Chapter 1: What is the main topic discussed in this episode?
You're listening to TED Talks Daily. I'm your host, Elise Hu. Today, a talk about a really common cognitive bias that most of us share. But given the Dunning-Kruger effect's popularity and culture, you probably think you know all there is to know about it. Cognitive psychologist Robin Kramer unpacks how we might need to think again in his 2022 talk from TEDx Brayford Poole.
I don't mean to brag, but there are lots of things that I'm pretty average at, from playing table tennis, cooking risotto, finding countries on a map, just to name a few.
Chapter 2: What is the Dunning-Kruger effect and why is it important?
Now, in our everyday lives, we're not typically assessed on our skills and abilities, so we're forced to rely on our own judgments. I may think I'm pretty decent with Italian cuisine, but how accurate is my assessment? Now, what we're talking about here is metacognition, our insight into our own thought processes.
If I have good metacognitive insight, then how good I think I am at a particular task should line up pretty well with how good I actually am. Of course, in the real world, this is often not the case. And indeed, we probably all know someone who thinks they're great at navigating maps, when in fact the reality is often the opposite. Not to name any names, of course. But still...
Perhaps you think this applies to other people and that you yourself wouldn't make this sort of mistake. So let's try a quick experiment. I want you to think about how you would rate yourself in terms of your driving ability.
Chapter 3: How do we assess our own skills and abilities?
Would you rate yourself as below average, average, or perhaps even above average? So most people rate themselves as above average, which of course is mathematically impossible and something that we call the better-than-average effect. This is just one of a number of cognitive biases that we see when people judge their own abilities.
Today, I'm going to focus on a related bias, the Dunning-Kruger effect. So back in 1999, two psychologists at Cornell University, Dunning and Kruger, described the mistakes people make when estimating their own abilities. So if we take a sample of people and we divide them into four groups based on their scores on a test and order those groups from lowest to highest. Now, the Dunning-Kruger effect
describes how the weakest performers significantly overestimate their performance. The explanation for this, according to Dunning and Kruger, is that insight and ability rely on the same thing. So if I'm poor at a task, I also lack the metacognitive insight to accurately assess my ability.
Now, this pattern's been seen again and again across a number of domains, from driving skill to exam taking, even chess playing. However, in recent years, a number of criticisms have been levelled at this approach, and we now have reason to believe that this pattern of results is virtually unavoidable. One reason for this is the statistical effect regression to the mean.
Now, this is something that comes about when we have two measures that are related, but not perfectly so. So imagine we have a sample of people and we measure their heights and their weights. Now, height and weight are related. Tall people are typically heavier, but the relationship is far from perfect. So unlike the shortest people in red won't all be the lightest people.
Some of them will be overweight or particularly muscular, for example. Similarly, the top end, the tallest people in blue, won't all be the heaviest people. Some of them will be underweight and so on. Now, as a result, on average, the shortest people will rank higher for weight than they do for height, and the tallest people will rank lower for weight than they do for height.
Now, some people might put forward a spurious explanation for why short people are relatively overweight or tall people relatively underweight, when in fact no explanation is needed. Perhaps more compelling a reason to doubt the Dunning-Kruger effect is that we can produce the same pattern in our data when our data is entirely meaningless.
So if we collect people's test scores along with their self-estimates of those scores, but then we shuffle those self-estimates and then analyze as before, then we still find that same pattern in the data. Of course, any effect that we can find with shuffled or randomized data is one that we should surely be suspicious of.
So given these and other issues with the Dunning-Kruger approach, I was saddened and disappointed, and frankly a little annoyed to discover that the same approach was now being implied in my field of expertise, which is face matching.
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Chapter 4: What is metacognition and how does it relate to self-assessment?
Now, we've all stood in line at passport control, anxiously waiting the passport officer's decision as to whether our ID photos look sufficiently like us or not. Now, we know this task is particularly difficult when the images show identities that we're unfamiliar with.
This is because it's hard to take into account the changes that can happen to the face across time, as well as over different situations, so changes in facial expression or lighting, for instance. We know this task is difficult for passport officers as well, and they also make mistakes.
So this is why I thought it would be particularly interesting to look at the relationship between insight and ability in this important security context. So given the issues we've described already with looking at overall scores and people's self-estimates, I instead decided to focus on individual decision-making. So over a series of experiments, I asked people to look at pairs of images
and decide whether they were a match or a mismatch. But I also asked people to provide a rating of confidence in each decision. Now, good metacognitive insights would be reflected in people being much more confident in decisions that turned out to be correct and much less confident in decisions that turned out to be incorrect.
Now, I think this pattern is particularly fascinating, but also fairly intuitive. The best performers on the test were much more confident in their correct responses in comparison with the incorrect ones. so showed good metacognitive insight.
The weakest performers, on the other hand, were no different in their confidence for the correct and incorrect responses, and so they showed poor metacognitive insight. So what might be going on with these weak performers? Now, it could be the case that they have some sense they tend to perform poorly on tests in general, and so they're just less confident overall in their responses.
However, I didn't find that pattern of lower confidence in my data, at least with individual decision-making. Instead, it's more likely that they were more confident in their correct responses in comparison with their incorrect ones. But this was simply unrelated to their accuracy on each trial because they had poor insight. So how does this all fit in with the Dunning-Kruger effect?
So Dunning and Kruger argued that the weakest performers showed the least amount of insight and they overestimated their performance. And that implied that they had greater confidence. Now, we didn't see that here in our data. The weakest performers didn't seem to be overly confident. However, the Dunning-Kruger effect also describes how insight depends on ability.
And so the weakest performers showed the least amount of insight, overestimating their performance in their case. The weakest performers do seem to show the least amount of insight. They couldn't differentiate between their correct and incorrect responses. So insight does appear to depend on ability, but not in the way that Dunning and Kruger originally thought.
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