Agnostic Brain, Biased Mind - what does the FFA do?

Many neuroimaging studies have repeatedly found an area in the human brain that seems to be involved in processing visual faces. This area located in the fusiform gyri in humans, has been affectionately named the fusiform face area or FFA. The FFA is most active when we are looking at pictures of faces, and almost non-responsive to other types of visual items such as objects, houses, scenes, random textures, or a blank screen. Prosopagnosics, who are not able to recognize faces, but are still able to detect the presence of a face and also show no difficulty in processing other types of visual stimuli, have been shown to involve less FFA activity. Even more compelling, patients with lateral occipital lobes lesioned lose some form of object-processing, but show intact face processing. And yet other patients with lesions that have affected the FFA, have problems with face processing (acquired prosopagnosia) but intact processing for other stimuli. The evidence strongly suggests that there is something special about faces, and something about the FFA that deals with this specialization.

The debate regarding the FFA pertain to whether it is the only region or even a critical region that does face processing. Some labs have shown that face processing information can be found in other regions of the brain that are not the FFA. Yet some labs have shown that the FFA is recruited to process fine levels of category distinctions. For example, bird and car experts have been shown to engage some level of FFA activity when processing these stimuli compared to novices. These findings suggest that the FFA is not processing faces per se, but visual representations that have come to require high-levels of fine discrimination through experience, of which faces are the best example of this currently.

I suggest that a more flexible definition is called for when thinking about the FFA and its role in processing visual information. Certainly, it does seem that faces occupy a special place in human experiences. On the other hand, it is difficult to explain why there would be a brain region that codes for faces and faces along based simply based on genetic or biologically determined causes.

In terms of a neural network, if indeed the brain consists of many different sub-types of neural networks that conglomerate to form one large complex network, the FFA is a sub-network specialized to perform a specific operation that is maximized and specialized (trained) for a specific information domain - faces. This or these specific operation(s) could involve identification, discrimination, recognition, or all of these, or even a yet unknown operation. Certainly neural network non-linearities can surprise us! Moreover, these operations have been tuned for a specialized class of stimuli that consists of eyes, nose, mouths, and other visual characteristics of faces when occurring together as a whole (whether from external input, or through internal imagination or retrieval).

What this means is that if you were able to "remove" the FFA, and plug it into a computer so that you can feed this FFA network with inputs and measure its outputs, you could theoretically feed it anything, but the information would be most meaningful or organized when the inputs correspond to information about a face. Of course, this would require us to know what is the language of the input to perform such an experiment.

Other types of inputs may elicit some level of meaningful output of the FFA. Neural network do that. Yet other types may elicit nothing at all. This does not necessarily mean that the FFA outputs from such inputs is useless, nor does necessarily mean that it is used! It is just output. What higher-level brain mechanisms do with the output depends on the task, and how the brain is wired to treat outputs from its sub-networks. It may be ignored, or it may actually incorporate relevant information. That is, the FFA is agnostic to the incoming information. It does not care. It will process it anyway. But other regions decide whether what is it saying needs to be incorporated or not, or if it should be further modified even.

Such a view would reconcile why the FFA is special for faces, yet seems to be carrying some information about other stimuli. It would also be consistent with the idea that information about faces is certainly also available to a certain extent in non-FFA regions, the same principles being applied to these other sub-networks. It would also be consistent with how self-organizing behavior in neural network (see von der Malsburg article [link]) can lead to a consistent topology across every person that processes a particular stimulus in a particular way in a particular spatial location.

This is probably not a new idea, but needs to be clarified in the literature I think.

Cognitive abilities in kindergartners and first graders: A comparison, evaluation, and extension of models using data from Robinson et al. (1996)

Paper submitted for final in structural equation modeling class, Spring 2009, UIUC Psychology. This paper is a critique of Robinson et al.'s (1996) paper on "The structure of abilities in math-precocious young children: Gender similarities and differences", published in the Journal of Educational Psychology (Vol. 88, Iss. 2, p341-352). This current paper, though, focuses on the age differences in abilities of kindergartners and first graders.

It is known that very young children show less differentiated cognitive abilities. Children who perform well in tests such as those involving math, tend to have correlated performance in other tests such as in verbal tests. As children age and progress towards adolescence, however, their cognitive abilities becomes differentiated so that abilities such as math and verbal abilities are not necessarily equally developed in the child.

Presumably, this occurs because when children are very young, they are untrained and unaffected by external factors such as education and related experiences (e.g. streaming into majors). Thus, the best predictor of the child's performance is the individual difference or a general factor. With age, the child undergoes specialization where children start to develop more specific knowledge in selective domains. Some children become more trained at math, while others at language. Importantly, these abilities aren't always equally developed. This may be the underlying reason for differentiated abilities in older children.

This current paper is a methodological exploration of the data in Robinson et al. (1996) using various modifications of the basic structural equation model. The main results are consistent with differentiated abilities in first graders relative to kindergartners. Some discrepancies in Robinson et al.'s (1996) paper are noted as well.

[Download pdf of paper]

Default Network, Meditation, and Focus Training

A recent study found that teaching children to focus improves their health outcome [ScienceDaily report]. In relation to the default network in the brain, perhaps one of the things that such early training does is to improve the individual's ability to regulate default network activity. DN activity has been linked to self-reflection, self-monitoring, day-dreaming, task-unrelated thoughts etc., and has often been seen to be negatively correlated to one's ability to perform a task. That is, the more you are able to disengage your default network, the better you can perform the task. This is presumably because your attention is more focused and not distracted by task-unrelated thoughts.

It is then not hard to see the link between DN activity regulation and meditation. Meditation is an act of self-regulation of thoughts, and has been related to several positive outcomes, in terms of physical and mental health and ability. If we apply to adolescence and aging, perhaps one form of training that would be extremely deterministic of cognitive efficacy in older adults is the amount of focus training experienced.

Likewise, if we were to train indivduals on how to focus their mental thoughts, and improve them over time, might brain activity be modulated? And subsequently, might cognitive abilities be improved or preserved better with age?

Admiring a Predecessor's Work

In the course of writing my dissertation, I come across John Horn's work on Fluid & Crystallized Intelligence over the lifespan back in 1965. This is his thesis that he did while here at University of Illinois, and the copy is in our library. I borrowed it because in his work, he talks about how the factorial structure of psychometric intelligence changes with age.

The first thing I noticed about this work was that it was typewritten! Of course, it is not surprising, since back then, computers were not as available. Its not that. It was because I imagined the painstaking hours it took to generate this written document. What happens when you make a mistake on one single letter halfway? What happens if a fire burns the paper? Did someone digitize? I certainly hope so! How did he do all those calculations? It is extremely humbling to know that others have done this without the huge aid of modern technology and still produced such a marvelous product.

The next thing was that this thesis was signed by Cattell, well-known for formalizing this dual-factor theory of intelligence. Imagine, he touched this piece of paper. This is not sentimentality. This is reverence. I can only hope that my own work will one day be deemed useful to someone, even if only slightly. This is a perennial concern, beyond my control...but it is a strong hope. So much work has been done in the past, of which we mostly overlook or disrespect in our own ego to validate our own thoughts. We must recognize that "there is nothing new under the sun". But what has been given us is the joy of refreshing the old, and progressing into it in greater depths.

The final thing regards what Horn studied. Basically, he found that young adults perform better at tests of fluid intelligence than older adults, and older adults perform better than young adults on tests of crystallized intelligence. This is quite a well-known notion, of which I hear very little about these days. Perhaps it is my own ignorance? I am not sure, but reviewing this work sparks some need in me to investigate this further. Hence the impetus to pursue adolescent research to "fill" up the gap in lifespan studies in cognitive aging, which has focused on older adults. Perhaps much has already been done, I just haven't been in contact with this field or literature...time will tell. I will have to read up more. The graph in this photo is from his thesis. It is hand drawn, and it truly speaks a thousand words.

I Think That I Shall Never See, A Brain as Pretty as a Tree

What if, sunlight to a tree, is as information to the brain? A tree needs sunlight to survive, to produce food. In response to this basic need, the tree spends a lot of its effort to maximize its ability to obtain sunlight. It does this by forming more leaves, and by spreading those leaves out at widely as possible to cover as much area as possible. Pushing this idea even further, to the extent that the tree covers a portion of area, that is the amount of sunlight it can absorb. Sunlight falling on other areas will be lost to the tree (albeit there might be secondary or tertiary transfer of energy via light reflection, diffusion, and other means). One important parameter that would determine the success of sunlight absorption for a tree would then be leaf surface area. Specifically, greater surface area would increase sunlight absorption rate.

Now, we project this idea onto the brain, of course being well aware that the brain is much different from a tree, although, probably not very very different. The brain is in the business of representing information. Its very function is the processing and retaining of all the information fed into it from the moment of its development. It would be interesting to pursue when this onsets, but that is a digression for later. Nevertheless, the brain develops in tandem with its experience with information. Some of that information is hardwired, or genetic. Some of that information is nurtured, or environmentally experienced. The role of each neuron then, in cooperation with all the other neurons in the brain, is to keep EVERY SINGLE EXPERIENCE, whether internal or external.

Why does the brain want to do that? Well, that's the same as answering why does a tree want so much sunlight for? We can only provide partial understanding here, because this borders on the domain of philosophical and religious pursuits. Biologically, a tree seeks sunlight as part of its nutritional source for the purpose of ultimately creating more trees. That is about as far as we can describe based on observation. This is, in a way, a tautology. Because what we impose as the purpose of the tree, is in fact, what we see the tree already doing. Therefore, such an answer may not satisfy some, but it is a partial answer at the least. Turning back to the brain, again, only a partial answer is given. The brain seeks to contain as much information as possible, because that's what is already observed that it is doing, and perhaps, this information helps the organism to survive, and to produce more organisms of this kind.

More importantly here, we shall consider how does the brain perform this function of representing as much information as possible. The tree does it by increasing surface area exposed to the sunlight. The brain's equivalent would be to increase the number of neurons it has, the connections between these neurons, the variability in the way these neurons can activate. Some smart person might be able to come up with an equation that tells us how much information a given brain with a given number of neurons and connections, and variability in activity, can hold. This could somehow be mathematically related to the concept of surface area...

However, there is a problem in terms of space. While the brain is fantastic and has way superior computational capacity, it is still finite. That is, there may come a time when a given person's brain can no longer process anymore new information. Maybe it has come already, just that we don't know it or that its not as big of a problem as we might think, given we have external aids for our memory now, through things such as computers, books, paper, language, and symbols. This finite capacity is indeed a problem, but our brains have a rather interesting way of solving it, at least to a great extent. Lets turn back to the tree for a moment, because its a greener thought. Lets say that to get more sunlight, the tree has two ways of doing it given a fixed amount of material. It can send more branches out with many leaves, or it can make fewer but bigger leaves. If it sends out more branches, it would have to content with using some of that material to make tree-parts that don't absorb sunlight (branches). If it makes bigger leaves, it may have to content with those leaves blocking each other out, since they will be close together as there are no branches to help spread them out. In the same way, the brain might have two ways of holding information within a limited amount of material. It can create more and more connections with more neurons, or it can use the existing neurons and connections in different ways. Here is where the tree analogy might break down. Unlike information, sunlight to a tree is a one-dimensional problem in the sense that it only needs to worry about expose area. Information, however, is obviously multi-dimensional, with auditory, visual, tactile, odor, taste modalities in the sensory domains, and countless of types of dimensions when you think about concepts and their associations, temporal information etc. Another dissimilarity between a tree and the brain is that most trees only make one kind of leaf, or grow with a certain fixed physical structure. The brain is able to flexibly use neuronal connections to group neurons in very dynamic ways. So, while a tree either has small or big leaves, the brain may use both small groups of neurons encoding some type of information, as well as bigger groups encoding other types of information.

The maximum surface area of the brain (meaning the physical ability of the brain to differentiate between its billions of states of activity using its neuronal connections), limits the total amount of different information the that brain can keep. This will be developed in later blogs. Here are some teasers. One brilliant way of reducing the space needed would be to encode information in terms of similarities and differences. And also, unlike a tree, the brain in the organism makes decisions about what the organism should do, affecting the environment and modifying subsequent experiences, as opposed to being completely at the mercy of the experiences.

Next time...."Similarities and Differences", and "The Brain, The Tree, Intentions, and Decisions".

Cool Studies at HBM 2007 Chicago

Day 1
Cultural Neuroscience
Thinking of culture using a top-down versus bottom-up framework. Trey Hedden (MIT) and Angela Gutchess (Harvard University)...notable speaker presentations.

Visual Field Maps, Plasticity, Reading
Discovery of retinotopic visual representation in visual cortical areas other than V1. Apparently, V2, V3, even V4 and MT have some retinotopy. Speaker session by Brian Wandell (Stanford University).

Day 2
Brain Noise
Didn't attend this one, but it seems that people are looking into neural noise as a predictor of subsequent brain activity and behavior. McIntosh was one of the speakers.

Manipulative Neuroscience
Awesome talk by Mitsuo Kawato (ATR, Kyoto). He is the brainchild of DB, humanoid robot that is able to mimic human movements by visual observation, eg drumming, juggling, dancing. The talk covered latest research about controlling robot movements through brain-computer interface as well as visual and tactile feedback.

Perceptual Decision Making
Great talks relevant to the visual discrimination project. Generally, I got ideas about how to proceed with the project in terms of possible analyses, and also the fact that others have done this before. The main question is, how does the brain make perceptual discriminations of visual information? What are the mechanisms and neural correlates? Most notable speaker for me here was Paul Sajda.

Day 3
Dual Brain Systems
Control vs Representation systems in the brain. Typically showing that the control network resides in frontal, parietal regions, and representations in the primary and secondary unimodal areas. Check out www.walterschneider.net.

Repetition and the Brain
Another notable symposium of talks. Kalanit Grill-Spector hosted this one. The topic is self-explanatory, but there were some main novel directions. There is distinction between repetition suppression for immediately repeated stimuli vs stimuli repeated over interspersed trials (Grill-Spector). There is an interesting finding that for interspersed trial repetition of object naming tasks, pre-op patients for removal of lateral anterior temporal poles showed normal repetition suppression of repeated objects was observed in the ventral visual areas. But after operation with temporal poles removed, suppression disappeared even in lower perceptual areas suggesting that suppression has a top-down source in this case (Rik Henson). Another contention was Grill-Spector's testing of the fatigue vs facilitation models of adaptation effects. She found evidence for fatigue rather than facilitation, but note that her design involved immediate repetition.

Day 4
Representation and Processes
Didn't attend all, but most notable for me was John-Dylan Haynes' talk on reading hidden intentions in the human brain. They used classifier algorithms on clusters of voxels in the whole brain to identify brain areas that would reliably discriminate between stimuli. This could be applied to the visual discrimination project.

3T Siemens Allegra

This is where its all done. You put someone in there, make them do some cognitive task. Compare it to when they are doing another task or doing nothing. Then make some inferences about the brain activity and the brain regions involved in performing the task.