Sex differences in brain size

Next time someone asks you “Are men and women’s brains different?”, you can answer, without hesitation, “Yes”. Not only do they tend to be found in different types of bodies, but they are different sizes. Men’s are typically larger by something like 130 cubic centimeters.

Not only are they actually larger, but they are larger even once you take into account body size (i.e. men’s brains are bigger even when accounting for the fact that heavier and/or taller people will tend to have bigger heads and brains, and than men tend to be heavier and taller than women). And this is despite the fact that there is no difference in size of brain at birth – the sex difference in brain volume development seems to begin around age two. (Side note: no difference in brain volume between male and female cats).

But is this difference in brain volume a lot? There’s substantial variation between individuals, as well as across the individuals of each sex. What does ~130cc mean in the context of this variation? One way of thinking about it is in terms of standardised effect size, which measures the size of a difference between the two population averages in standard units based on the variation within those populations.

Here’s a good example – we all know that men are taller than women. Not all men are taller than all women, but men tend to be taller. With the effect size, we can precisely express this vague idea of ‘tend to be’. The (Cohen’s d) effect size statistic of the height difference between men and women is ~1.72.

What this means is that the distribution of heights in the two populations can be visualised like this:

mf_heightsWith this spread of heights, the average man is taller than 95.7% of women.

Estimates of the effect size of total brain volume vary, but a reasonable value is about ~1.3, which looks like this:

mf_brainsThis means that the average man has a larger brain, by volume, than 90% of the female population.

For reference, psychology experiments typically look at phenomena with effet sizes of the order ~0.4 , which looks like this:

mf_0p4And which means that the average of group A exceeds 65.5% of group B.

In this context, human sexual dimorphism in brain volume is an extremely large effect.

So when they ask “Are men and women’s brains different?”, you can unhesitatingly say, “yes”. And when they ask “And what does that mean for differences in how they think” you can say “Ah, now that’s a different issue”.

Link: meta-analysis of male-female differences in brain structure:

Kristoffer Magnusson’s awesome interactive effect size visualisation

Previously: gendered brain blogging

Edit 8/2/17: Andy Fugard pointed out that there are many different measures of effect size, and I only discuss/use one: the Cohen’s d effect size. I’ve edited the text to make this clearer.

Edit 2 (8/2/17): Kevin Mitchell points out this paper that claims sex differences in brain size are already apparent in neonates

How to overcome bias

How do you persuade somebody of the facts? Asking them to be fair, impartial and unbiased is not enough. To explain why, psychologist Tom Stafford analyses a classic scientific study.

One of the tricks our mind plays is to highlight evidence which confirms what we already believe. If we hear gossip about a rival we tend to think “I knew he was a nasty piece of work”; if we hear the same about our best friend we’re more likely to say “that’s just a rumour”. If you don’t trust the government then a change of policy is evidence of their weakness; if you do trust them the same change of policy can be evidence of their inherent reasonableness.

Once you learn about this mental habit – called confirmation bias – you start seeing it everywhere.

This matters when we want to make better decisions. Confirmation bias is OK as long as we’re right, but all too often we’re wrong, and we only pay attention to the deciding evidence when it’s too late.

How we should to protect our decisions from confirmation bias depends on why, psychologically, confirmation bias happens. There are, broadly, two possible accounts and a classic experiment from researchers at Princeton University pits the two against each other, revealing in the process a method for overcoming bias.

The first theory of confirmation bias is the most common. It’s the one you can detect in expressions like “You just believe what you want to believe”, or “He would say that, wouldn’t he?” or when the someone is accused of seeing things a particular way because of who they are, what their job is or which friends they have. Let’s call this the motivational theory of confirmation bias. It has a clear prescription for correcting the bias: change people’s motivations and they’ll stop being biased.

The alternative theory of confirmation bias is more subtle. The bias doesn’t exist because we only believe what we want to believe, but instead because we fail to ask the correct questions about new information and our own beliefs. This is a less neat theory, because there could be one hundred reasons why we reason incorrectly – everything from limitations of memory to inherent faults of logic. One possibility is that we simply have a blindspot in our imagination for the ways the world could be different from how we first assume it is. Under this account the way to correct confirmation bias is to give people a strategy to adjust their thinking. We assume people are already motivated to find out the truth, they just need a better method. Let’s call this the cognition theory of confirmation bias.

Thirty years ago, Charles Lord and colleagues published a classic experiment which pitted these two methods against each other. Their study used a persuasion experiment which previously had shown a kind of confirmation bias they called ‘biased assimilation’. Here, participants were recruited who had strong pro- or anti-death penalty views and were presented with evidence that seemed to support the continuation or abolition of the death penalty. Obviously, depending on what you already believe, this evidence is either confirmatory or disconfirmatory. Their original finding showed that the nature of the evidence didn’t matter as much as what people started out believing. Confirmatory evidence strengthened people’s views, as you’d expect, but so did disconfirmatory evidence. That’s right, anti-death penalty people became more anti-death penalty when shown pro-death penalty evidence (and vice versa). A clear example of biased reasoning.

For their follow-up study, Lord and colleagues re-ran the biased assimilation experiment, but testing two types of instructions for assimilating evidence about the effectiveness of the death penalty as a deterrent for murder. The motivational instructions told participants to be “as objective and unbiased as possible”, to consider themselves “as a judge or juror asked to weigh all of the evidence in a fair and impartial manner”. The alternative, cognition-focused, instructions were silent on the desired outcome of the participants’ consideration, instead focusing only on the strategy to employ: “Ask yourself at each step whether you would have made the same high or low evaluations had exactly the same study produced results on the other side of the issue.” So, for example, if presented with a piece of research that suggested the death penalty lowered murder rates, the participants were asked to analyse the study’s methodology and imagine the results pointed the opposite way.

They called this the “consider the opposite” strategy, and the results were striking. Instructed to be fair and impartial, participants showed the exact same biases when weighing the evidence as in the original experiment. Pro-death penalty participants thought the evidence supported the death penalty. Anti-death penalty participants thought it supported abolition. Wanting to make unbiased decisions wasn’t enough. The “consider the opposite” participants, on the other hand, completely overcame the biased assimilation effect – they weren’t driven to rate the studies which agreed with their preconceptions as better than the ones that disagreed, and didn’t become more extreme in their views regardless of which evidence they read.

The finding is good news for our faith in human nature. It isn’t that we don’t want to discover the truth, at least in the microcosm of reasoning tested in the experiment. All people needed was a strategy which helped them overcome the natural human short-sightedness to alternatives.

The moral for making better decisions is clear: wanting to be fair and objective alone isn’t enough. What’s needed are practical methods for correcting our limited reasoning – and a major limitation is our imagination for how else things might be. If we’re lucky, someone else will point out these alternatives, but if we’re on our own we can still take advantage of crutches for the mind like the “consider the opposite” strategy.

This is my BBC Future column from last week. You can read the original here. My ebook For argument’s sake: Evidence that reason can change minds is out now.

Can boy monkeys throw?

180px-cebus_albifrons_editAimed throwing is a gendered activity – men are typically better at it than women (by about 1 standard deviation, some studies claim). Obviously this could be due to differential practice, which is in turn due to cultural bias in what men vs women are expected to be a good at and enjoy (some say “not so” to this practice-effect explanation).

Monkeys are interesting because they are close evolutionary relatives, but don’t have human gender expectations. So we note with interest this 2000 study which claims no difference in throwing accuracy between male and female Capuchin monkeys. In fact, the female monkeys were (non-significantly) more accurate than the males (perhaps due to throwing as part of Capuchin female sexual displays?).

Elsewhere, a review of cross-species gender differences in spatial ability finds “most of the hypotheses [that male mammals have better spatial ability than females] are either logically flawed or, as yet, have no substantial support. Few of the data exclusively support or exclude any current hypotheses“.

Chimps are closer relatives to humans than monkeys, but although there is a literature on gendered differences in object use/preference among chimps, I couldn’t immediately find anything on gendered differences in throwing among chimps. Possibly because few scientists want to get near a chimp when it is flinging sh*t around.

Cite: Westergaard, G. C., Liv, C., Haynie, M. K., & Suomi, S. J. (2000). A comparative study of aimed throwing by monkeys and humans. Neuropsychologia, 38(11), 1511-1517.

Previously: gendered brain blogging

Gender brain blogging

s-l300I’ve started teaching a graduate seminar on the cognitive neuroscience of sex-differences. The ambition is to carry out a collective close-reading of Cordelia Fine’s “Delusions of Gender: The Real Science Behind Sex Differences” (US: “How Our Minds, Society, and Neurosexism Create Difference“). Week by week the class is going to extract the arguments and check the references from each chapter of Fine’s book.

I mention this to explain why there is likely to be an increase in the number of gender-themed posts by me to

Here’s Fine summarising her argument in the introduction to the 2010 book:

There are sex differences in the brain. There are also large […] sex differences in who does what and who achieves what. It would make sense if these facts were connected in some way, and perhaps they are. But when we follow the trail of contemporary science we discover a surprising number of gaps, assumptions, inconsistencies, poor methodologies and leaps of faith.

This is a book about science works and how is made to work as much as it is a book about gender. It’s the Bad Science of  cognitive neuroscience.  Essential.

The troubled friendship of Tversky and Kahneman

Daniel Kahneman, by Pat Kinsella (detail)
Daniel Kahneman, by Pat Kinsella for the Chronicle Review (detail)

Writer Michael Lewis’s new book, “The Undoing Project: The Friendship That Changed Our Minds”, is about two of the most important figures in modern psychology, Amos Tvesky and Daniel Kahneman.

In this extract for the Chronicle of Higher Education, Lewis describes the emotional tension between the pair towards the end of their collaboration. It’s a compelling ‘behind the scenes’ view of the human side to the foundational work of the heuristics and biases programme in psychology, as well as being brilliantly illustrated by Pat Kinsella.

One detail that caught my eye is this response by Amos Tversky to a critique of the work he did with Kahneman. As well as being something I’ve wanted to write myself on occasion, it illustrates the forthrightness which made Tversky a productive and difficult colleague:

the objections you raised against our experimental method are simply unsupported. In essence, you engage in the practice of criticizing a procedural departure without showing how the departure might account for the results obtained. You do not present either contradictory data or a plausible alternative interpretation of our findings. Instead, you express a strong bias against our method of data collection and in favor of yours. This position is certainly understandable, yet it is hardly convincing.


Link: A Bitter Ending: Daniel Kahneman, Amos Tversky, and the limits of collaboration

echo chambers: old psych, new tech

If you were surprised by the result of the Brexit vote in the UK or by the Trump victory in the US, you might live in an echo chamber – a self-reinforcing world of people who share the same opinions as you. Echo chambers are a problem, and not just because it means some people make incorrect predictions about political events. They threaten our democratic conversation, splitting up the common ground of assumption and fact that is needed for diverse people to talk to each other.

Echo chambers aren’t just a product of the internet and social media, however, but of how those things interact with fundamental features of human nature. Understand these features of human nature and maybe we can think creatively about ways to escape them.

Built-in bias

One thing that drives echo chambers is our tendency to associate with people like us. Sociologists call this homophily. We’re more likely to make connections with people who are similar to us. That’s true for ethnicity, age, gender, education and occupation (and, of course, geography), as well as a range of other dimensions. We’re also more likely to lose touch with people who aren’t like us, further strengthening the niches we find ourselves in. Homophily is one reason obesity can seem contagious – people who are at risk of gaining weight are disproportionately more likely to hang out with each other and share an environment that encourages obesity.

Another factor that drives the echo chamber is our psychological tendency to seek information that confirms what we already know – often called confirmation bias. Worse, even when presented with evidence to the contrary, we show a tendency to dismiss it and even harden our convictions. This means that even if you break into someone’s echo chamber armed with facts that contradict their view, you’re unlikely to persuade them with those facts alone.

News as information and identity

More and more of us get our news primarily from social media and use that same social media to discuss the news.

Social media takes our natural tendencies to associate with similar minded people and seek information that confirms and amplifies our convictions. Dan Kahan, professor of law and psychology at Yale, describes each of us switching between two modes of information processing – identity affirming and truth seeking. The result is that for issues that, for whatever reasons, become associated with a group identity, even the most informed or well educated can believe radically different things because believing those things is tied up with signalling group identity more than a pursuit of evidence.

Mitigating human foibles

Where we go from here isn’t clear. The fundamentals of human psychology won’t just go away, but they do change depending on the environment we’re in. If technology and the technological economy reinforce the echo chamber, we can work to reshape these forces so as to mitigate it.

We can recognise that a diverse and truth-seeking media is a public good. That means it is worth supporting – both in established forms like the BBC, and in new forms like Wikipedia and The Conversation.

We can support alternative funding models for non-public media. Paying for news may seem old-fashioned, but there are long-term benefits. New ways of doing it are popping up. Services such as Blendle let you access news stories that are behind a pay wall by offering a pay-per-article model.

Technology can also help with individual solutions to the echo chamber, if you’re so minded. For Twitter users, let’s you view the feed of any other Twitter user, so if you want to know what Nigel Farage or Donald Trump read on Twitter, you can. (I wouldn’t bother with Trump. He only follows 41 people – mostly family and his own businesses. Now that’s an echo chamber.)

For Facebook users, is a browser extension that shows the political biases of your friends and Facebook newsfeed. If you want a shortcut, this Wall Street Journal article puts Republican and Democratic Facebook feeds side-by-side.

Of course, these things don’t remove the echo chamber, but they do highlight the extent to which you’re in one, and – as with other addictions – recognising that you have a problem is the first step to recovery.

The ConversationThis article was originally published on The Conversation. Read the original article.