The five “fallacies” they explain (really they mean biases rather than logical fallacies) are

1. Confirmation bias
2. Fundamental attribution error
3. Neglect of probability
4. The trust gap
5. Argumentative theory of reasoning

The more I learn about critical thinking, the more I realise “logical fallacy” is a useless concept, and the concept of “bias” is the one that does the work, but more about that on another day.

Lee Ross has featured in a couple of episodes and I hope he gets an episode to himself in future for his work on attribution theory and attitude polarisation.

In a study summarised by the Association for Psychological Science, parents were given information about the financial implications of having children. This was deliberately biased information, emphasising the enormous costs rather than future benefits such as having someone to make sure you’re okay in later life. Parents given this negative information increased their liking for their children, compared to a separate group who received positive information.

This is classic cognitive dissonance: if you are told that a decision has made you worse off, this conflicts with a self-image of being someone who is smart and in control. The mind will protect itself from the thought “I made a bad decision”, and so something has to give. In this case, the way out is to like children more; specifically, to more intensely value the emotional relationship with the child, so that the financial costs don’t seem to matter so much.

An implication of this is that people love their children much more in present society than in previous eras. The APS summary concludes “As the value of children has diminished, and the costs have escalated, the belief that parenthood is emotionally rewarding has gained currency. In that sense, the myth of parental joy is a modern psychological phenomenon.”

For more on the “Myth of parental joy”, I recommend Daniel Gilbert’s Stumbling on Happiness.

My answers on the spot didn’t capture the true reasons why I contribute. A main factor is that people regard what they find in Wikipedia as fact, so I’m horrified that so much of it is incomplete or misleading. I’ve also come round to the view that this is a project that is genuinely changing the world for the better.

The Jimmy Wales interview filmed on the same day is a truly excellent overview of current issues facing Wikipedia:

The essay is aimed at other editors. It’s “behind the scenes”, not part of the encyclopedia itself. So it can be more informal – dare I say humourous? – than the articles themselves. My hope is that it will act as a guideline for other editors and may encourage people who are just starting to contribute, or just thinking of contributing, to the psychology articles.

One commenter writes, “It’s very thorough and gives things from a UK perspective.” I can’t argue with that.

Though lots has been written about Bayes, I wanted to convey to a lay audience what he achieved and why it’s so important now. Here is an attempt at a set of “footnotes” for anyone who wants to follow up:

The historical background is explained in:

• John Earman, Bayes or Bust (MIT Press)
• Edwin T. Jaynes,  Probability Theory: the logic of science (Cambridge University Press) and his other writings.

The Jaynes book suggests some of the applications of Bayesianism that I mention, including radio astronomy. It was my own idea to include flirting as an example of a reverse inference problem: I’m sure people don’t usually think of it that way.

“Is Bayesianism just common sense…?” For examples of how medical or other decisions go wrong because people don’t think in a Bayesian way, an accessible introduction is Stuart Sutherland, Irrationality (Penguin paperback, reissued by Pinter & Martin).  Similar material is covered in Chapter 12 of Scott Plous’ The Psychology of Judgement and Decision Making (McGraw-Hill). These authors in turn, are digesting the work of Amos Tversky, Daniel Kahneman and colleagues.

A full-on technical overview of Bayes’ theorem, why it should be taken seriously as a standard of reasoning, and how human judgements violate it, can be found in Jonathan Baron’s textbook Thinking and Deciding (Cambridge University Press).

According to my memory, although I can’t find the reference, the “Bayesian argument against torture” was proposed by the mathematician Robert Matthews.

“The training had absolutely no effect”: This remark conflates a number of different studies; I had to because of the limitations I was speaking under. Two papers reprinted in the book Judgment under uncertainty: Heuristics and biases describe how attempts to train people to be more Bayesian often made no difference: Alpert & Raiffa’s “A progress report on the training of probability assessors” and Lichtenstein, Fischhoff & Phillips’ “Calibration of Probabilities: The State of the Art.”