” should we design Joe to use? There’s a regress here, in that creator-agents with different starting decision theories will design agents that have different starting decision theories. But it is a non-vicious regress. We can gain understanding by making this regress explicit, and asking under what circumstances agents with decision theory X will design future agents with decision theory Y, for different values of X and Y.

3a. When will a CDT-er build agents that use “could” and “should”? Suppose again that you’re designing Joe, and that Joe will go out in a world and win utilons on your behalf. What kind of Joe-design will maximize your expected utilons?

If we assume nothing about Joe’s world, we might find that your best option was to design Joe to act as a bundle of wires which happens to have advantageous physical effects, and which doesn’t act like an agent at all.

But suppose Joe’s world has the following handy property: suppose Joe’s actions have effects, and Joe’s “policy”, or the actions he “would have taken” in response to alternative inputs also have effects, but the details of Joe’s internal wiring doesn’t otherwise matter. (I'll call this the "policy-equivalence assumption"). Since Jo...

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This is Rationality and Philosophy, Part 11: Train Philosophers with Pearl and Kahneman, not Plato and Kant, published by lukeprog.

Part of the sequence: Rationality and Philosophy

Hitherto the people attracted to philosophy have been mostly those who loved the big generalizations, which were all wrong, so that few people with exact minds have taken up the subject.

Bertrand Russell

I've complained before that philosophy is a diseased discipline which spends far too much of its time debating definitions, ignoring relevant scientific results, and endlessly re-interpreting old dead guys who didn't know the slightest bit of 20th century science. Is that still the case?

You bet. There's some good philosophy out there, but much of it is bad enough to make CMU philosopher Clark Glymour suggest that on tight university budgets, philosophy departments could be defunded unless their work is useful to (cited by) scientists and engineers — just as his own work on causal Bayes nets is now widely used in artificial intelligence and other fields.

How did philosophy get this way? Russell's hypothesis is not too shabby. Check the syllabi of the undergraduate "intro to philosophy" classes at the world's top 5 U.S. philosophy departments — NYU, Rutgers, Princeton, Michigan Ann Arbor, and Harvard — and you'll find that they spend a lot of time with (1) old dead guys who were wrong about almost everything because they knew nothing of modern logic, probability theory, or science, and with (2) 20th century philosophers who were way too enamored with cogsci-ignorant armchair philosophy. (I say more about the reasons for philosophy's degenerate state here.)

As the CEO of a philosophy/math/compsci research institute, I think many philosophical problems are important. But the field of philosophy doesn't seem to be very good at answering them. What can we do?

Why, come up with better philosophical methods, of course!

Scientific methods have improved over time, and so can philosophical methods. Here is the first of my recommendations...

More Pearl and Kahneman, less Plato and Kant

Philosophical training should begin with the latest and greatest formal methods ("Pearl" for the probabilistic graphical models made famous in Pearl 1988), and the latest and greatest science ("Kahneman" for the science of human reasoning reviewed in Kahneman 2011). Beginning with Plato and Kant (and company), as most universities do today, both (1) filters for inexact thinkers, as Russell suggested, and (2) teaches people to have too much respect for failed philosophical methods that are out of touch with 20th century breakthroughs in math and science.

So, I recommend we teach young philosophy students:

more Bayesian rationality, heuristics and biases, & debiasing, less informal "critical thinking skills";

more mathematical logic & theory of computation, less term logic;

more probability theory & Bayesian scientific method, less pre-1980 philosophy of science;

more psychology of concepts & machine learning, less conceptual analysis;

more formal epistemology & computational epistemology, less pre-1980 epistemology;

more physics & cosmology, less pre-1980 metaphysics;

more psychology of choice, less philosophy of free will;

more moral psychology, decision theory, and game theory, less intuitionist moral philosophy;

more cognitive psychology & cognitive neuroscience, less pre-1980 philosophy of mind;

more linguistics & psycholinguistics, less pre-1980 philosophy of language;

more neuroaesthetics, less aesthetics;

more causal models & psychology of causal perception, less pre-1980 theories of causation.

(In other words: train philosophy students like they do at CMU, but even "more so.")

So, my own "intro to philosophy" mega-course might be guided by the following core readings:

Stanovich, Rationality and the Reflective Mind (2010)...

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This is Rationality and Philosophy, Part 11: Philosophy Needs to Trust Your Rationality Even Though It Shouldn't, published by lukeprog.

Part of the sequence: Rationality and Philosophy

Philosophy is notable for the extent to which disagreements with respect to even those most basic questions persist among its most able practitioners, despite the fact that the arguments thought relevant to the disputed questions are typically well-known to all parties to the dispute.

Thomas Kelly

The goal of philosophy is to uncover certain truths... [But] philosophy continually leads experts with the highest degree of epistemic virtue, doing the very best they can, to accept a wide array of incompatible doctrines. Therefore, philosophy is an unreliable instrument for finding truth. A person who enters the field is highly unlikely to arrive at true answers to philosophical questions.

Jason Brennan

After millennia of debate, philosophers remain heavily divided on many core issues. According to the largest-ever survey of philosophers, they're split 25-24-18 on deontology / consequentialism / virtue ethics, 35-27 on empiricism vs. rationalism, and 57-27 on physicalism vs. non-physicalism.

Sometimes, they are even divided on psychological questions that psychologists have already answered: Philosophers are split evenly on the question of whether it's possible to make a moral judgment without being motivated to abide by that judgment, even though we already know that this is possible for some people with damage to their brain's reward system, for example many Parkinson's patients, and patients with damage to the ventromedial frontal cortex (Schroeder et al. 2012).1

Why are physicists, biologists, and psychologists more prone to reach consensus than philosophers?2 One standard story is that "the method of science is to amass such an enormous mountain of evidence that... scientists cannot ignore it." Hence, religionists might still argue that Earth is flat or that evolutionary theory and the Big Bang theory are "lies from the pit of hell," and philosophers might still be divided about whether somebody can make a moral judgment they aren't themselves motivated by, but scientists have reached consensus about such things.

In its dependence on masses of evidence and definitive experiments, science doesn't trust your rationality:

Science is built around the assumption that you're too stupid and self-deceiving to just use [probability theory]. After all, if it was that simple, we wouldn't need a social process of science... [Standard scientific method] doesn't trust your rationality, and it doesn't rely on your ability to use probability theory as the arbiter of truth. It wants you to set up a definitive experiment.

Sometimes, you can answer philosophical questions with mountains of evidence, as with the example of moral motivation given above. But or many philosophical problems, overwhelming evidence simply isn't available. Or maybe you can't afford to wait a decade for definitive experiments to be done. Thus, "if you would rather not waste ten years trying to prove the wrong theory," or if you'd like to get the right answer without overwhelming evidence, "you'll need to [tackle] the vastly more difficult problem: listening to evidence that doesn't shout in your ear."

This is why philosophers need rationality training even more desperately than scientists do. Philosophy asks you to get the right answer without evidence that shouts in your ear. The less evidence you have, or the harder it is to interpret, the more rationality you need to get the right answer. (As likelihood ratios get smaller, your priors need to be better and your updates more accurate.)

Because it tackles so many questions that can't be answered by masses of evidence or definitive experiments, philosophy needs to trust your rationality ...

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This is Rationality and Philosophy, Part 9: Intuitions Aren't Shared That Way, published by lukeprog.

Part of the sequence: Rationality and Philosophy

Consider these two versions of the famous trolley problem:

Stranger: A train, its brakes failed, is rushing toward five people. The only way to save the five people is to throw the switch sitting next to you, which will turn the train onto a side track, thereby preventing it from killing the five people. However, there is a stranger standing on the side track with his back turned, and if you proceed to thrown the switch, the five people will be saved, but the person on the side track will be killed.

Child: A train, its brakes failed, is rushing toward five people. The only way to save the five people is to throw the switch sitting next to you, which will turn the train onto a side track, thereby preventing it from killing the five people. However, there is a 12-year-old boy standing on the side track with his back turned, and if you proceed to throw the switch, the five people will be saved, but the boy on the side track will be killed.

Here it is: a standard-form philosophical thought experiment. In standard analytic philosophy, the next step is to engage in conceptual analysis — a process in which we use our intuitions as evidence for one theory over another. For example, if your intuitions say that it is "morally right" to throw the switch in both cases above, then these intuitions may be counted as evidence for consequentialism, for moral realism, for agent neutrality, and so on.

Alexander (2012) explains:

Philosophical intuitions play an important role in contemporary philosophy. Philosophical intuitions provide data to be explained by our philosophical theories [and] evidence that may be adduced in arguments for their truth... In this way, the role... of intuitional evidence in philosophy is similar to the role... of perceptual evidence in science...

Is knowledge simply justified true belief? Is a belief justified just in case it is caused by a reliable cognitive mechanism? Does a name refer to whatever object uniquely or best satisfies the description associated with it? Is a person morally responsible for an action only if she could have acted otherwise? Is an action morally right just in case it provides the greatest benefit for the greatest number of people all else being equal? When confronted with these kinds of questions, philosophers often appeal to philosophical intuitions about real or imagined cases...

...there is widespread agreement about the role that [intuitions] play in contemporary philosophical practice... We advance philosophical theories on the basis of their ability to explain our philosophical intuitions, and appeal to them as evidence that those theories are true...

In particular, notice that philosophers do not appeal to their intuitions as merely an exercise in autobiography. Philosophers are not merely trying to map the contours of their own idiosyncratic concepts. That could be interesting, but it wouldn't be worth decades of publicly-funded philosophical research. Instead, philosophers appeal to their intuitions as evidence for what is true in general about a concept, or true about the world.

In this sense,

We [philosophers] tend to believe that our philosophical intuitions are more or less universally shared... We... appeal to philosophical intuitions, when we do, because we anticipate that others share our intuitive judgments.

But anyone with more than a passing familiarity with cognitive science might have bet in advance that this basic underlying assumption of a core philosophical method is... incorrect.

For one thing, philosophical intuitions show gender diversity. Consider again the Stranger and Child versions of the Trolley problem. It turns out that men are less likely than women to thi...

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This is Rationality and Philosophy, Part 8: Living Metaphorically, published by lukeprog.

Part of the sequence: Rationality and Philosophy

In my last post, I showed that the brain does not encode concepts in terms of necessary and sufficient conditions. So, any philosophical practice which assumes this — as much of 20th century conceptual analysis seems to do — is misguided.

Next, I want to show that human abstract thought is pervaded by metaphor, and that this has implications for how we think about the nature of philosophical questions and philosophical answers. As Lakoff & Johnson (1999) write:

If we are going to ask philosophical questions, we have to remember that we are human... The fact that abstract thought is mostly metaphorical means that answers to philosophical questions have always been, and always will be, mostly metaphorical. In itself, that is neither good nor bad. It is simply a fact about the capacities of the human mind. But it has major consequences for every aspect of philosophy. Metaphorical thought is the principal tool that makes philosophical insight possible, and that constrains the forms that philosophy can take.

To understand how fundamental metaphor is to our thinking, we must remember that human cognition is embodied:

We have inherited from the Western philosophical tradition a theory of faculty psychology, in which we have a "faculty" of reason that is separate from and independent of what we do with our bodies. In particular, reason is seen as independent of perception and bodily movement...

The evidence from cognitive science shows that classical faculty psychology is wrong. There is no such fully autonomous faculty of reason separate from and independent of bodily capacities such as perception and movement. The evidence supports, instead, an evolutionary view, in which reason uses and grows out of such bodily capacities.

Consider, for example, the fact that as neural beings we must categorize things:

We are neural beings. Our brains each have 100 billion neurons and 100 trillion synaptic connections. It is common in the brain for information to be passed from one dense ensemble of neurons to another via a relatively sparse set of connections. Whenever this happens, the pattern of activation distributed over the first set of neurons is too great to be represented in a one-to-one manner in the sparse set of connections. Therefore, the sparse set of connections necessarily groups together certain input patterns in mapping them across to the output ensemble. Whenever a neural ensemble provides the same output with different inputs, there is neural categorization.

To take a concrete example, each human eye has 100 million light-sensing cells, but only about 1 million fibers leading to the brain. Each incoming image must therefore be reduced in complexity by a factor of 100. That is, information in each fiber constitutes a "categorization" of the information from about 100 cells.

Moreover, almost all our categorizations are determined by the unconscious associative mind — outside our control and even our awareness — as we interact with the world. As Lakoff & Johnson note, "Even when we think we are deliberately forming new categories, our unconscious categories enter into our choice of possible conscious categories."

And because our categories are shaped not by a transcendent, universal faculty of reason but by the components of our sensorimotor system that process our interaction with the world, our concepts and categories end up being largely sensorimotor concepts and categories.

Here are some examples of metaphorical thought shaped by the sensorimotor system:

Important Is Big

Example: "Tomorrow is a big day."

Mapping: From importance to size.

Experience: As a child, finding that big things (e.g. parents) are important and can exert major forces ...

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This is Rationality and Philosophy, Part 7: Concepts Don't Work That Way, published by lukeprog.

Part of the sequence: Rationality and Philosophy

Philosophy in the Flesh, by George Lakoff and Mark Johnson, opens with a bang:

The mind is inherently embodied. Thought is mostly unconscious. Abstract concepts are largely metaphorical.

These are three major findings of cognitive science. More than two millennia of a priori philosophical speculation about these aspects of reason are over. Because of these discoveries, philosophy can never be the same again.

When taken together and considered in detail, these three findings... are inconsistent with central parts of... analytic philosophy...

This book asks: What would happen if we started with these empirical discoveries about the nature of mind and constructed philosophy anew?

...A serious appreciation of cognitive science requires us to rethink philosophy from the beginning, in a way that would put it more in touch with the reality of how we think.

So what would happen if we dropped all philosophical methods that were developed when we had a Cartesian view of the mind and of reason, and instead invented philosophy anew given what we now know about the physical processes that produce human reasoning?

What emerges is a philosophy close to the bone. A philosophical perspective based on our empirical understanding of the embodiment of mind is a philosophy in the flesh, a philosophy that takes account of what we most basically are and can be.

Philosophy is a diseased discipline, but good philosophy can (and must) be done. I'd like to explore how one can do good philosophy, in part by taking cognitive science seriously.

Conceptual Analysis

Let me begin with a quick, easy example of how cognitive science can inform our philosophical methodology. The example below shouldn’t surprise anyone who has read A Human’s Guide to Words, but it does illustrate how misguided thousands of philosophical works can be due to an ignorance of cognitive science.

Consider what may be the central method of 20th century analytic philosophy: conceptual analysis. In its standard form, conceptual analysis assumes (Ramsey 1992) the “classical view” of concepts, that a “concept C has definitional structure in that it is composed of simpler concepts that express necessary and sufficient conditions for falling under C.” For example, the concept bachelor has the constituents unmarried and man. Something falls under the concept bachelor if and only if it is an unmarried man.

Conceptual analysis, then, is the attempt to examine our intuitive concepts and arrive at definitions (in terms of necessary and sufficient conditions) that capture the meaning of those concepts. De Paul & Ramsey (1999) explain:

Anyone familiar with Plato's dialogues knows how [conceptual analysis] is conducted. We see Socrates encounter someone who claims to have figured out the true essence of some abstract notion... the person puts forward a definition or analysis of the notion in the form of necessary and sufficient conditions that are thought to capture all and only instances of the concept in question. Socrates then refutes his interlocutor's definition of the concept by pointing out various counterexamples...

For example, in Book I of the Republic, when Cephalus defines justice in a way that requires the returning of property and total honesty, Socrates responds by pointing out that it would be unjust to return weapons to a person who had gone mad or to tell the whole truth to such a person.... [The] proposed analysis is rejected because it fails to capture our intuitive judgments about the nature of justice.

After a proposed analysis or definition is overturned by an intuitive counterexample, the idea is to revise or replace the analysis with one that is not subject to the counterexample. C...