Mere Exposure and What Everybody Knows

Students in my Intro to the Psych Major course recently had to read and review a pop psychology book. This is a great assignment, by the way, and I really enjoy reading the papers. If only I didn't have a zillion other things to read: research paper introductions, thesis drafts, articles submitted for publication that I've been asked to review, etc.

Anyway, one student reviewed a book on anger, in which the author made something like the following assertion: "You cannot forgive others until you forgive yourself." In his review, the student was using this critically as an example of something that everyone knows anyway. And of course I've heard this before too and was about to let it pass.

But then I thought, "Why not? Why can't you forgive others until you forgive yourself?"

And I sat there for a few minutes trying to think of any reason that this might be true. Not empirical evidence, mind you. Just any semi-logical, halfway plausible reason for the proposed relationship between other-forgiveness and self-forgiveness. But I couldn't do it. In fact, the more I thought about it, the more meaningless the phrase became.

I suspect that the real reason that this assertion seems obvious--along with the kindred, "You cannot love another until you love yourself"--is just that we've heard them before. There is a phenomenon that research psychologists have termed the "mere exposure effect." Simply having been exposed to a stimulus before (even subliminally) causes that stimulus to be processed more easily, to seem more familiar, to be liked more, and--in the case of assertions--be perceived as more true.

This is making me think of all sorts of other related research-based stuff having to do with the automatic tendency to accept assertions as true and with different ways of processing persuasive messages. But I should probably get back to grading papers.

Evidence Shmevidence

I just logged in after a long absence and I have 13 comments! I'm famous! I'll go through those soon and get them posted, but in the meantime ...

I was reading an article on herbal remedies in Parade magazine today (the one that comes with the Sunday paper) and saw a section on Ginkgo biloba. Ginkgo is a tree that we have a lot of on the Fresno State campus. It has funny fan shaped leaves that turn bright yellow in the fall. Anyway, the section on ginkgo contains two interesting sentences. The first says, "Evidence suggests that ginkgo biloba has a positive effect on the vascular system ...." The second says, "Some doctors recommend it to boost memory ...."

One way to read this is that 1) ginkgo has a positive effect on the vascular system and 2) ginkgo boosts memory. The different wordings--"evidence suggests" versus "some doctors recommend"--might be just for the sake of variety. But I don't think this is the case. The reason is that while there may be scientific evidence that ginkgo has positive effects on the vascular system, the best current evidence is that it does not boost memory. So although it cannot truthfully be said that "evidence suggests" that it works, it can be said that "some doctors recommend" it. The change in wording shifts responsibility for what is essentially a false statement onto those unnamed doctors, while still generating positive interest in ginkgo.

Although the average person might not even notice the "evidence suggests" versus "some doctors recommend" difference, I maintain that this is exactly the kind of thing that psychology majors should be learning to notice. There is a huge difference between knowledge based on empirical evidence and "knowledge" based on opinion--even expert opinion. This is especially true in psychology where we have lots of scientific knowledge about the diagnosis and treatment of mental disorders, but we also have people (with Ph.D.s, no less) who claim they will hypnotize you and take you back to your past lives to identify the traumatic experiences that are causing your current psychological problems.

"Some psychologists recommend ..." should definitely put you on guard.

Theory Generation

In Psych 144, I always emphasize the importance of distinguishing phenomena (things that we observe) from theories (explanations for those phenomena). For example, there are sex differences in scores on the math portion of the SAT. No question about it. But there are many different ways to explain this fact that might involve biased tests, a biased society, genetics, and so on.

But this blog has nothing to do with sex differences. What it does have to do with, though, is the fact that no one has commented on any of my blogs. That's the phenomenon of interest for today. And in an exercise straight out of Psych 144, I'm going to generate as many theories to explain this phenomenon as I can. I'll worry about which ones are correct later.

1. Students never look at blog.
2. Students do not know how to post comments.
3. Blogs are too boring to comment on.
4. Blogs are incomprehensible.
5. Students are too dense to think up comments.
6. Settings do not allow comments (because blogger is too dense).
7. Students are afraid to interact with instructor.
8. Students are afraid of appearing to be apple polishers.
9. Students find topics uninteresting.

Other suggestions?

In Praise of Statistical Thinking

In an essay in the Chronicle of Higher education, Eric G. Wilson (an English professor at Wake Forest) writes "In Praise of Melancholy."

Melancholy, by the way, is just a fancy word for depression. The word itself derives from the ancient Greek meaning "black bile," because it was once thought that depression was caused by an excess of black bile in the body.

Anyway, the author of the essay mentions that recent polls show that 85% of Americans rate themselves as happy. He also claims that this is a disturbing trend because melancholy has been the inspiration for much of the world's great art, music, and literature. Yet we are content to "annihilate" it through positive psychology, psychotherapy, and the use of antidepressants like Prozac.

Although there are a lot of interesting things about this argument, there is a fundamental problem that prevents it from ever getting off the ground ... and it's something that I emphasize over and over in Psych 144. In the abstract it's this: evaluating a claim about a statistical relationship requires a comparison of one variable across levels of the other. In the concrete: the author claims that people are happier than they used to be, but he only presents data on how happy they are now. Who's to say they weren't just as happy in the "olden days?" If they were, then the whole idea that we are in the process of "annihilating" melancholy falls flat.

I'm not an expert on happiness (or what psychologists are more likely to refer to as "subjective well being"), but I'm pretty sure there are data out there that show whether or not there has been a change in the happiness levels of Americans across time. I'm also pretty sure that those data show that there really hasn't been much change over time--certainly not changes on the order of what the Wilson suggests in his essay.

But this is a blog, not a journal article. So you'll have to look up those data yourself.

The Meaning of Imply

While lecturing the other day on correlational research, I had a thought about the famous dictum, "Correlation does not imply causation." No, I'm not going to try to debunk it. But I am going to suggest that it be reworded because the word imply is ambiguous.

One meaning of imply is something like guarantee. This is the intended meaning in "Correlation does not imply causation." The simple fact that two variables are statistically related does not guarantee that the underlying relationship between them is causal. It could be causal, but this is not a sure thing.

A second meaning of imply--which is probably more common in everyday conversation--is something like hint at. So if my wife casually notes how full the trash can in the garage is getting, some might say that she is implying that I carry it out to the dumpster. (Note that I wouldn't say this ... but some might.) So "Correlation does not imply causation" could be interpreted to mean that the fact that two variables are statistically related does not even hint at there being a causal connection between them.

But this isn't right because it does hint at it. For example, if caffeine consumption is correlated with later miscarriages, then this suggests that researchers ought to take a look at caffeine consumption as a possible cause of miscarriages. It doesn't guarantee that caffeine consumption is a cause ... but it could be.

So let me propose a rewording of the old dictum: "Correlation does not guarantee causation."

You CAN Do Anything with a Bachelors in Psych

"You can't do anything with a bachelors degree in psych."

In addition to its being plain incorrect, this claim bothers me for several reasons.

The first is that, according to Tara Kuther in The Psychology Major's Handbook, the majority of psychology majors do not go to graduate school ... ever. But of course they must be doing something.

The second is that it implies that there is something unique about psych majors in terms of their ability to get jobs with only their bachelors degrees ... as though sociology, philosophy, and chemistry majors are being handed jobs left and right. Yes, the technical knowledge obtained by chemistry majors gives them the upper hand for getting certain kinds of jobs, but the technical knowledge obtained by psychology majors (e.g., behavioral principles, statistical analysis) gives them the upper hand for others.

The third reason this claim bothers me is that it reflects a misunderstanding of the traditional purpose of an American-style college education, which is not to be trained for a particular career. Yes there are some exceptions; nursing and engineering come to mind. But a bachelor's degree in psychology is not supposed to turn you into a psychologist any more than a bachelor's degree in biology is supposed to turn you into a physician. What both of those bachelor's degrees are supposed to do is turn you into an educated human being who knows a little about a wide variety of fields, a little more about one specific field, and has lots of important general skills like the ability to write and speak clearly, analyze complex problems, motivate yourself, and work effectively with others.

The fact is that a bachelor's degree in psychology can be one step on the way to doing just about anything--including becoming a psychologist, a doctor, a lawyer, a newspaper reporter, an advertising executive, ....

Jumping to Causal Conclusions

There's been a rash of studies in the news purporting to show that some medication, food, or behavior (call it X) has an effect on some aspect of health (call it Y). In Psych 42, I've talked recently about new studies on caffeine and miscarriage and anger suppression and mortality (i.e., death). The latest study concerns birth control pills and ovarian cancer.

All of these studies do clearly show an association between X and Y. However, because none of them is an experiment--where the independent variable is manipulated and extraneous variables are controlled--they do not show that X causes Y. And it doesn't matter how many times the journalists or researchers state that they do.

In the latest example, the researchers showed that women who took the Pill were less likely than women who did not take the pill to develop ovarian cancer. This could be because they took the Pill ... but it is also possible that women who took the Pill differed from women who did not in some other way. For example, women who took the Pill might also have been women who tended to think about and act on their health and well-being more than women who did not--and this difference might have been responsible for the reduction in ovarian cancer.

Yet the article linked above is titled "Pill Prevents Ovarian Cancer for Decades." The word "prevents" clearly implies causation. But if the risk reduction the researchers found was due to something other than the Pill, then then it is not the Pill that is doing the "preventing" and going on the Pill will not reduce anyone's risk.

There are other issues here--such as the effectiveness of statistically controlling for confounding variables--but I'll cover those in another blog. For now, let me give my standard disclaimer about this sort of thing. I am not saying there is anything wrong with this research. It is interesting and important and tells us that there is an association between X and Y that might be a causal one. But researchers, journalists, and the general public need to be much more cautious about jumping to causal conclusions.