Tag Archives: depression

Coyne on adaptive rumination theory (again)

A while ago I blogged about Andrews and Thomson’s *adaptive rumination hypothesis* (ARH) of depression, which holds that depression is an evolutionary adaption designed to help us solve difficult problems. I linked to two critiques of ARH by Jerry Coyne, who is clearly no fan of ARH. Coyne’s now taken his argument to the [pages of Psychiatric Times|http://www.psychiatrictimes.com/depression/content/article/10168/1575333], where he tears ARH to shreds for a third time. The main thrust of Coyne’s argument is that Andrews and Thomson employ a colloquial definition of adaptation (i.e., something that’s useful) rather than the more appropriate evolution definition:
Andrews and Thomson consider depression an “adaptation” because it supposedly helps the sufferer solve problems. But an evolutionary adaptation is more than something that is merely useful. Biologists consider a trait adaptive only if that behavior, and the genes producing it, enhance an individual’s fitness—the average lifetime output of offspring. It is this genetic advantage, and the evolutionary changes in behavior it promotes, that is the essence of adaptation by natural selection. To demonstrate that depression is an evolved adaptation, then, we must show that it enhances reproduction.
Andrews and Thomson don’t do this, or even try. And if they did try, they probably wouldn’t succeed, for everything we know about depression suggests that rather than enhancing fitness, it reduces it. The most obvious issue is suicide, a word that, curiously, does not appear in Andrews and Thomson’s text. Statistics show that those with major depression are 20 times more likely to kill themselves than are individuals in the general population. Evolutionarily speaking, this is a strong selective penalty. Depression also appears to reduce libido and may make one unattractive as a sexual partner. Andrews and Thomson point out depression’s “adverse effect on women’s fertility and the outcome of pregnancy.” Other health problems are comorbid with depression, although it’s not clear whether depression is the cause or consequence of these problems. Finally, studies show that depressed mothers provide poorer care of their children.
As Coyne notes, this is a problem not only for ARH, but also for a number of other evolutionary psychological accounts of depression–essentially, all those theories that posit that the depressive state *itself* is adaptive (as opposed to balancing selection/heterozygote advantage models which allow for the possibility that some genes that contribute to depression may be selected for under the right circumstances, without implying that depression itself is advantageous).

A while ago I wrote about Andrews and Thomson’s adaptive rumination hypothesis (ARH) of depression, which holds that depression is an evolutionary adaption designed to help us solve difficult problems. I linked to two critiques (1, 2) of ARH by Jerry Coyne, who is clearly no fan of ARH. Coyne’s now taken his argument to the pages of Psychiatric Times, where he tears ARH to shreds for a third time. The main thrust of Coyne’s argument is that Andrews and Thomson employ a colloquial definition of adaptation (i.e., something that’s useful) rather than the more appropriate evolution definition:

Andrews and Thomson consider depression an “adaptation” because it supposedly helps the sufferer solve problems. But an evolutionary adaptation is more than something that is merely useful. Biologists consider a trait adaptive only if that behavior, and the genes producing it, enhance an individual’s fitness—the average lifetime output of offspring. It is this genetic advantage, and the evolutionary changes in behavior it promotes, that is the essence of adaptation by natural selection. To demonstrate that depression is an evolved adaptation, then, we must show that it enhances reproduction.

Andrews and Thomson don’t do this, or even try. And if they did try, they probably wouldn’t succeed, for everything we know about depression suggests that rather than enhancing fitness, it reduces it. The most obvious issue is suicide, a word that, curiously, does not appear in Andrews and Thomson’s text. Statistics show that those with major depression are 20 times more likely to kill themselves than are individuals in the general population. Evolutionarily speaking, this is a strong selective penalty. Depression also appears to reduce libido and may make one unattractive as a sexual partner. Andrews and Thomson point out depression’s “adverse effect on women’s fertility and the outcome of pregnancy.” Other health problems are comorbid with depression, although it’s not clear whether depression is the cause or consequence of these problems. Finally, studies show that depressed mothers provide poorer care of their children.

As Coyne notes, this is a problem not only for ARH, but also for a number of other evolutionary psychological accounts of depression–essentially, all those theories that posit that the depressive state itself is adaptive (as opposed to balancing selection/heterozygote advantage models which allow for the possibility that some genes that contribute to depression may be selected for under the right circumstances, without implying that depression itself is advantageous).

what’s adaptive about depression?

Jonah Lehrer has an interesting article in the NYT magazine about a recent Psych Review article by Paul Andrews and J. Anderson Thomson. The basic claim Andrews and Thomson make in their paper is that depression is “an adaptation that evolved as a response to complex problems and whose function is to minimize disruption of rumination and sustain analysis of complex problems”. Lehrer’s article is, as always, engaging, and he goes out of his way to obtain some critical perspectives from other researchers not affiliated with Andrews & Thomson’s work. It’s definitely worth a read.

In reading Lehrer’s article and the original paper, two things struck me. One is that I think Lehrer slightly exaggerates the novelty of Andrews and Thomson’s contribution. The novel suggestion of their paper isn’t that depression can be adaptive under the right circumstances (I think most people already believe that, and as Lehrer notes, the idea traces back a long way); it’s that the specific adaptive purpose of depression is to facilitate solving of complex problems. I think Andrews and Thomson’s paper received a somewhat critical reception (which Lehrer discusses) not so much because people found the suggestion that depression might be adaptive objectionable, but because there are arguably more plausible things depression could have been selected for. Lehrer mentions a few:

Other scientists, including Randolph Nesse at the University of Michigan, say that complex psychiatric disorders like depression rarely have simple evolutionary explanations. In fact, the analytic-rumination hypothesis is merely the latest attempt to explain the prevalence of depression. There is, for example, the “plea for help” theory, which suggests that depression is a way of eliciting assistance from loved ones. There’s also the “signal of defeat” hypothesis, which argues that feelings of despair after a loss in social status help prevent unnecessary attacks; we’re too busy sulking to fight back. And then there’s “depressive realism”: several studies have found that people with depression have a more accurate view of reality and are better at predicting future outcomes. While each of these speculations has scientific support, none are sufficient to explain an illness that afflicts so many people. The moral, Nesse says, is that sadness, like happiness, has many functions.

Personally, I find these other suggestions more plausible than the Andrews and Thomson story (if still not terribly compelling). There are a variety of reasons for this (see Jerry Coyne’s twin posts for some of them, along with the many excellent comments), but one pretty big one is that is that they’re all at least somewhat more consistent with a continuity hypothesis under which many of the selection pressures that influenced the structure of the human mind have been at work in our lineage for millions of years. That’s to say, if you believe in a “signal of defeat” account, you don’t have to come up with complex explanations for why human depression is adaptive (the problem being that other mammals don’t seem to show an affinity for ruminating over complex analytical problems); you can just attribute depression to much more general selection pressures found in other animals as well.

One hypothesis I particularly like in this respect, related to the signal-of-defeat account, is that depression is essentially just a human manifestation of a general tendency toward low self-confidence and aggression. The value of low self-confidence is pretty obvious: you don’t challenge the alpha male, so you don’t get into fights; you only chase prey you think you can safely catch; and so on. Now suppose humans inherited this basic architecture from our ancestral apes. In human societies there’s still a clear potential benefit to being subservient and non-confrontational; it’s a low-risk, low-reward strategy. If you don’t bother anyone, you’re probably not going to get the girl impress the opposite sex very much, but at least you won’t get clubbed over the head by a competitor very often. So there’s a sensible argument to be made for frequency dependent selection for depression-related traits (the reason it’s likely to be frequency dependent is that if you ever had a population made up entirely of self-doubting, non-aggressive individuals, being more aggressive would probably become highly advantageous, so at some point, you’d achieve a stable equilibrium).

So where does rumination–the main focus of the Andrews and Thomson paper–come into the picture? Well, I don’t know for sure, but here’s a pretty plausible just-so story: once you evolve the capacity to reason intelligently about yourself, you now have a higher cognitive system that’s naturally going to want to understand why it feels the way it does so often. If you’re someone who feels pretty upset about things much of the time, you’re going to think about those things a lot. So… you ruminate. And that’s really all you need! Saying that depression is adaptive doesn’t require you to think of every aspect of depression (e.g., rumination) as a complex and human-specific adaptation; it seems more parsimonious to see depressive rumination as a non-adaptive by-product of a more general and (potentially) adaptive disposition to experience negative affect.  On this type of account, ruminating isn’t actually helping a depressed person solve any problems at all. In fact, you could even argue that rumination shouldn’t make you feel better, or it would defeat the very purpose of having a depressive nature in the first place. In other words, it’s entirely consistent with the basic argument that depression is adaptive under some circumstances that the very purpose of rumination might be to keep depressed people in a depressed state. I don’t have any direct evidence for this, of course; it’s a just-so story. But it’s one that is, in my opinion (a) more plausible and (b) more consistent with indirect evidence (e.g., that rumination generally doesn’t seem to make people feel better!) than the Andrews and Thomson view.

The other thing that struck me about the Andrews and Thomson paper, and to a lesser extent, Lehrer’s article, is that the focus is (intentionally) squarely on whether and why depression is adaptive from an evolutionary standpoint. But it’s not clear that the average person suffering from depression really cares, or should care, about whether their depression exists for some distant evolutionary reason. What’s much more germane to someone suffering from depression is whether their depression is actually increasing their quality of life, and in that respect, it’s pretty difficult to make a positive case. The argument that rumination is adaptive because it helps you solve complex analytical problems is only compelling if you think that those problems are really worth mulling over deeply in the first place. For most of the things that depressed people tend to ruminate over (most of which aren’t life-changing decisions, but trivial things like whether your co-workers hate you because of the unfashionable shirt you wore to work yesterday), that just doesn’t seem to be the case. So the argument becomes circular: rumination helps you solve problems that a happier person probably wouldn’t have been bothered by in the first place. Now, that isn’t to say that there aren’t some very specific environments in which depression might still be adaptive today; it’s just that there don’t seem to be very many of them. If you look at the data, it’s quite clear that, on average, depression has very negative effects. People lose friends, jobs, and the joy of life because of their depression; it’s hard to see what monumental problem-solving insight could possibly compensate for that in most cases. By way of analogy, saying that depression is adaptive because it promotes rumination seems kind of like saying that cigarettes serve an adaptive purpose because they make nicotine withdrawal go away. Well, maybe. But wouldn’t you rather not have the withdrawal symptoms to begin with?

To be clear, I’m not suggesting that we should view depression solely in pathological terms, and should entirely write off the possibility that there are some potentially adaptive aspects to depression (or personality traits that go along with it). Rather, the point is that, if you’re suffering from depression, it’s not clear what good it’ll do you to learn that some of your ancestors may have benefited from their depressive natures. (By the same token, you wouldn’t expect a person suffering from sickle-cell anemia to gain much comfort from learning that they carry two copies of a mutation that, in a heterozygous carrier, would confer a strong resistance to malaria.) Conversely, there’s a very real danger here, in the sense that, if Andrews and Thomson are wrong about rumination being adaptive, they might be telling people it’s OK to ruminate when in fact excessive rumination could be encouraging further depression. My sense is that that’s actually the received wisdom right now (i.e., much of cognitive-behavioral therapy is focused on getting depressed individuals to recognize their ruminative cycles and break out of them). So the concern is that too much publicity might be a bad thing in this case, and, far from heralding the arrival of a new perspective on the conceptualization and treatment of depression, may actually be hurting some people. Ultimately, of course, it’s an empirical matter, and certainly not one I have any conclusive answers to. But what I can quite confidently assert in the meantime is that the Lehrer article is an enjoyable read, so long as you read it with a healthy dose of skepticism.

ResearchBlogging.org
Andrews, P., & Thomson, J. (2009). The bright side of being blue: Depression as an adaptation for analyzing complex problems. Psychological Review, 116 (3), 620-654 DOI: 10.1037/a0016242

internet use causes depression! or not.

I have a policy of not saying negative things about people (or places, or things) on this blog, and I think I’ve generally been pretty good about adhering to that policy. But I also think it’s important for scientists to speak up in cases where journalists or other scientists misrepresent scientific research in a way that could have a potentially large impact on people’s behavior, and this is one of those cases. All day long, media outlets have been full of reports about a new study that purportedly reveals that the internet–that most faithful of friends, always just a click away with its soothing, warm embrace–has a dark side: using it makes you depressed!

In fairness, most of the stories have been careful to note that the  study only “links” heavy internet use to depression, without necessarily implying that internet use causes depression. And the authors acknowledge that point themselves:

“While many of us use the Internet to pay bills, shop and send emails, there is a small subset of the population who find it hard to control how much time they spend online, to the point where it interferes with their daily activities,” said researcher Dr. Catriona Morrison, of the University of Leeds, in a statement. “Our research indicates that excessive Internet use is associated with depression, but what we don’t know is which comes first. Are depressed people drawn to the Internet or does the Internet cause depression?”

So you might think all’s well in the world of science and science journalism. But in other places, the study’s authors weren’t nearly so circumspect. For example, the authors suggest that 1.2% of the population can be considered addicted to the internet–a rate they claim is double that of compulsive gambling; and they suggest that their results “feed the public speculation that overengagement in websites that serve/replace a social function might be linked to maladaptive psychological functioning,” and “add weight to the recent suggestion that IA should be taken seriously as a distinct psychiatric construct.”

These are pretty strong claims; if the study’s findings are to be believed, we should at least be seriously considering the possibility that using the internet is making some of us depressed. At worst, we should be diagnosing people with internet addiction and doing… well, presumably something to treat them.

The trouble is that it’s not at all clear that the study’s findings should be believed. Or at least, it’s not clear that they really support any of the statements made above.

Let’s start with what the study (note: restricted access) actually shows. The authors, Catriona Morrison and Helen Gore (M&G), surveyed 1,319 subjects via UK-based social networking sites. They had participants fill out 3 self-report measures: the Internet Addiction Test (IAT), which measures dissatisfaction with one’s internet usage; the Internet Function Questionnaire, which asks respondents to indicate the relative proportion of time they spend on different internet activities (e.g., e-mail, social networking, porn, etc.); and the Beck Depression Inventory (BDI), a very widely-used measure of depression.

M&G identify a number of findings, three of which appear to support most of their conclusions. First, they report a very strong positive correlation (r = .49) between internet addiction and depression scores; second, they identify a small group of 18 subjects (1.2%) who they argue qualify as internet addicts (IA group) based on their scores on the IAT; and third, they suggest that people who used the internet more heavily “spent proportionately more time on online gaming sites, sexually gratifying websites, browsing, online communities and chat sites.”

These findings may sound compelling, but there are a number of methodological shortcomings of the study that make them very difficult to interpret in any meaningful way. As far as I can tell, none of these concerns are addressed in the paper:

First, participants were recruited online, via social networking sites. This introduces a huge selection bias: you can’t expect to obtain accurate estimates of how much, and how adaptively, people use the internet by sampling only from the population of internet users! It’s the equivalent of trying to establish cell phone usage patterns by randomly dialing only land-line numbers. Not a very good idea. And note that, not only could the study not reach people who don’t use the internet, but it was presumably also more likely to oversample from heavy internet users. The more time a person spends online, the greater the chance they’d happen to run into the authors recruitment ad. People who only check their email a couple of times a week would be very unlikely to participate in the study. So the bottom line is, the 1.2% figure the authors arrive at is almost certainly a gross overestimate. The true proportion of people who meet the authors’ criteria for internet addiction is probably much lower. It’s hard to believe the authors weren’t aware of the issue of selection bias, and the massive problem it presents for their estimates, yet they failed to mention it anywhere in their paper.

Second, the cut-off score for being placed in the IA group appears to be completely arbitrary. The Internet Addiction Test itself was developed by Kimberly Young in a 1998 book entitled “Caught in the Net: How to Recognize the Signs of Internet Addiction–and a Winning Strategy to Recovery”. The test was introduced, as far as I can tell (I haven’t read the entire book, just skimmed it in Google Books), with no real psychometric validation. The cut-off of 80 points out of a maximum 100 possible as a threshold for addiction appears to be entirely arbitrary (in fact, in Young’s book, she defines the cut-off as 70; for reasons that are unclear, M&G adopted a cut-off of 80). That is, it’s not like Young conducted extensive empirical analysis and determined that people with scores of X or above were functionally impaired in a way that people with scores below X weren’t; by all appearances, she simply picked numerically convenient cut-offs (20 – 39 is average; 40 – 69 indicates frequent problems; and 70+ basically means the internet is destroying your life). Any small change in the numerical cut-off would have translated into a large change in the proportion of people in M&G’s sample who met criteria for internet addiction, making the 1.2% figure seem even more arbitrary.

Third, M&G claim that the Internet Function Questionnaire they used asks respondents to indicate the proportion of time on the internet that they spend on each of several different activities. For example, given the question “How much of your time online do you spend on e-mail?”, your options would be 0-20%, 21-40%, and so on. You would presume that all the different activities should sum to 100%; after all, you can’t really spend 80% of your online time gaming, and then another 80% looking at porn–unless you’re either a very talented gamer, or have an interesting taste in “games”. Yet, when M&G report absolute numbers for the different activities in tables, they’re not given in percentages at all. Instead, one of the table captions indicates that the values are actually coded on a 6-point Likert scale ranging from “rarely/never” to “very frequently”. Hopefully you can see why this is a problem: if you claim (as M&G do) that your results reflect the relative proportion of time that people spend on different activities, you shouldn’t be allowing people to essentially say anything they like for each activity. Given that people with high IA scores report spending more time overall than they’d like online, is it any surprise if they also report spending more time on individual online activities? The claim that high-IA scorers spend “proportionately more” time on some activities just doesn’t seem to be true–at least, not based on the data M&G report. This might also explain how it could be that IA scores correlated positively with nearly all individual activities. That simply couldn’t be true for real proportions (if you spend proportionately more time on e-mail, you must be spending proportionately less time somewhere else), but it makes perfect sense if the response scale is actually anchored with vague terms like “rarely” and “frequently”.

Fourth, M&G consider two possibilities for the positive correlation between IAT and depression scores: (a) increased internet use causes depression, and (b) depression causes increased internet use. But there’s a third, and to my mind far more plausible, explanation: people who are depressed tend to have more negative self-perceptions, and are much more likely to endorse virtually any question that asks about dissatisfaction with one’s own behavior. Here are a couple of examples of questions on the IAT: “How often do you fear that life without the Internet would be boring, empty, and joyless?” “How often do you try to cut down the amount of time you spend on-line and fail?” Notice that there are really two components to these kinds of questions. One component is internet-specific: to what extent are people specifically concerned about their behavior online, versus in other domains? The other component is a general hedonic one, and has to do with how dissatisfied you are with stuff in general. Now, is there any doubt that, other things being equal, someone who’s depressed is going to be more likely to endorse an item that asks how often they fail at something? Or how often their life feels empty and joyless–irrespective of cause? No, of course not. Depressive people tend to ruminate and worry about all sorts of things. No doubt internet usage is one of those things, but that hardly makes it special or interesting. I’d be willing to bet money that if you created a Shoelace Tying Questionnaire that had questions like “How often do you worry about your ability to tie your shoelaces securely?” and “How often do you try to keep your shoelaces from coming undone and fail?”, you’d also get a positive correlation with BDI scores. Basically, depression and trait negative affect tend to correlate positively with virtually every measure that has a major evaluative component. That’s not news. To the contrary, given the types of questions on the IAT, it would have been astonishing if there wasn’t a robust positive correlation with depression.

Fifth, and related to the previous point, no evidence is ever actually provided that people with high IAT scores differ in their objective behavior from those with low scores. Remember, this is all based on self-report. And not just self-report, but vague self-report. As far as I can tell, M&G never asked respondents to estimate how much time they spent online in a given week. So it’s entirely possible that people who report spending too much time online don’t actually spend much more time online than anyone else; they just feel that way (again, possibly because of a generally negative disposition). There’s actually some support for this idea: A 2004 study that sought to validate the IAT psychometrically found only a .22 correlation between IAT scores and self-reported time spent online. Now, a .22 correlation is perfectly meaningful, and it suggests that people who feel they spend too much time online also estimate that they really do spend more time online (though, again, bias is a possibility here too). But it’s a much smaller correlation than the one between IAT scores and depression, which fits with the above idea that there may not be any real “link” between internet use and depression above and beyond the fact that depressed individuals are more likely to more negatively-worded items.

Finally, even if you ignore the above considerations, and decide to conclude that there is in fact a non-artifactual correlation between depression and internet use, there’s really no reason you would conclude that that’s a bad thing (which M&G hedge on, and many of the news articles haven’t hesitated to play up). It’s entirely plausible that the reason depressed individuals might spend more time online is because it’s an effective form of self-medication. If you’re someone who has trouble mustering up the energy to engage with the outside world, or someone who’s socially inhibited, online communities might provide you with a way to fulfill your social needs in a way that you would otherwise not have been able to. So it’s quite conceivable that heavy internet use makes people less depressed, not more; it’s just that the people who are more likely to use the internet heavily are more depressed to begin with. I’m not suggesting that this is in fact true (I find the artifactual explanation for the IAT-BDI correlation suggested above much more plausible), but just that the so-called “dark side” of the internet could actually be a very good thing.

In sum, what can we learn from M&G’s paper? Not that much. To be fair, I don’t necessarily think it’s a terrible paper; it has its limitations, but every paper does. The problem isn’t so much that the paper is bad; it’s that the findings it contains were blown entirely out of proportion, and twisted to support headlines (most of them involving the phrase “The Dark Side”) that they couldn’t possibly support. The internet may or may not cause depression (probably not), but you’re not going to get much traction on that question by polling a sample of internet respondents, using measures that have a conceptual overlap with depression, and defining groups based on arbitrary cut-offs. The jury remains open, of course, but these findings by themselves don’t really give us any reason to reconsider or try to change our online behavior.

ResearchBlogging.org
Morrison, C., & Gore, H. (2010). The Relationship between Excessive Internet Use and Depression: A Questionnaire-Based Study of 1,319 Young People and Adults Psychopathology, 43 (2), 121-126 DOI: 10.1159/000277001