As you can see, there is an extremely high correlation between brain size and IQ in children aged 6-15. Basically, the larger your brain, the higher your IQ. Researchers imaged the brains of 164 children aged 6-15 and attempted to estimate their IQ’s based on brain size. The estimates correlated very highly with the true IQ’s of the subjects. The correlations were .718 using one method and and .684 using another method. That is an average correlation of ~.7 combining both measures. That is an fantastic correlation in the social sciences, where good correlations are hard to come by.
We have folks come to this site all the time saying that IQ tests:
- Are useless
- Measure test-taking ability only
- Are culturally biased in favor of Western Whites and hence worthless when given non-Western non-Whites.
- Don’t even measure intelligence at all
- When used to discuss racial differences are examples of “pseudo-science” or “scientific racism.”
Now that we have an excellent measure showing that the larger your brain, the higher your IQ, and the smaller your brain, the lower your IQ, all of these rejoinders are looking like they are unsupported by science. In other words, most of the critiques against IQ are false if not nonsensical and at worst they are out and out lies.
MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning
Liye Wang, PLOS One, March 30, 2015
Abstract
In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject’s IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQ’s and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.
