Better Fathers Have Smaller Testicles, But…

There is currently an article making the rounds in the popular media (or at least the range of media that I’m exposed to) suggesting that testicular volume is a predictor of paternal investment in children: the larger the testicles, the less nurturing, fatherly behavior we see. I get the nagging sense that stories about genitals tends to get a larger-than-average share of attention (I did end up tracking the article down, after all), and that might have motivated both the crafting and sharing of this study (at least in the media. I can’t speak directly to the author’s intentions, though I can note the two domains often fail to overlap). In any case, more attention does not necessarily mean that people end up with an accurate picture of the research. Indeed, the percentage of people who will – or even can – read the source paper itself is vastly outnumbered by those who will not. So, for whatever it’s worth, here’s a more in-depth look at the flavor of the week research finding.

Our next new flavor will come out at the end of the month…

The paper (Mascaro, Hackett, & Rilling, 2013) begins with a discussion of life history theory. With respect to sexual behavior, life history theory posits that there is a tradeoff between mating effort and parental effort: the energy an organism spends investing in any single offspring is energy not spent in making new ones. Since then name of the game in evolution is maximizing fitness, this tradeoff needs to be resolved, and can be in various ways. Humans, compared to many other species, tend to fall rather heavily on the “investing” side of the scale, pouring immense amounts of time and energy into each highly-dependent offspring. Other species, like Salmon, for instance, invest all their energy into a single bout of mating, producing many offspring, but investing relatively less in each (as dead parents often make poor candidates for sources of potential investment). Life history theory is not just useful for understanding between-species differences though; it is also useful for understanding individual differences within species (as it must be, since the variation in the respective traits between species needed to have come from some initial population without said variance).

Perhaps the most well-known examples are the between-sex differences in life history tradeoffs among mammals, but let’s just stick to humans to make it relatable. When a woman gets pregnant, provided the baby will carried to term, her minimum required investment is approximately 9 months of pregnancy and often several years of breastfeeding, much of which precludes additional reproduction. The metabolic and temporal costs of this endeavor are hard to overstate. By contrast, a male’s minimum obligate investment in the process is a single ejaculate and however long intercourse took. One can immediately see that men tend to have more to gain from investing in mating effort, relative to women, at least from the minimum-investment standpoint. However, not all men have as much potential to achieve those mating-effort gains; some men are more attractive sexual partners, and others will be relatively shut-out of the mating market. If one cannot compete in the mating domain, it might pay to make oneself more appealing in the investment domain where they can more effectively compete. Accordingly, if one tends to attempt the investment strategy (though this need not mean a consciously-chosen plan), it’s plausible their body might follow a similar investment strategy, placing fewer resources into the more mating-orientated aspects of our physiology: specifically, the testicles.

Unsurprisingly, testicular volume appears to be correlated with a number of factors, but most notably sperm production (this especially the case between species, as I’ve written about before). Those men who tend to preferentially pursue a mating strategy (relative to an investment one) have slightly-different adaptive hurdles to overcome, most notably in the insemination and sperm competition arenas. Accordingly, Mascaro, Hackett, & Rilling (2013) predicted that we ought to see a relationship between testes size (representing a form of mating effort) and nurturing offspring (representing a form of parental effort). Enter the current study, where 70 biological fathers who were living with the mother of their children had their testicular volume (n = 55) and testosterone levels (n = 66) assessed. Additionally, reports of their parental behavior were also collected, along with a few other measures. As the title of the paper suggests, there was indeed a negative correlation (-0.29) between reported care-giving and testicle volume. This is the point where the highlighted finding begins to need qualifications, however, due to another pesky little factor: testosterone. Testosterone levels were also found to negatively correlate with reports of care-giving (-0.27), as well as the father’s reported desire to provide care (-0.26). Given that these are correlations, it’s not readily apparent that testicular volume per se would be the metaphorical horse pulling the cart.

Pulling the cart, metaphorically, “all the way“, that is.

Perhaps also unsurprisingly, testicular volume showed what the authors called a “moderate positive correlation” with testosterone levels (0.26, p = 0.06). As an aside, I find it interesting that the authors had, only a few sentences prior, reported an almost identically-sized correlation (r = -0.25, p = 0.06) between testicular volume and desire to invest in children, but there they labeled the correlation as a “strong trend”, rather than a “moderate correlation”. The choice of wording seems peculiar.

In any case, if bigger balls tended to go together with more testosterone, it becomes more difficult to make the case for testicular volume itself to be driving the relationship with parenting behaviors. In order to attempt and solve this problem, Mascaro, Hackett, & Rilling (2013) created a regression model, using testicular volume, testosterone levels, father’s earning, and hours worked as predictors of childcare. In that model, the only significant predictor of childcare was testosterone level.

Removing the “father’s earning” and “number of hours worked” variables from the regression model resulted in a gain in predictive value for testicular volume (though it was still not significant) but, again, it was testosterone that appeared to be having the greater effect. Whether or not it would be defensible to modify the regression model in that particular way in the first place is debatable, as the modification seems to be done in the interest of making testicular volume appear relatively more predictive than it was previously (also, removing those two previous factors resulted in the model accounting for quite a bit less of the variance in fathers’ overall childcare behaviors). Just because the authors had some a priori prediction about testicular volume and not about hours worked or money earned seems like only a mediocre reason for justifying the exclusion of the latter two variables while retaining the former.

There was also some neuroscience included in the study concerning the men looking at pictures of children’s faces and correlating the neural responses with childcare, testicular volume, and testosterone. I’ll preface what I’m about to say with the standard warning: I’m not the world’s foremost expert on neuroscience, so there is a distinct possibility I’m misunderstanding something here. That said, the authors did find a relationship there between testicular volume and neural response to children – a relationship that was apparently not diminished when controlling for testosterone.  It should be noted that, again, unless I’m misunderstanding something, this connection didn’t appear to translate into significant increases in the childcare actually displayed by the males in the study once the effects of testosterone were considered (if it did, it should have shown up in the initial regression models). Then again, I have historically been overly-cautious about inferring much from brain scans, so take from that what you will.

I’ve got my eye on you, imaging technology…

To return to the title of this post, yes, testicular volume appears to have some predictive value in determining parental care, but this value tends to be reduced, often substantially so, once a few other variables are considered. Now I happen to think that the hypotheses derived from life history theory are well thought out in this paper. I imagine I might be inclined to have made such predictions myself. Testicular measures have already given us plenty of useful information about the mating habits of various species, and I would expect there is still value to be gained from considering them. That said, I would also advise some degree of caution in attempting to fit the data to these interesting hypotheses. Using selective phrasing to highlight some trends (the connection between testicular volume and desire to provide childcare) relative to others (the connection between testicular volume and testosterone) because they fit the hypothesis better makes me uneasy. Similarly, dropping variables from a regression model to improve the predictive power of the variable of interest is also troublesome. Perhaps the basic idea might prove more fruitful were it to be expanded to other kinds of men (single men, non-fathers, divorced, etc) but, in any case, I find the research idea quite an interesting step, and I look forward to hearing a lot more about our balls in the future.

References: Mascaro, J., Hackett, P., & Rilling, J. (2013). Testicular volume is inversely correlated with nurturing-related brain activity in human fathers. Proceedings of the National Academy of Sciences of the United States of America.

Should Psychological Neuroscience Research Be Funded?

In my last post, when discussing some research by Singer et al (2006), I mentioned as an aside that their use of fMRI data didn’t seem to add a whole lot to their experiment. Yes, they found that brain regions associated with empathy appear to be less active in men watching a confederate who behaved unfairly towards them receive pain; they also found that areas associated with reward seemed slightly more active. Neat; but what did that add beyond what a pencil and paper or behavioral measure might? That is, let’s say the authors (all six of them) had subjects interact with a confederate who behaved unfairly towards them. This confederate then received a healthy dose of pain. Afterwards, the subjects were asked two questions: (1) how bad do you feel for the confederate and (2) how happy are you about what happened to them? This sounds fairly simple, likely because, well, it is fairly simple. It’s also incredibly cheap, and pretty much a replication of what the authors did. The only difference is the lack of a brain scan. The question becomes, without the fMRI, how much worse is this study?

“No fMRI data? Why not just insult psychology directly and get it over with?”

There are two crucial questions in mind, when it comes to the above question. The first is a matter of new information: how much new and useful information has the neuroscience data given us? The second is a matter of bang-for-your-buck: how much did that neuroscience information cost? Putting the two questions together,we have the following: how much additional information (in whatever unit information comes in) did we get from this study per dollar spent? As an initial caveat before I give my answer to the question, I will point out that I am by no means an expert in the field of neuroscience. Though some might feel this automatically disqualified my having an opinion about the field, I would follow that up by noting that there’s are reasons I’m not an expert in the field of neuroscience. As far as I can tell, some of the major reasons include that I have found almost all of it that I have been exposed to either incredibly dull, lacking in perceived value, or both in many cases.

Now that my neuroscience credentials and biases have been laid bare, let’s move onto the question of the day. As with most questions, I’ll begin my answer to it with a thought experiment: let’s say you ran the initial same study as Singer et al did, and in addition to your short questionnaire you put people into an fMRI machine and got brain scans. In the first imaginary world, we obtained results identical to what Singer et al reported: areas thought to be related to empathy decrease in activation, areas thought to be related to pleasure increase in activation. The interpretation of these results seems fairly straightforward – that is, until one considers the second imaginary world. In this second world, we see the results of brain scan show the reverse pattern: specifically, areas thought to be related to empathy show an increase in activation and areas associated with reward show a decrease. The trick to this thought experiment, however, is that the survey responses remain the same; the only differences between the two worlds are the brain pictures.

This makes interpreting our results rather difficult. In the second world, do we conclude that the survey responses are, in some sense, wrong? The subjects “really” feel bad about the confederates being hurt, but they are unaware of it? This strikes me as a bit off, as far as conclusions go. Another route might be to suggest that our knowledge of what areas of the brain are associated with empathy and pleasure is somehow off: maybe increased activation means less empathy, or maybe empathy is processed elsewhere in the brain, or some other cognitive process is interfering. Hell; maybe it’s possible that the technology employed by fMRIs just isn’t sensitive to what you’re trying to look at. Though the brain scan might have highlighted our ignorance as to how the brain is working in that case, it didn’t help us to resolve it. Further, that the second interpretative route seems like a more reasonable one than the first, it also brings to our attention a perhaps under-appreciated fact: we would be privileging the results of the survey measure above the results of the brain scan.

So make sure to check your survey privilege.

The fact that the survey measures are privileged in this case raises the possibility of another hypothetical world: imagine you had done the the experiment and the brain scan as before, but not the survey. In that case, interpretation of the fMRI  data doesn’t even seem possible; description of the brain activation is, sure, but not a profitable understanding of what we would be seeing. This leads to an interesting perspective on the relative contribution of each experimental tool: the majority of the useful information in this study – its  academic value – does not appear to be derived from the brain imaging. The only thing the brain imaging adds is a description of the activation. So yes, the brain scans are technically adding something, but their primary contributions are descriptions of themselves, rather than new interpretations or insights. While such a thought experiment does not definitely answer the question of how much value is added by neuroscience information in psychology, it provides a tentative starting position: not the majority. The bulk of the valuable information in the study came from the survey, and all the subsequent brain information was interpreted in light of it.

Let’s move onto the second question, then: how much did this information cost to obtain? Admittedly, objective information on this question isn’t the easiest to find. The estimates I have come across, however, range from about $400 to over $1000, perhaps even closer to $2000, per subject (the latter article estimates that 20 subjects would cost approximately $40,000). For the sake of comparison, I’d like to discuss how much a recent study I ran cost. The study involved getting subjects to read a hypothetical moral dilemma and answer approximately 5 questions. It was short and approximately as complicated as the non-neuroscience part of the Singer et al paper. Using Mturk (an Amazon site where you can pay people to take your surveys), I was able to pay subjects around $0.10 each (rounding up) for their responses. My sample of approximately 350 subjects cost me well under $50, but let’s say it cost $50 to make the math easy. If I wanted to run that same survey and also collect fMRI data, I would have been looking at a bill of somewhere in the neighborhood of $350,000. On top of the cost, there’s also the matter of time: it takes far longer to get the subject set up in the fMRI and collect the data (which means you need to pay the subject and researchers more for their time), and it also takes far longer to analyze the data you do collect. So there are unaccounted for opportunity costs here as well that we’ll ignore for now.

So now we have a tentative answer for our second question: the neuroscience-version of my study would likely have cost well over 7000 times as much as the non-neuroscience one. Thus, in order to justify the cost of the additional neuroscience, we would want approximately 99.9999% of the information gain of our research to come from the neuroscience information we gathered, and that estimate is actually fairly charitable towards the neuroscience end of things. However, as I previously estimated, we would be hard-pressed to say that even half of the information value of a study could be attributed towards the addition of neuroscience information. In fact, the actual value is likely well below half. In other words, we’re not even anywhere close to justifying the money invested in neuroscience in psychology. Accordingly, I find the justification for the use of neuroscience in psychology to be wanting, and I would advocate the money being dumped into the field (however much that is) be diverted to areas where it could do more research good. Of course, the US could also consider investing $100,000,000 into mapping the brain, I suppose.

Or let me conduct research with a combined sample size of twice the US population. Please?

Is all this to say that no useful information or positive outcomes would be derived from large investments in neuroscience? Well, that depends on two things: (a) what the investment is in and (b) what else the investment might have been in. I can’t speak to how much benefit we might observe from investing the money directly into neuroscience technology itself in the hopes of improving it and/or bringing the cost of its use down. I would also be vest hesitant to speak to what other investments might be more profitable. What I do feel comfortable saying, however, is that if we’re talking about basic, run-of-the-mill psychological research, there is no feasible way that neuroscience is capable of justifying the monstrous costs involved in producing it. The value added from a single neuroscience paper on 30 subjects is not greater than the value added by dozens, hundreds, or thousands of non-neuroscience papers (the precise value of which depends, obviously, on how much you pay your participants). What people and top journals see in psychological neuroscience, I don’t really understand. Then again, I’m not expert in it, so there’s that, I suppose…

References: Singer, T., Seymour, B., O’Doherty, J., Stephan, K., Dolan, R., & Frith, C. (2006). Empathic neural responses are modulated by the perceived fairness of others Nature, 439 (7075), 466-469 DOI: 10.1038/nature04271