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IQ testing, challenges to dual-processing, and the cost scientific publishing

Teaching should be full of ideas instead of stuffed with facts.


Galeph, J. (2018). Stuart Ritchie on “Conceptual objections to IQ testing”. Rationally speaking.

I used to think that IQ tests were only good at measuring performance on IQ tests but over the past few years my thinking around IQ testing has evolved. In this conversation between Julie Galeph of the Rationally speaking podcast and Prof. Stuart Ritchie they respond to some of the most common objections to IQ testing. Ritchie has also written a book called, somewhat uncontroversially, Intelligence: All that matters, which makes his position on the matter fairly clear.

I think it comes down to whether or not you believe that the g factor is what it’s supposed to be; a construct that summarises the positive correlations between performance on a variety of cognitive tasks. This means that good performance on one type of cognitive task is usually predictive of performance on other cognitive tasks. For some people, the existence of the g factor, along with it’s description of general intelligence, is fairly uncontroversial. For others, it’s anathema to even mention the concept of general intelligence.

To make my own bias clear, I’m inclined to believe the following statements about general intelligence:

  • A significant proportion of intelligence is heritable (close to 50%).
  • The g factor is an appropriate short hand for “general intelligence”.
  • The g factor can predict performance across a variety of cognitive tasks.
  • Scores on IQ tests are appropriate indicators of general intelligence.
  • Scores on IQ tests are positively correlated with successful outcomes in other areas that we care about e.g. academic achievement, employment, income, and health.

Having said that, I can’t go as far as Ritchie in the claim that intelligence is all that matters.


Melnikoff, D. E., & Bargh, J. A. (2018a). The Mythical Number Two. Trends in Cognitive Sciences, 22(4), 280–293.

First, we reveal that the dual-process typology, to the best of our knowledge, has never been tested, and may not be testable. Then, we offer an account of how the typology rose to prominence in the absence of direct empirical support. Finally, we review the many findings that oppose the dual-process typology, and conclude that we as a field should no longer assume that mental process can be partitioned into the categories Type 1 and Type 2.

Like everyone else I know, I had no problem with the idea that we have two systems for making clinical decisions; a slower, inefficient and conscious process (System 1), and a quicker, efficient and unconscious one (System 2). System 2 thinking is encouraged when we want to avoid errors of reasoning and cognitive biases (even though we also know that “thinking” about cognitive bias does little to avoid the mistakes we make because of them).

While I knew that the article posed a challenge to this concept I certainly wasn’t expecting to read that “…the ‘two types’ framework lacks empirical support, contradicts well-established findings, and is internally incoherent.” That was unexpected. And this is why I think that it’s a good idea to read this paper. You may end up disagreeing with the argument and hold fast to the dual-process explanation of clinical reasoning. But I think it’s almost always useful to challenge the ideas we cherish the most.

See also:

  • Melnikoff, D. E., & Bargh, J. A. (2018b). The Insidious Number Two. Trends in Cognitive Sciences, 22(8), 668–669.
  • Pennycook, G., Neys, W. D., Evans, J. St. B. T., Stanovich, K. E., & Thompson, V. A. (2018). The Mythical Dual-Process Typology. Trends in Cognitive Sciences, 22(8), 667–668.


Monbiot, G. (2018). Scientific publishing is a rip-off. We fund the research – it should be free. The Guardian.

The model was pioneered by the notorious conman Robert Maxwell. He realised that, because scientists need to be informed about all significant developments in their field, every journal that publishes academic papers can establish a monopoly and charge outrageous fees for the transmission of knowledge. He called his discovery “a perpetual financing machine”.

The more I think about it the more I’m baffled by the idea that we (academics) do the research, then we write the articles, we review the articles, we serve on editorial and review boards, and then we pay publishers to get access to the final product. Can you imagine if anyone tried to start a business like that today? I know that it’s complicated and that we can’t expect everyone to simply stop submitting to the “top journals” in their fields, but this really is something that we need to work on in the long term. I’m not saying that SciHub is the solution; but it’s an interesting option in the meantime.

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