Now let's look at an example going back to the 1950s. And this example is going to illustrate how difficult it is to make valid reverse inferences, even if you have a lot of control and you can actually manipulate the brain directly. So this is a picture of Jose Delgado who put himself in the ring with this brave bull, who's charging him at the moment, and he has implanted an electrode into the bull's brain. And he says, I discovered the aggression center of the bull's brain. To prove it, I'm going to do this demonstration with the bull charging at me. And here is with the radio antenna you see there. Now he's pushing the button, and the bull skids to a halt, and in his words becomes as placid as Ferdinand. So did he discover the aggression center? Well, let's look at where that electrode was in the brain. This is the brain of a cow, different cow, not the same one, and there is the caudate nucleus, which is where that electrode was placed. And this electrode, as we just saw, could be part of the pleasure system, but mainly it's also heavily involved in the initiation of movement. And what happened is, when he pressed that button, the bull was forced to turn continuously to the right. So you or I might be also very confused, and we might stop being aggressive, if suddenly something made us turn to the right. [LAUGH] Right? But it doesn't have to be the aggression center of the brain. So this and many other examples demonstrate how difficult it is to really make reliable reverse inferences about the brain representations that underlay any particular task, emotional or psychological state. Is it impossible to make reverse inferences? Well, Max Coldheart in one challenge, in 2006, in a nice series of challenge and reply papers, posed this question. Perhaps functional neuroimaging hasn't taught us anything about the brain so far [LAUGH]. So, that's one extreme view and I'll let you go through those replies yourself and see what you think. But I think, that reverse inference can be done, it's just not very easy. So, here are two strategies, and one strategy is to leverage what we know about neuroscience. So, if you have strong prior knowledge about what does or doesn't activate a particular brain area, then activation of that area becomes an interesting answer. I'll show you an example of that. And the second strategy is what we'll call quantitative reverse inference. Which is, we have a brain marker, maybe it's activation of a region like the caudate, and we assess that activity across our candidate set of tasks. We can quantify a sensitivity, specificity, positive predictive value. And this might require testing across many task contexts and study populations. So it's not the job of one study but the job of many studies together. Both of these are complimentary. So they can and should be used together. So here's an example of the first approach. And this goes back to Kosslyn's work again, which is still one of my favorite examples from the field of using brain imaging to test the psychological theory. So this goes back to a debate between the Kosslyn and Pylyshyn pollution which started back around 1973, where Pylyshyn claimed that mental imagery, when you form a mental picture of something in your mind, is propositional. It's a set of logical rules. So the feeling of seeing mental images is really an epiphenomenon. For example, if you imagine a map where the treasure chest is to the west of the palm tree, then you don't actually see that map in your head, or at least not in an important way. There's a set of logical rules. Like is two left of [LAUGH] and operations like that. Kosslyn said no, mental imagery is probably depictive. It's analog, it uses the machinery for perception. And this debate went on for decades. And it was finally resolved, in many people's view, by brain imaging evidence, and it goes like this. It hinges on this brain area here which is primary visual cortex along the corcoran fissure. And this graph here some you a plot from earlier work from Roger. That shows that the visual cortex, V1, is mapped where different points on the retina map to different points on the, the cortex. And what they've done here, is they've shown this stimulus here, like this ring that you see on the left, and on the right is the actual primary visual cortex from the monkey where you can see the pattern of 2-deoxyglucose uptake metabolic changes. So there's a very strong mapping. And in particular the fovea in the center of the field is mapped to the back of the brain and the peripheral field is mapped more anteriorly to the front of the brain. So there's a strong prior expectation about what does and doesn't activate visually cortex. What Kosslyn did is a series of experiments and one of them, he asked people to imagine a small letter A versus a big letter A. And what he saw, was that when they imagined the small A, there was activity concentrated near the area of V1, and when they imagined a big A, it spread more anteriorly [LAUGH]. Just like you'd expect it to if it's depictive and analog. Very hard to explain that with a propositional representation structure. So that's one kind of answer. The second example is an example from meta-analysis. In meta-analysis, as we'll see later, is the analysis results across many different studies. In neuroimaging, it's an analysis usually across many types of studies of different kinds, and that can help quantify our ability to make reverse inferences. So this graph here, shows you results, a meta-analysis from six different kinds of studies. And what you see here is, the task categories are on the bottom, there's at least 40 studies of each type or so. And they include studies of emotion, inhibition, long term memory, pain, switching attention, working memory. Then what we were able to do is take those seven types of study and ask if one activates, a secondary somatic sensory cortex, that's two, what's the probability that they're in pain [LAUGH] or doing a pain task, rather than any of these other categories? And what we found here in this case, the probability of being in pain given F2, which is the positive predictive value, is quite high, almost 90%. So that's a type of reverse inference and it illustrates how it can be made among a defined set of alternative candidates. So now let's think a little bit as we wrap up this lecture about what brain mapping is good for and what it can and can't be used for. It can be used to make inferences on the presence of activity, that can either help us test a theory, as we've shown here, or it can help us to categorize the pattern of responses to a task for theory building and for exploratory analysis and development. It can limit the false positive rates to a specified level, where we get only the stuff we really believe is true. And we can leverage hypothesis testing to provide evidence on a variety of theories, if those theories care about whether there's activity or not. So we can test, is brain area r involved in task x. What brain imaging, or brain mapping, isn't really good for is providing reverse inference, not directly at least. To do that we need clever experimental designs, specialized analyses, many studies and conditions. Secondly, it's not very good for establishing meaningful effects or effects that are significantly large, or meaningfully large. So it's really terrible for estimating effect sizes and predictive accuracy, and we'll talk about this more in later modules. And finally, there's a number of assumptions that underlie getting the map that I showed you, or any map. And brain mapping isn't very good for testing those assumptions. You have to do it yourself. And finally, brain mapping isn't very good for comparing evidence for different kinds of theories. It's a hypothesis test of one theory, but just because a model or a test shows some effect, it doesn't mean that that's the right model or the best model or that it's even close. So other techniques are also needed to actually compare models and to develop, essentially a picture of what the best models are that explain brain function. That's the end of this module on psychological inference.