drupal statistics module

Machines Like Us

Does psychology belong in the science club?

Monday, 16 September 2013
by Jon Brock

First, a disclaimer: I’m the proud holder of a Bachelor of Science (upper second class) in experimental psychology. So you shouldn’t be too surprised when I tell you psychology is a science.

But for many other people, particularly scientists from other disciplines, psychology is at best a “soft” science. It doesn’t belong in the same exalted company as physics, chemistry, or, dare I say, neuroscience.

The long-standing debate about psychology’s scientific credentials was reignited last year by microbiologist and founding editor of RealClearScience, Alex Berezow. In a provocative article entitled Keep psychology out of the science club, Berezow argued:

The dismissive attitude that scientists have toward psychologists […] is […] rooted in the failure of psychologists to acknowledge that they don’t have the same claim on secular truth that the hard sciences do.

Left unanswered was the question of how science arrives at this so-called “secular truth." More to the point: how is psychology any different when it comes to interrogating reality?

Models in mind

In the introductory chapter of his most recent book, The Magic of Reality, evolutionary biologist Richard Dawkins discusses how we know what is real.

Most obviously, we can use our senses. If we can see or hear or feel something, if we can touch it or taste it, then we know that it’s real.

We can also use machines to enhance and expand our senses. Bacteria are invisible to the naked eye but we can see them with a microscope. We can’t perceive radio waves, but we can have them translated into sounds we can hear.

But as Dawkins notes:

There is a less familiar way in which a scientist can work out what is real when our five senses cannot detect it directly. This is through a model of what might be going on, which can then be tested […]

We look carefully at the model and predict what we ought to see or hear, etc. if the model were correct. Then we look to see whether the predictions are right or wrong. If they are right, this increases our confidence that the model really does represent reality […]. If our predictions are wrong, we reject the model, or modify it and try again.

Dawkins illustrates this point with some famous examples of scientific modelling. Crick and Watson, for instance, didn’t see the double-helix shape of DNA: they built a model (literally, with cardboard) and saw that its predictions were consistent with observations made by another scientist, Rosalind Franklin.