The 'connectome' of our cerebral cortex

Hagmann et al. use diffusion mapping techniques to provide some awesome summary graphics of connectivity networks of our cerebral cortex. Regions of the neocortex are linked by a dense network of neural pathways, with several distinct nodes, like airline hubs. Their data:

...provides evidence for the existence of a structural core in human cerebral cortex. This complex of densely connected regions in posterior medial and parietal cortex is both spatially and topologically central within the brain. Its anatomical correspondence with regions of high metabolic activity and with some elements of the human default network suggests that the core may be an important structural basis for shaping large-scale brain dynamics. The availability of single-participant structural and functional connection maps now provides the opportunity to investigate interparticipant connectional variability and to relate it to differences in individual functional connectivity and behavior.

Click on figure to enlarge...

As new kind of science, as data deluge makes the scientific method obsolete...

An article by Chris Anderson in Wired Magazine, pointed out to me by my son Jon, argues that science as we have known it has ended. The argument is that the quest for knowledge that used to begin with grand theories now, in the petabyte age, begins with massive amounts of data. Google has set the new model for science. I show some clips here, and then follow with the contra argument by John Timmers that follows):

Google conquered the advertising world with nothing more than applied mathematics. It didn't pretend to know anything about the culture and conventions of advertising — it just assumed that better data, with better analytical tools, would win the day. And Google was right...Google's founding philosophy is that we don't know why this page is better than that one: If the statistics of incoming links say it is, that's good enough. No semantic or causal analysis is required. That's why Google can translate languages without actually "knowing" them (given equal corpus data, Google can translate Klingon into Farsi as easily as it can translate French into German). And why it can match ads to content without any knowledge or assumptions about the ads or the content.

The hypothesize-model-test model of science is becoming obsolete...The models we were taught in school about "dominant" and "recessive" genes steering a strictly Mendelian process have turned out to be an even greater simplification of reality than Newton's laws. The discovery of gene-protein interactions and other aspects of epigenetics has challenged the view of DNA as destiny and even introduced evidence that environment can influence inheritable traits, something once considered a genetic impossibility...the more we learn about biology, the further we find ourselves from a model that can explain it...There is now a better way. Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

The best practical example of this is the shotgun gene sequencing by J. Craig Venter. Enabled by high-speed sequencers and supercomputers that statistically analyze the data they produce, Venter went from sequencing individual organisms to sequencing entire ecosystems. In 2003, he started sequencing much of the ocean, retracing the voyage of Captain Cook. And in 2005 he started sequencing the air. In the process, he discovered thousands of previously unknown species of bacteria and other life-forms.

Venter can make some guesses about the animals — that they convert sunlight into energy in a particular way, or that they descended from a common ancestor. But besides that, he has no better model of this species than Google has of your MySpace page. It's just data. By analyzing it with Google-quality computing resources, though, Venter has advanced biology more than anyone else of his generation.

This kind of thinking is poised to go mainstream. In February, the National Science Foundation announced the Cluster Exploratory, a program that funds research designed to run on a large-scale distributed computing platform developed by Google and IBM in conjunction with six pilot universities. The cluster will consist of 1,600 processors, several terabytes of memory, and hundreds of terabytes of storage, along with the software, including Google File System, IBM's Tivoli, and an open source version of Google's MapReduce. Early CluE projects will include simulations of the brain and the nervous system and other biological research that lies somewhere between wetware and software.

Here is the immediate rejoinder to this article from John Timmers at Ars Technica.

Every so often, someone (generally not a practicing scientist) suggests that it's time to replace science with something better. The desire often seems to be a product of either an exaggerated sense of the potential of new approaches, or a lack of understanding of what's actually going on in the world of science. This week's version, which comes courtesy of Chris Anderson, the Editor-in-Chief of Wired, manages to combine both of these features in suggesting that the advent of a cloud of scientific data may free us from the need to use the standard scientific method.

It's easy to see what has Anderson enthused. Modern scientific data sets are increasingly large, comprehensive, and electronic. Things like genome sequences tell us all there is to know about the DNA present in an organism's cells, while DNA chip experiments can determine every gene that's expressed by that cell. That data's also publicly available—out in the cloud, in the current parlance—and it's being mined successfully. That mining extends beyond traditional biological data, too, as projects like WikiProteins are also drawing on text-mining of the electronic scientific literature to suggest connections among biological activities.

There is a lot to like about these trends, and little reason not to be enthused about them. They hold the potential to suggest new avenues of research that scientists wouldn't have identified based on their own analysis of the data. But Anderson appears to take the position that the new research part of the equation has become superfluous; simply having a good algorithm that recognizes the correlation is enough.

The source of this flight of fancy was apparently a quote by Google's research director, who repurposed a cliché that most scientists are aware of: "All models are wrong, and increasingly you can succeed without them." And Google clearly has. It doesn't need to develop a theory as to why a given pattern of links can serve as an indication of valuable information; all it needs to know is that an algorithm that recognizes specific link patterns satisfies its users. Anderson's argument distills down to the suggestion that science can operate on the same level—mechanisms, models, and theories are all dispensable as long as something can pick the correlations out of masses of data.

Science 2.0 I can't possibly imagine how he comes to that conclusion. Correlations are a way of catching a scientist's attention, but the models and mechanisms that explain them are how we make the predictions that not only advance science, but generate practical applications. One only needs to look at a promising field that lacks a strong theoretical foundation—high-temperature superconductivity springs to mind—to see how badly the lack of a theory can impact progress. Put in more practical terms, would Anderson be willing to help test a drug that was based on a poorly understood correlation pulled out of a datamine? These days, we like our drugs to have known targets and mechanisms of action and, to get there, we need standard science.

Anderson does provide two examples that he feels support his position, but they actually appear to undercut it. He notes that we know quantum mechanics is wrong on some level, but have been unable to craft a replacement theory after decades of work. But he neglects to mention two key things: without the testable predictions made by the theory, we'll never be able to tell how precisely it is wrong and, in those decades where we've failed to find a replacement, the predictions of quantum mechanics have been used to create the modern electronics industry, with the data cloud being a consequence of that.

If anything, his second example is worse. We can now perform large-scale genetic surveys of the life present in remote environments, such as the far reaches of the Pacific. Doing so has informed us that there's a lot of unexplored biodiversity on the bacterial level; fragments of sequence hint at organisms we've never encountered under a microscope. But as Anderson himself notes, the only thing we can do is make a few guesses as to the properties of the organisms based on who their relatives are, an activity that actually requires a working scientific theory, namely evolution. To do more than that, we need to deploy models of metabolism and ecology against the bacteria themselves.

Overall, the foundation of the argument for a replacement for science is correct: the data cloud is changing science, and leaving us in many cases with a Google-level understanding of the connections between things. Where Anderson stumbles is in his conclusions about what this means for science. The fact is that we couldn't have even reached this Google-level understanding without the models and mechanisms that he suggests are doomed to irrelevance. But, more importantly, nobody, including Anderson himself if he had thought about it, should be happy with stopping at this level of understanding of the natural world.

MRI of mental time travel.

Arzy et al. make the interesting observation that one's imagined self location influences the neural activity related to mental time travel. Slightly edited clips from the article:

A fundamental characteristic of human conscious experience is the ability to not only experience the present moment but also to recall the past and predict the future, or to "travel" back and forth in time, a facility that is called "mental time travel" (MTT)...Converging evidence from recent memory research suggests that re-experiencing and pre-experiencing an event rely on similar neural mechanisms. Similar strategies and the same brain regions are found to be used in imagining past and future events, as future predictions may be based on past memories... when changing the location of one's self in time to past or future, one does not only recall and predict, but one also changes one's mental egocentric perspective on life events. Moreover, from these new self-locations in time, other life events might be regarded differently with respect to their relations to past or future. Thus, when imagining oneself as 10 years younger, last year's events are in the future (relative future) in relation to the initially imagined self-location in time, and vice versa (relative past).

Since earlier studies had shown behavioral and electrophysiological differences between judgments about one's own body while taken from one's actual spatial self-location versus different imagined self-locations, and given evidence that shared mechanisms process time and space in the brain, the authors developed a behavioral paradigm to determine if differences are found not only between different self-locations in time (past, now, and future), but also while imagining events in the relative past or the relative future. They followed neural correlates of MTT using behavioral measures, evoked potential (EP) mapping, and electrical neuroimaging in healthy adult participants.

Stimuli and procedure. The three different self-locations in time (past, now, and future) are shown. Participants were asked to mentally imagine themselves in one of these self-locations, and from these self-locations to judge whether different self or nonself events (e.g., top row) already happened (relative past, darker colors) or are yet to happen (relative future, lighter colors).

Their work confirmed that:

...that MTT is composed of two different cognitive processes: absolute MTT, which is the location of the self to different points in time (past, present, or future), and relative MTT, which is the location of one's self with respect to the experienced event (relative past and relative future). These processes recruit a network of brain areas in distinct time periods including the occipitotemporal, temporoparietal, and anteromedial temporal cortices. Our findings suggest that in addition to autobiographical memory processes, the cognitive mechanisms of MTT also involve mental imagery and self-location, and that relative MTT, but not absolute MTT, is more strongly directed to future prediction than to past recollection.

Generators of MTT map are localized to the right temporoparietal, occipitotemporal, and left anteromedial temporal cortices.

Discontinuity between human and nonhuman minds?

In a recent issue of Brain and Behavioral Science (BBS) Penn, Holyoak and Povinelli argue for a profound difference in kind, not degree, between human and animal minds. Their suggestions elicit mainly vigorous opposition as well as some support from an array of commentators. Several of the commentators point out evidence for flexible relational capabilities within a physical symbol system exhibited by dolphins and birds. As I read through the debate and its mind-numbing detail I give up on trying to convey a succinct summary, but here is their abstract. (You might compare this with the work of Hauser et al, that I mentioned in a previous post.):

Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as “one of degree and not of kind” (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate the higher-order, systematic, relational capabilities of a physical symbol system (PSS) (Newell 1980). We show that this symbolic-relational discontinuity pervades nearly every domain of cognition and runs much deeper than even the spectacular scaffolding provided by language or culture alone can explain. We propose a representational-level specification as to where human and nonhuman animals' abilities to approximate a PSS are similar and where they differ. We conclude by suggesting that recent symbolic-connectionist models of cognition shed new light on the mechanisms that underlie the gap between human and nonhuman minds.

Most popular consciousness papers...

For April 2008, from the ASSC archive:

1. Destrebecqz, Arnaud and Peigneux, Philippe (2005) Methods for studying
unconscious learning. In: Progress in Brain Research. Elsevier, pp. 69-80.
1968 downloads from 26 countries. http://eprints.assc.caltech.edu/170/
2. Koriat, A. (2006) Metacognition and Consciousness. In: Cambridge handbook
of consciousness. Cambridge University Press, New York, USA. 1799 downloads
from 29 countries. http://eprints.assc.caltech.edu/175/
3. Sagiv, Noam and Ward, Jamie (2006) Crossmodal interactions: lessons from
synesthesia. In: Visual Perception, Part 2 - Fundamentals of Awareness:
Multi-Sensory Integration and High-Order Perception. Progress in Brain
Research, Volume 155. Elsevier, pp. 259-271. 1089 downloads from 18
countries. http://eprints.assc.caltech.edu/224/
4. Chalmers, David J. (2004) How can we construct a science of
consciousness? In: The Cognitive Neurosciences III. MIT Press, Cambridge,
MA. 1009 downloads from 9 countries. http://eprints.assc.caltech.edu/28/
5. Dehaene, Stanislas and Changeux, Jean-Pierre and Naccache, Lionel and
Sackur, Jérôme and Sergent, Claire (2006) Conscious, preconscious, and
subliminal processing: a testable taxonomy. Trends in Cognitive Science, 10
(5). pp. 204-211. 900 downloads from 13 countries.
http://eprints.assc.caltech.edu/20/

The brain's default network - a review

Buckner et al. offer a review of work what our brains are doing when we are not focused on the external environment. This is an open access article in a new annual volume, "The Year in Cognitive Neuroscience," being initiated by the New York Academy of Sciences. (Table of contents of this first issue is here. ) I am passing on the abstract and one central figure and legend from the article:

Thirty years of brain imaging research has converged to define the brain's default network—a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment. Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system. Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others. Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems. The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations. These two subsystems converge on important nodes of integration including the posterior cingulate cortex. The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world. We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease.


The default network is activated by diverse forms of tasks that require mental simulation of alternative perspectives or imagined scenes. Four such examples from the literature illustrate the generality. (A) Autobiographical memory: subjects recount a specific, past event from memory. (B) Envisioning the future: cued with an item (e.g., dress), subjects imagine a specific future event involving that item. (C) Theory of mind: subjects answer questions that require them to conceive of the perspective (belief) of another person. (D) Moral decision making: subjects decide upon a personal moral dilemma. Note that all the studies activate strongly PCC/Rsp and dMPFC. Active regions also include those close to IPL and LTC, although further research will be required to determine the exact degree of anatomic overlap. It seems likely that these maps represent multiple, interacting subsystems.

The futurist: machines as smart as ourselves

John Tierney does a nice write up of the debate over the ideas of futurist Ray Kurzweil. (I've always thought that Kurzweil was simple proof of the proposition that if you propose any 10 crazy things, one of them will turn out to be right. People remember the correct prophesy, and forget the mistakes.) Still.... the guy has been right on a number of times. Here is part of the discussion of our cognitive/emotional repertoire being bested by machines ( (possibly piggybacked onto our biological hardware). This event is referred to as "the singularity." Kurzweil proposes that:

..by the 2020s we’ll be adding computers to our brains and building machines as smart as ourselves...This serene confidence is not shared by neuroscientists like Vilayanur S. Ramachandran, who discussed future brains with Dr. Kurzweil at the festival. It might be possible to create a thinking, empathetic machine, Dr. Ramachandran said, but it might prove too difficult to reverse-engineer the brain’s circuitry because it evolved so haphazardly...“My colleague Francis Crick used to say that God is a hacker, not an engineer,” Dr. Ramachandran said. “You can do reverse engineering, but you can’t do reverse hacking.”...Dr. Kurzweil’s predictions come under intense scrutiny in the engineering magazine IEEE Spectrum, which devotes its current issue to the Singularity. Some of the experts writing in the issue endorse Dr. Kurzweil’s belief that conscious, intelligent beings can be created, but most think it will take more than a few decades....He is accustomed to this sort of pessimism and readily acknowledges how complicated the brain is. But if experts in neurology and artificial intelligence (or solar energy or medicine) don’t buy his optimistic predictions, he says, that’s because exponential upward curves are so deceptively gradual at first.

“Scientists imagine they’ll keep working at the present pace,” he told me after his speech. “They make linear extrapolations from the past. When it took years to sequence the first 1 percent of the human genome, they worried they’d never finish, but they were right on schedule for an exponential curve. If you reach 1 percent and keep doubling your growth every year, you’ll hit 100 percent in just seven years.”

Dr. Kurzweil is so confident in these curves that he has made a $10,000 bet with Mitch Kapor, the creator of Lotus software. By 2029, Dr. Kurzweil wagers, a computer will pass the Turing Test by carrying on a conversation that is indistinguishable from a human’s.

You should also check out John Horgan's caustic comments on the whole singularity bit in a special IEEE spectrum feature, which ends with:

Let's face it. The singularity is a religious rather than a scientific vision. The science-fiction writer Ken MacLeod has dubbed it “the rapture for nerds,” an allusion to the end-time, when Jesus whisks the faithful to heaven and leaves us sinners behind.

Such yearning for transcendence, whether spiritual or technological, is all too understandable. Both as individuals and as a species, we face deadly serious problems, including terrorism, nuclear proliferation, overpopulation, poverty, famine, environmental degradation, climate change, resource depletion, and AIDS. Engineers and scientists should be helping us face the world's problems and find solutions to them, rather than indulging in escapist, pseudoscientific fantasies like the singularity.

Sarcasm and the right parahippocampal gyrus...

Getting inside someone's else's head to realize when they are ironic, sarcastic, or angry is one of our most advanced 'theory of mind' capabilities. You would expect the brain imaging people to show the frontotemporal lobe to light up when sarcasm is being detected, since one of the early signs of frontotemporal dementia is loss of the ability to detect sarcasm. Hurley describes the work of Rankin and others looking at brain correlates of being able to detect sarcasm based entirely on paralinguistic (non-verbal) cues (check out the link to the videos used).

...magnetic resonance scans revealed that the part of the brain lost among those who failed to perceive sarcasm was not in the left hemisphere of the brain, which specializes in language and social interactions, but in a part of the right hemisphere previously identified as important only to detecting contextual background changes in visual tests....The right parahippocampal gyrus must be involved in detecting more than just visual context — it perceives social context as well....The discovery fits with an increasingly nuanced view of the right hemisphere’s role...The left hemisphere does language in the narrow sense, understanding of individual words and sentences...But it’s now thought that the appreciation of humor and language that is not literal, puns and jokes, requires the right hemisphere.

So is it possible that Jon Stewart, who wields sarcasm like a machete on “The Daily Show,” has an unusually large right parahippocampal gyrus?..“His is probably just normal,” Dr. Rankin said. “The right parahippocampal gyrus is involved in detecting sarcasm, not being sarcastic...I bet Jon Stewart has a huge right frontal lobe; that’s where the sense of humor is detected on M.R.I.”...A spokesman for Mr. Stewart said he would have no comment — not that a big-shot television star like Jon Stewart would care about the size of his neuroanatomy.

Models of cognitive control in prefrontal cortex.

In the May issue of Trends in Cognitive Sciences David Badre reviews different models of the cognitive controls in our prefrontal cortex that support flexible behavior by selecting actions that are consistent with our goals and appropriate for our environment. I thought I would pass on two nice graphics from the papers, showing the structures and models involved. They do make the point that we have a long way to go before figuring out how the system works.

Figure (click to enlarge). Schematic of major anatomical sub-divisions in the frontal lobes. Boundaries and Brodmann areas (BA) are only approximate. Arrows indicate anatomical directions of anterior/rostral (front) versus posterior/caudal (back) and dorsal (up) versus ventral (down). From caudal to rostral, labeled areas include motor cortex, dorsal (PMd) and ventral premotor cortex, dorsal (pre-PMd) and ventral aspects of anterior premotor cortex, ventro- (VLPFC) and dorsolateral PFC (DLPFC), and lateral frontal polar cortex (FPC).


Figure: (Click to enlarge) Theoretical accounts of the rostro–caudal gradient in the PFC. (a) From a working memory perspective, rostral and caudal PFC can be distinguished on the basis of processing domain general versus specific representations. Hierarchical versions of this perspective propose that domain-specific posterior frontal regions can be modulated by the maintenance domain general rules in anterior DLPFC and FPC. (b) Relational complexity proposes a gradient in the PFC with respect to evaluation of simple stimulus properties, first-order relationships among the properties, and second-order relationships among relationships. (c) The cascade model proposes four levels of control that are distinguished by temporally disparate control signals, either sensory, context, episodic or branching. (d) Abstract representational hierarchy proposes that regions of the PFC are distinguished by the level of abstraction at which representations compete in a hierarchy of action representations.

Meeting George Bush versus Meeting Cinderella

The rest of the title of this article by von Cramon and Schubotz is "The Neural Response When Telling Apart What is Real from What is Fictional in the Context of Our Reality." Our ability to distinguish fact from fiction emerges early during our development, and by the age of 5, we not only differentiate reality from fiction but can also distinguish between different fictional worlds. The neural correlates underlying this ability are unknown. The authors obtain fMRI images showing significant difference in brain activity while processing real versus fictional conditions. The graphic is from the paper just to include a pretty picture, I'll spare you the details, because they really don't add all that much to the bottom line:

The processing of real and fictional scenarios activated a common set of regions including medial-temporal lobe structures. When the scenarios involved real people, brain regions associated with episodic memory retrieval and self-referential thinking, the anterior prefrontal cortex and the precuneus/posterior cingulate, were more active. In contrast, areas along the left lateral inferior frontal gyrus (shown in the graphic), associated with semantic memory retrieval, were implicated for scenarios with fictional characters. This implies that there is a fine distinction in the manner in which conceptual information concerning real persons in contrast to fictional characters is represented. In general terms, the findings suggest that fiction relative to reality tends to be represented in more factual terms, whereas our representations of reality relative to fiction are colored by personal subjectivity. What modulates our understanding of the relative difference between reality and fiction seems to be whether such character-type information is coded in self-relevant terms or not.

The authors note their agreement with the statement of William James: "In the relative sense, then, the sense in which we contrast reality with simple unreality, ... reality means simply relation to our emotional and active life

Most popular consciousness papers

Here is the list of the five most downloaded papers from the ASSC archive for April, 2008:

1. Destrebecqz, Arnaud and Peigneux, Philippe (2005) Methods for studying
unconscious learning. In: Progress in Brain Research. Elsevier, pp. 69-80.
1877 downloads from 23 countries. http://eprints.assc.caltech.edu/170/
2. Koriat, A. (2006) Metacognition and Consciousness. In: Cambridge handbook
of consciousness. Cambridge University Press, New York, USA. 1297 downloads
from 23 countries. http://eprints.assc.caltech.edu/175/
3. Dehaene, Stanislas and Changeux, Jean-Pierre and Naccache, Lionel and
Sackur, Jérôme and Sergent, Claire (2006) Conscious, preconscious, and
subliminal processing: a testable taxonomy. Trends in Cognitive Science, 10
(5). pp. 204-211. 880 downloads from 16 countries.
http://eprints.assc.caltech.edu/20/
4. Sagiv, Noam and Ward, Jamie (2006) Crossmodal interactions: lessons from
synesthesia. In: Visual Perception, Part 2 - Fundamentals of Awareness:
Multi-Sensory Integration and High-Order Perception. Progress in Brain
Research, Volume 155. Elsevier, pp. 259-271. 868 downloads from 13
countries. http://eprints.assc.caltech.edu/224/
5. Tsuchiya, Naotsugu and Koch, Christof (2005) Continuous flash suppression
reduces negative afterimages. Nature Neuroscience, 8 (8). pp. 1096-1101. 762
downloads from 13 countries. http://eprints.assc.caltech.edu/35/

MRI - the new phrenology

Having just done a posting on MRI, I thought it appropriate to point to a discussion by Michael Shermer in his "Skeptic" column in the Scientific American on the misuse and over-interpretation of MRI data.

It is a reminder that seeing scans with highlighted (usually in red) areas where your brain “lights up” when thinking about X (money, sex, God, and so on) should not seduce us into buying the Swiss Army knife model of the brain, with specialized modules for vision, language, facial recognition, cheating detection, risk taking, spirituality and even God. There is the minor problem of reversing the causal inference:

...where people see some activity in a brain area and then conclude that this part of the brain is where X happens. We can show that if I put you into a state of fear, your amygdala lights up, but that doesn’t mean that every time your amygdala lights up you are experiencing fear. Every brain area lights up under lots of different states. We just don’t have the data to tell us how selectively active an area is.

As Patricia Churchland points out:

Mental modules are complete nonsense. There are no modules that are encapsulated and just send information into a central processor. There are areas of specialization, yes, and networks maybe, but these are not always dedicated to a particular task.” Instead of mental module metaphors, let us use neural networks.

The Neural Buddhists

Check out the David Brooks OpEd piece with the title of this post. You really have to respect Brooks for putting so much energy into understanding contemporary mind science.

...the self is not a fixed entity but a dynamic process of relationships. Second, underneath the patina of different religions, people around the world have common moral intuitions. Third, people are equipped to experience the sacred, to have moments of elevated experience when they transcend boundaries and overflow with love. Fourth, God can best be conceived as the nature one experiences at those moments, the unknowable total of all there is...In their arguments with Christopher Hitchens and Richard Dawkins, the faithful have been defending the existence of God. That was the easy debate. The real challenge is going to come from people who feel the existence of the sacred, but who think that particular religions are just cultural artifacts built on top of universal human traits. It’s going to come from scientists whose beliefs overlap a bit with Buddhism...In unexpected ways, science and mysticism are joining hands and reinforcing each other. That’s bound to lead to new movements that emphasize self-transcendence but put little stock in divine law or revelation. Orthodox believers are going to have to defend particular doctrines and particular biblical teachings. They’re going to have to defend the idea of a personal God, and explain why specific theologies are true guides for behavior day to day. I’m not qualified to take sides, believe me. I’m just trying to anticipate which way the debate is headed. We’re in the middle of a scientific revolution. It’s going to have big cultural effects.

Our brains can choose our actions 10 sec before awareness

Here is an elegant update from Soon et al. of the continuing story that started with Libet's original observation that supplementary motor area (SMA) becomes active before our subjective sense of consciously willing an action. This work ignited a a long controversy as to whether subjectively 'free' decisions are determined by brain activity ahead of time. These new results go substantially further than those of previous studies by showing that the earliest predictive information is encoded in specific regions of frontopolar and parietal cortex, up to 10 seconds before it enters awareness (and not in SMA), presumably reflecting the operation of a network of high-level control areas that begin to prepare an upcoming decision. This preparatory time period in high-level control regions is considerably longer than that reported previously for motor-related brain regions.


Figure (click to enlarge) Color-coded brain areas show regions where the specific outcome of a motor decision could be decoded before (bottom, green) and after (top, red) it had been made. The graphs separately depict for each time point the accuracy with which the subject's free choice to press the left or right button could be decoded from the spatial pattern of brain activity in that region (solid line, left axis; filled symbols, significant at P < 0.05; open symbols, not significant; error bars, s.e.m.; chance level is 50%). As might be expected, the decoding accuracy was higher in cortical areas involved in the motor execution of the response than in areas shaping the upcoming decision before it reaches awareness (note the difference in scale). The vertical red line shows the earliest time at which the subjects became aware of their choices. The dashed (right) vertical line in each graph shows the onset of the next trial. The inset in the bottom left shows the representative spatial pattern of preference of the most discriminative searchlight position in frontopolar cortex for one subject (ant, anterior; sup, superior)

Mind Reading with fMRI

From the Nature Editor's summary:

Recent functional magnetic resonance imaging (fMRI) studies have shown that, based on patterns of activity evoked by different categories of visual images, it is possible to deduce simple features in the visual scene, or to which category it belongs. Kay et al. take this approach a tantalizing step further. Their newly developed decoding method, based on quantitative receptive field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas, can identify with high accuracy which specific natural image an observer saw, even for an image chosen at random from 1,000 distinct images. This prompts the thought that it may soon be possible to decode subjective perceptual experiences such as visual imagery and dreams, an idea previously restricted to the realm of science fiction.

The abstract from Kay et al., followed by one figure:

A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position, and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.


Figure Legend - The experiment consisted of two stages. In the first stage, model estimation, fMRI data were recorded while each subject viewed a large collection of natural images. These data were used to estimate a quantitative receptive-field model for each voxel. In the second stage, image identification, fMRI data were recorded while each subject viewed a collection of novel natural images. For each measurement of brain activity, we attempted to identify which specific image had been seen. This was accomplished by using the estimated receptive-field models to predict brain activity for a set of potential images and then selecting the image whose predicted activity most closely matches the measured activity.

The mind's eye in number space

From Loetscher et al., an interesting bit on how our subtle muscle movements correlate with counting operations - numbers and space:

Human subjects' answer to questions like “what number is halfway between 2 and 8” provides insights into spatial attention mechanisms involved in numerical processing. Here we show that mental numerical bisections are accompanied by a systematic pattern of horizontal eye movements: processing of a large number followed by a small number is accompanied with leftward eye movements, a tendency less pronounced or even reversed for the processing of a small number followed by a large number. The eyes thus appear to move along a left-to-right-oriented number line, indicating that shifts of attention in representational space are accompanied by an ocular motor orienting response. These results add to the growing evidence for a convergence of numerical processing, spatial attention, and movement planning in the parietal and frontal lobes. They also demonstrate the homologous relationship between our internal representations of numbers and space, and show that the concept of “number space” is more than a mere metaphor.

A hierarchy of temporal receptive windows in our brains

Here is the abstract from a fascinating study by Hasson et al. on how our visual system assembles time narratives - as during watching a movie - followed by part of one of the figures from the paper:

Real-world events unfold at different time scales and, therefore, cognitive and neuronal processes must likewise occur at different time scales. We present a novel procedure that identifies brain regions responsive to sensory information accumulated over different time scales. We measured functional magnetic resonance imaging activity while observers viewed silent films presented forward, backward, or piecewise-scrambled in time. Early visual areas (e.g., primary visual cortex and the motion-sensitive area MT+) exhibited high response reliability regardless of disruptions in temporal structure. In contrast, the reliability of responses in several higher brain areas, including the superior temporal sulcus (STS), precuneus, posterior lateral sulcus (LS), temporal parietal junction (TPJ), and frontal eye field (FEF), was affected by information accumulated over longer time scales. These regions showed highly reproducible responses for repeated forward, but not for backward or piecewise-scrambled presentations. Moreover, these regions exhibited marked differences in temporal characteristics, with LS, TPJ, and FEF responses depending on information accumulated over longer durations (~36 s) than STS and precuneus (~12 s). We conclude that, similar to the known cortical hierarchy of spatial receptive fields, there is a hierarchy of progressively longer temporal receptive windows in the human brain.


Figure- Maps are shown on inflated (top) and unfolded (bottom) left and right hemispheres. White outlines mark the main regions in which responses were not time reversible. Anatomical abbreviations: ITS, inferior temporal sulcus; LS, lateral sulcus; STS, superior temporal sulcus; TPJ, temporal parietal junction; CS, central sulcus; IPS, intraparietal sulcus. Several higher-order visual areas were functionally defined based on their responses to faces (red outlines), objects (blue outlines), and houses (green outlines). Functionally and anatomically defined cortical areas: V1, primary visual cortex; MT+, MT complex responsive to visual motion; PPA, parahippocampal place area; FFA, fusiform face area; LO, lateral occipital complex responsive to pictures of objects; STS-face, area in superior temporal sulcus responsive to faces.

Most popular consciousness articles for February

From the ASSC downloads archive:
1. Koriat, A. (2006) Metacognition and Consciousness. In: Cambridge handbook
of consciousness. Cambridge University Press, New York, USA.http://eprints.assc.caltech.edu/175/
2. Sagiv, N. and Ward, J. (2006) Crossmodal interactions: lessons from synesthesia. In: Visual Perception, Part 2. Progress in Brain Research,
Volume 155.http://eprints.assc.caltech.edu/224
3. Seth, A.K. and Baars, B.J. (2005) Neural Darwinism and Consciousness. Consciousness and Cognition, 14. pp. 140-168.http://eprints.assc.caltech.edu/163/
4. Dehaene, S., Changeux, J.-P., Naccache, L., Sackur, J. and Sergent, C. (2006) Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends in Cognitive Science, 10 (5). pp. 204-211.http://eprints.assc.caltech.edu/20/
5. Gennaro, R. J. (2007) Representationalism, peripheral awareness, and the transparency of experience. Philosophical Studies.http://eprints.assc.caltech.edu/218/

The maturing architecture of the brain's default network

From Raichle and others in the St. Louis group, an interesting story on the development of the brain network we most likely use for introspective mental activity:

In recent years, the brain's "default network," a set of regions characterized by decreased neural activity during goal-oriented tasks, has generated a significant amount of interest, as well as controversy. Much of the discussion has focused on the relationship of these regions to a "default mode" of brain function. In early studies, investigators suggested that, the brain's default mode supports "self-referential" or "introspective" mental activity. Subsequently, regions of the default network have been more specifically related to the "internal narrative," the "autobiographical self," "stimulus independent thought," "mentalizing," and most recently "self-projection." However, the extant literature on the function of the default network is limited to adults, i.e., after the system has reached maturity. We hypothesized that further insight into the network's functioning could be achieved by characterizing its development. In the current study, we used resting-state functional connectivity MRI (rs-fcMRI) to characterize the development of the brain's default network. We found that the default regions are only sparsely functionally connected at early school age (7–9 years old); over development, these regions integrate into a cohesive, interconnected network.


Figure legend - (click on figure to enlarge). Voxelwise resting-state functional connectivity maps for a seed region (solid black circle) in mPFC (ventral: –3, 39, –2). (A) Qualitatively, the rs-fcMRI map for the mPFC (ventral) seed region reveals the commonly observed adult connectivity pattern of the default network. The connectivity map in children, however, significantly deviates from that of the adults. Functional connections with regions in the posterior cingulate and lateral parietal regions (highlighted with blue open circles) are present in the adults but absent in children. (B) These qualitative differences between children and adults are confirmed by the direct comparison (random effects) between adults and children. mPFC (ventral) functional connections with the posterior cingulate and lateral parietal regions are significantly stronger in adults than children.

Awaress and attention: different brain processes

Most of the proposed neural correlates of visual awareness do not explicitly distinguish top-down attention from awareness per se. However, several authors have started to point at the need to disambiguate visual awareness and spatial attention. Experimental evidence supporting their possible neural dissociation has remained sparse. Such evidence is now provided by a nice piece of work from Wyart and Tallon-Baudry:

To what extent does what we consciously see depend on where we attend to? Psychologists have long stressed the tight relationship between visual awareness and spatial attention at the behavioral level. However, the amount of overlap between their neural correlates remains a matter of debate. We recorded magnetoencephalographic signals while human subjects attended toward or away from faint stimuli that were reported as consciously seen only half of the time. Visually identical stimuli could thus be attended or not and consciously seen or not. Although attended stimuli were consciously seen slightly more often than unattended ones, the factorial analysis of stimulus-induced oscillatory brain activity revealed distinct and independent neural correlates of visual awareness and spatial attention at different frequencies in the gamma range (30–150 Hz). Whether attended or not, consciously seen stimuli induced increased mid-frequency gamma-band activity over the contralateral visual cortex, whereas spatial attention modulated high-frequency gamma-band activity in response to both consciously seen and unseen stimuli. A parametric analysis of the data at the single-trial level confirmed that the awareness-related mid-frequency activity drove the seen–unseen decision but also revealed a small influence of the attention-related high-frequency activity on the decision. These results suggest that subjective visual experience is shaped by the cumulative contribution of two processes operating independently at the neural level, one reflecting visual awareness per se and the other reflecting spatial attention.