Origami and the Brain

Origami. The art of folding paper into shapes using a single sheet of paper without tearing or cutting. Perhaps, at an abstract level, this may be likened to what our brains do. We have one brain. We can't make big changes to it, like take one part of the brain and manually "connect" it to another part of the brain. Rather, we have to work within the limits of certain neural connection rules to establish a certain way to get to an end state.

For example, some rules may be related to the fact that our neurons have many short range local connections with neighboring neurons, as well as, some long range connections to more distant groups of neurons. Establishing and pruning these connections is dependent on time and stimulation from external as well as internal events. These events can be cognitive or biological or physical (e.g. the intention to retrieve a memory, or some neurotransmitter regulation, or some visual energy input, respectively). Within this system, our brains try to represent external information, and to generate certain actions or responses.

In a similar manner, in origami, each fold is like an imprint of an event that happens. The effect of folding, however, is limited by the thickness, elasticity, and size of the paper, as well as the force of the folding. Folding could be a sharp strong crease, a light depression, or a curve. Folding also occurs along specific lines or regions on the paper at a time. Finally, folding has temporal order. Through a combination of these factors, the paper encodes what forces have been exerted on it, and represents all of that in a particular physical form. The end state.

The end state maybe be a meaningful shape, or it may have a meaningful function. We can transform a simple piece of paper into a form of a crane, or a box, or a really complex shape (origami experts have been able to do wonders!). We can even use the tension inherent in the folded paper as a spring with tremendous kinetic energy when released. We can also use folding to allow a large piece of material that ordinarily would not fit in specific area to conform to the shape and therefore fit in the area.

Likewise, the brain performs an interesting function in incorporating sensory information from the physical world and representing all the rich material within a single piece of organic tissue. This "folding" of information from one state to another may be a framework to understand neural function.

Consider that we can quantify the physical forces and characteristics of a piece of paper and its folds. Based on low level parameters, we can then determine what the origami will look like, what it can do, what properties its resulting form maintains. Applying a similar method to parameterize neural function may allow us to better describe how the properties of the brain relate to cognition and behavior. For example, the ease with which a paper folds may be dependent on the thickness of the paper (for a given material elasticity/rigidity/brittleness). This will in turn determine how much force must be applied to the paper to achieve a fold of a certain angle. In the same way, one property of the brain may be how strong the connections in a certain neuronal region may be. The stronger the connections, the easier it may be for a signal in one region to affect the activity in another. Another case in point, the brain maintains a certain level to generate new neurons in key parts of the cortex. Neurogenesis is known to occur even in late adulthood in the hippocampus and the peri-ventricular walls. Importantly, recent studies have shown that neurogenesis may be helpful in overcoming drug addiction. A possible mechanism might be that the new neurons enable the brain to represent existing addiction behaviors (information "folding"), in a new way that discourages addiction [link to relevant post]. Moreover, it is possible that different individuals have different rates, or ability, of neurogenesis, and external events or neurochemical interventions may also encourage neurogenesis. It is this rate of neurogenesis that might be a candidate parameter that determines how much a particular brain can fold.

Of course, this is all analogical. There is no necessary association between paper and brain. But, this presents an interesting way to approach the problem of quantifying brain function. Paper folding has been applied to several interesting real life problems. For example, the folding of solar-energy panels into a satellite so that large plates fit into a small structure for launching, and unfold in space to achieve maximum surface area for efficient energy collection. In addition, protein folding occurs according to the electro-chemical forces at the molecular level. Paper folding has been applied to understanding and even manipulating these forces to make protein molecules that achieve specific helpful biomolecular functions. Here's an example of applying origami to practical problem from an MIT group [link].

After all, the reason why origami is meaningful, is because we perceive cranes in a few simple folds.

Separation vs. Association

A key function of the brain is to first, process the fact that we are encountering different types of stimuli at every moment, and second, process the simultaneous fact that while there are these different types of stimuli, there are also many consistencies that reflect modifications of the same stimulus at a higher level of abstraction.

One way to evaluate what a cortical region may be doing with respect to this separation/association dichotomy may be to determine the number of neurons at the first level relative to the second level.

If the ratio of neurons at the first relative to second level is large, then the function of the second level is probably to associate. This is a many-to-few limitation. So various permutations and combinations at the first level are funneled into the reduced dimensionality of the second level. Therefore, some combinations are subsumed.

If the ratio of neurons from first to second levels is small, then there is the potential for expansion. The problem becomes a few-to-many scenario. The same combination at the first level may elicit several possible outcomes at the second level. There is information expansion.

...

PhD

Well, it is the start of the first weekend after defending. How was the defense? It was utterly fun. How often can you squash 5 brilliant minds in one room and have them talk about your work? How often can you debate with them and have them listen to your thoughts on things? How often can you hear them agree or even disagree with you in the most honest sense of it all? I would wish this on anybody who dares to try.

No one knows it all. But the defense is about stating what you know, and what you know you don't know. It is about being honest, and seeking truth. If what you find is real, it will bear itself. If what you think is true, you will find it. Sound familiar?

After the defense, we all went to Jim Gould's to have dinner. And it was, how shall I put it, fun! I think I felt it, that warmth of accomplishment. So that food tastes better. Sweetness has a fragrance, salt floods with depth, sour comes with juiciness, and bitter? There is no bitter.

Soon after, we watched an amazing movie - Inglorious Basterds! What a choice right? Brad Pitt was brilliant. Incidentally, Brangelina was in my defense.

The next day, I cleaned up the mess that was my apartment. It felt good to exert mindless sweat. The day after, we watched Mamma Mia. Tomorrow, BBQ!

That's what PhD is about, what happens before and after.

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.

3T Trio finds a new home in Beckman Institute

The Siemens 3T Trio is a full body MRI scanner. The Beckman Institute just recently acquired it. Today, the machine was brought to the basement of the Beckman building and we were fortunate enough to have some free time to glimpsed part of the process.

The scanner was brought in through a hole they have in the back of the building. It had rained the night before, so the ground around the hole was a little soggy. More importantly, they had to move fast because more rain was coming. The movers had to remove the heavy covers on the hole, lift the magnet bore and lower it into the basement, where there is a trolley for them to push the magnet into its final place. The movers took a break halfway because the rain did come anyway, before they could finish, but they continued later. As far as I know, the scanner is in the basement now, just waiting to be tested and used!

This is a full-body scanner, compared to the head-only scanner 3T Allegra. It should provide more uniform signal, although the 3T Allegra is sometimes better for certain sequences, or so I hear. So we'll see which one shines. They will move the 3T Allegra, head-only scanner, which is right now at the BIC down south, up into the Beckman basement as well, once this Trio is fully functional. There will still be about a month or so of testing and installation before we can begin to use it.

If you are my Facebook friend, you can check out other photos I have of this there [Facebook photo link].

A structural model of aging, brain and behavior

Possible working structural model that can be tested with measures of stimulus, behavior, neuro-functional, neuro-anatomical variables. The dynamic influences of age and "culture" can also be tested. Culture here refers to long-term experiences of any kind. More complex models can be postulated from this current framework by adding more factors, or measures, and by also constraining the specific weights and covariances. In the broadest sense, the weights and covariances are modeled linearly. However, certainly, non-linear functions can be imposed. The result of such impositions would be a neural network with non-linear activation functions.

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.

Studying Adolescents

The more I research into aging, the more I think that one important aspect of lifespan research is the adolescent period. This is an "impressionable" age, and there may be a good reason for that. Longitudinal neuroimaging data is needed to evaluate the impact of life experiences on determining subsequent aging outcome. Possible future pursuit?

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".

Why The World Should Use BrainVoyager

Models that Account for the Same Data

Perhaps what our minds are doing is accounting for the data. The data is everything around us that we experience with our senses. This information is fed into our neurons, which, by virtue of their network organization, perform some sort of operation on this information. This operation can be likened to a form of model fitting (for those of you who are familiar with the modeling world). Our neurons constantly flux in an effort to represent the information we encounter in the most stable possible way, that allows us to incorporate new information as well as to maintain old information, and even to allow old information to modify new ones.

Consider the method called principal components analysis. This is nothing but redefining the data in terms of another set of dimensions. It thus appears that the same data can be understood in different ways, without changing the data one bit. Furthermore, using one set of dimensions over another set is simply dependent on one's goals or assumptions when trying to arrive at an explanation or investigation.

So then, the question is, which approach is scientific?

Reverse Engineering Brain Networks: Testing the Brain like One would Test a Neural Network

Typical steps in a neural network modeling study are the definition of a particular cognitive phenomenon, the creation and definition of the network model by specifying the neural architecture, activation functions and the learning rules. One then sets out to test
how well the network model matches up with the phenomenon, and to the extent that it does and is parsimonious, and has neural plausibility, it is a good model.

It is then not difficult to imagine how we can do the same thing with the brain by treating it as a neural model that's already built, and we're just trying to discover its architecture, its activation functions and its learning rules. Thus, we run the brain through simulations, observe the input and resulting output, and hypothesize the parameters that led to the observation.

We can then reverse engineer these parameters into the model (which is what we do anyway), and again, test how good the model is.

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.

HBM: Ideas: Visual Discrimination Project: General Questions

1. Is there poorer behavioral perceptual discrimination with age?
2. If so, what are the neural correlates? Is it a perceptual representation problem (ventral visual dedifferentiation)? Or is it a selection/decision-making/control process problem (noise, non-selectivity in frontal cortex)?
3. Is this the same across all types of stimuli (eg faces, patterns, random shapes)?
4. Is there a constant in terms of brain activation pattern across all individuals which is necessary for discrimination?
5. Which parts of the brain are predictive of whether the individual is able to discriminate visual stimuli (classifier algorithms on whole brain)?
6. What leads to individual differences in performance? And if all individuals are equated at some level of performance, do the individual differences disappear?

Binding and Bandwidth

This is an idea about what might happen if different types of information were attended to. Consider this thought experiment:

There is an item A, and another item B. A and B both contain sub-features A1...An, B1...Bn. When we attend to A or B, we are in fact binding A1...An, and/or B1...Bn, to represent A, B.

Now, we have limited bandwidth. Which means, we can only process a limited amount of information at any one time. Consider for the moment that we can only process 4 bits of information. So, if we attend to A, we only process A1-A4, and if we process B, we process B1-B4. We could, by way of divided attention, process A1,A2,B1,B4. Assuming that there is minimal cost in having to dissociate between two different groupings of features (which is rarely the case, but lets just assume that this is possible for argument's sake). This also means, we do not process the other information about the other features that are present.

Now, consider another type of processing, or rather, another level. If in fact we process something called A-B. That is, we bring the binding function up from the item level of A and B, to a higher representation that binds both A-B. What would this result in terms of the amount of information we can process at a time?

This is now only 1 bit of information. We would have more capacity left over (3 bits) from our initial 4 bits. Furthermore, within the 1 bit, we might be able to reinstate the original A1-A4, and B1-B4 via past experience. However, we will suffer from interference in this case, since we did not explicitly process A1-A4 or B1-B4, but rather A-B. Thus, there should be a cost of attending to this higher level at the expense of the lower levels. Likewise, there is a cost of attending to the lower levels at the expense of the higher levels. This is also known in the literature as chunking.

Thus, in summary is that the level of binding should be inversely related to the bandwidth, or the amount of information we can process at any one time.

What is consciousness = What am I?

I am a wave of existence/influence in the medium of space and time. I have a defined peak and full-width-at-half-maximum. I change the medium through my existence in it, as it, in turn, limits me.

Brain Calisthenics

New York Times article
Interesting article about "exercising" your brain.

Quality of representation: PhD Proposal

Here is an idea that I am toying with for my PhD research.

In aging, the idea is that neural representations change in several ways. Right now, we know there are changes, but we don't know what they are exactly, or why they happen the way they do. One obvious objective change we are pretty certain of is that, with age, processing speed slows down.

Now, is this a result of nerve conduction changes? Or is this more to do with changes in the processes themselves. That is, neuronal connectivity is changing, so the process computation changes as well, and changes in a way that results in slowing of the process. In addition, connectivity may also be changing in terms of a reduction in neural plasticity, the ability of neurons to form (or prune) connections based on experience.

One test I propose is related to ascertaining if the observations about neural changes with age relate to changes in processes vis a vis changes in connectivity. If neural representations are poorer with age, then it also means that they are less able to dissociate between similar representations. That is, aging reduces distinctiveness between neural representations. We should be able to measure this using the adaptation paradigm. In theory, if two representations are similar, the adaptation should be greater. To the extent that two representations are distinct, there will be less adaptation. Thus, with younger adults, there should be less adaptation to similar but different stimuli, or, there should be adaptation only when stimuli are very similar. In older adults, adaptation should happen at a lower threshold of similarity, for stimuli that are in fact quite distinct compared to that for the young.

In addition, since the neural representational quality in the lower perceptual areas feeds the cognitive processes that operate on them downstream (e.g. perceptual matching, target identification, memory, attention, decision-making etc) then it stands that if the neural representation is poorer, the cortex involved in working on these representations will either work harder to produce the same result, or be incapable of producing the same result if the representations were clearer. So, this could be measured as a correlation of frontal cortex activity with the degree of adaptation in the posterior, more perceptual areas.

Definition of consciousness

Consciousness: the state in which a cognitive system has or processes the knowledge or information that there is a distinction between the object of processing and the system implementing the process.

Cognitive Training

Two main findings emerge with research into the efficacy of cognitive training.

1. Training helps. Training improves behavioral performance in the task that people are trained in. This is evidenced in the Seattle Longitudinal Study and the Berlin Aging Study as studied by Baltes, Willis, Schaie, Lindenberger from the 70s to 90s, and even current day. Mostly, these studies train people in tasks involving spatial orientation and inductive reasoning. And they show that training in these specific domains leads to improvements post-test in that domain. This might explain why expertise exists. That is, why there are people around who are very good at what they do because they have so much experience doing it.

2. Training does not spill over to other domains as much. To be fair, there are some general transfers of learning, mostly within other tests that probe that trained domain. But across different domains, there seems to be not much transfer. Many have tried to investigate if training serves to improve general intelligence, and thus would logically lead to more transfer, since it is general. However, this has proven elusive. Most of the problem is because general intelligence itself is elusive, and is really still very much a modular thing. That is, there may not be a general intelligence at all, but specialties in cognitive abilities like spatial orientation, inductive reasoning, perceptual speed etc.

3. Perceptual speed does not improve with training. This is the general finding. However, not finding improvement with training does not mean that it is not possible. Perhaps there is a method of improving perceptual speed, but we have not found it yet.

4. These improvements with training are found even in aging!