We are rarely alone when learning something for the first time. We are social creatures, and whether it’s a new technology or an ancient tradition, we typically benefit from instruction when learning new tasks. This form of learning–in which a task is rapidly (within seconds) learned from instruction–can be referred to as rapid instructed task learning (RITL; pronounced “rittle”). Despite the fundamental role this kind of learning plays in our lives, it has been largely ignored by researchers until recently.

My Ph.D. dissertation investigated the evolutionary and neuroscientific basis of RITL.

RITL almost certainly played a tremendous role in shaping human evolution. The selective advantages of RITL for our species are clear: having RITL abilities allows us to partake in a giant web of knowledge shared with anyone willing to instruct us. We might have received instructions to avoid a dangerous animal we have never seen before (e.g., a large cat with a big mane), or instructions on how to make a spear and kill a lion with it. The possible scenarios in which RITL would have helped increase our chances of survival are virtually endless.

There are two basic forms of RITL. Read the rest of this entry »

When you type a search into Google it figures out the most important websites based in part on how many links each has from other websites. Taking up precious website space with a link is costly, making each additional link to a page a good indicator of importance.

We thought the same logic might apply to brain regions. Making a new brain connection (and keeping it) is metabolically and developmentally costly, suggesting that regions with many connections must be providing important enough functions to make those connections worth the sacrifice.

We developed two new metrics for quantifying the most connected—and therefore likely the most important—brain regions in a recently published study (Cole et al. (2010). Identifying the brain’s most globally connected regions, NeuroImage 49(4): 3132-3148).

We found that two large-scale brain networks were among the top 5% of globally connected regions using both metrics (see figure above). The cognitive control network (CCN) is involved in attention, working memory, decision-making and other important high-level cognitive processes (see Cole & Schneider, 2007). In contrast, the default-mode network (DMN) is typically anti-correlated with the CCN and is involved in mind-wandering, long-term memory retrieval, and self-reflection.

Needless to say, these networks have highly important roles! Without them we would have no sense of self-control (via the CCN) or even a sense of self to begin with (via the DMN).

However, there are other important functions (such as arousal, sleep regulation, breathing, etc.) that are not reflected here, most of which involve subcortical regions. These regions are known to project widely throughout the brain, so why aren’t they showing up?

It turns out that these subcortical regions only show up for one of the two metrics we used. This metric—unlike the other one—includes low-strength connections. Subcortical regions tend to be small and project weak connections all over the brain, such that only the metric including weak connections could identify them up.

I recently found out that this article received the 2010 NeuroImage Editor’s Choice Award (Methods section). I was somewhat surprised by this, since I thought there wasn’t much interest in the study. When I looked up the most popular articles at NeuroImage, however, I found out it was the 7th most downloaded article from January to May 2010. Hopefully this interest will lead to some innovative follow-ups to try to understand what makes these brain regions so special!

-MWCole

Figuring out how the brain decides between two options is difficult. This is especially true for the human brain, whose activity is typically accessible only via the small and occasionally distorted window provided by new imaging technologies (such as functional MRI (fMRI)).

In contrast, it is typically more accurate to observe monkey brains since the skull can be opened and brain activity recorded directly.

Despite this, if you were to look just at the human research, you would consider it a fact that the anterior cingulate cortex (ACC) increases its activity during response conflict. The thought is that this brain region detects that you are having trouble making decisions, and signals other brain regions to pay more attention.

If you were to only look at research with monkeys, however, you would think otherwise. No research with macaque monkeys (the ‘non-human primate’ typically used in neuroscience research) has found conflict activity in ACC.

My most recent publication looks at two possible explanations for this discrepancy: 1) Differences in methods used to study these two species, and 2) Fundamental evolutionary differences between the species.

Read the rest of this entry »

Thousands of brain imaging studies are published each year. A subset of these studies are replications, or slight variations, of previous studies. Attempting to come to a solid conclusion based on the complex brain activity patterns reported by all these replications can be daunting. Meta-analysis is one tool that has been used to make sense of it all.

Meta-analyses take locations of brain activity in published scientific papers and pool them together to see if there is any consistency.

This is typically done using a standardized brain that all the studies fit their data to (e.g., Talairach). Activation coordinates are then placed on a template brain as dots. When dots tend to clump together then the author can claim some consistency is present across studies. See the first figure for an example of this kind of result.

More sophisticated ways of doing this have emerged, however. One of these advanced methods is called activation likelihood estimation (ALE). This method was developed by Peter Terkeltaub et al. (in conjunction with Jason Chein and Julie Fiez) in 2002 and extended by Laird et al. in 2005.

ALE computes the probability of each part of the brain being active across studies. This is much more powerful than simple point-plotting because it takes much of the guess-work out of deciding if a result is consistent across studies or not.

Read the rest of this entry »

Keeping up with new findings is constantly becoming more difficult with the rate of publication in just cognitive neuroscience increasing by over 200 per year, with an overall increase of 2333 over the last ten years  (see figure below). I will briefly describe some methods I’ve recently discovered to help deal with this onslaught of new information.

I have found that using a combination of computer applications and websites can be effective for keeping up with science readings.

The websites are useful for searching and subscribing to syndicated (RSS) feeds. The applications are useful for organizing articles.

Websites for searching

Google Scholar
This website is extremely useful for exploring a comprehensive collection of research on a particular topic. It uses Google’s legendary indexing algorithms to make keyword searching a breeze, while browsing citation links can reveal a chain of publications on a topic. It’s also useful because citations can be quickly imported into programs like EndNote, and articles that are often unavailable on other websites are made available via Google’s indexing.

Scopus
Scopus is “the largest abstract and citation database of research literature and quality web sources”. It’s very useful for seeing all the papers that have cited a particular article, and all the papers that article has cited. Google Scholar also has this feature, but in my experience there are more false-positives than with Scopus. The consistent link, citation, and abstract-viewing interface makes Scopus often more effective than Google Scholar. Read the rest of this entry »

Topic 6: Causal Understanding

Causal understanding is an important part of human cognition.  How do we understand that a particular event or force has caused another event?  How do realize that inserting coins into a soda machine results in a cool beverage appearing below?  And ultimately, how do we understand people’s reactions to events?

The NSF workshop panel on the Grand Challenges of Mind and Brain highlighted the question of ‘causal understanding’ as their 6th research topic.   (This was the final topic in their report.)

In addition to studying causal understanding, it is probably just as important to study causal misunderstanding: that is, why do individuals infer the wrong causes for events?  Or incorrect results from causes? Studying the errors we make in causal inference and understanding may help us discover the underlying neural mechanisms.  

It probably isn’t too difficult to imagine that progress on causal understanding, and improvements in our ability to be correct about causation, will probably be important for the well-being of humanity.  But what kinds of experiments and methods could be used to human brain mechanisms of  causal understanding?

Read the rest of this entry »

Computational models that are implemented, i.e., written out as equations or software, are an increasingly important tool for the cognitive neuroscientist.  This is because implemented models are, effectively, hypotheses that have been worked out to the point where they make quantitative predictions about behavior and/or neural activity.

In earlier posts, we outlined two computational models of learning hypothesized to occur in various parts of the brain, i.e., Hebbian-like LTP (here and here) and error-correction learning (here and here). The computational model described in this post contains hypotheses about how we learn to make choices based on reward.

The goal of this post is to introduce a third type of learning: Reinforcement Learning (RL).  RL is hypothesized by a number of cognitive neuroscientists to be implemented by the basal ganglia/dopamine system.  It has become somewhat of a hot topic in Cognitive Neuroscience and received a lot of coverage at this past year’s Computational Cognitive Neuroscience Conference. Read the rest of this entry »

Cognitive neuroscience constantly works to find the appropriate level of description (or, in the case of computational modeling, implementation) for the topic being studied.  The goal of this post is to elaborate on this point a bit and then illustrate it with an interesting recent example from neurophysiology.

As neuroscientists, we can often  choose to talk about the brain at any of a number of levels: atoms/molecules, ion channels and other proteins, cell compartments, neurons, networks, columns, modules, systems, dynamic equations, and algorithms.

However, a description at too low a level might be too detailed, causing one to lose the forest for the trees.  Alternatively, a description at too high a level might miss valuable information and is less likely to generalize to different situations.

For example, one might theorize that cars work by propelling gases from their exhaust pipes.  Although this might be consistent with all of the observed data, by looking “under the hood” one would find evidence that this model of a car’s function is incorrect.

Read the rest of this entry »

I just got back from CNS a few days ago. I thought I’d write a quick summary of one of the more interesting symposia at the conference.

Taking place Monday (4/14) afternoon, The rise and fall of cognitive control: Lifespan development covered how executive brain functions develop and peak in the 20s and 30s, falling again toward the end of life.

The first talk, by Cindy Lustig, reported on a functional MRI study of 239 individuals ranging from 9 to 97 years of age. She found that the “default-network” brain activity (likely related to mind wandering) was better suppressed during difficult tasks early in life and decreased later in life. This suggests that difficulties older people have with hard tasks may originate in their poor ability to reduce background thoughts.

Adele Diamond gave the next talk, which focused on an impressive preschool program that improves cognitive control in children to help them with future school success. The program, called Tools of the Mind, is based on research showing that self-regulation (i.e., cognitive control) is very predictive of future academic success. The program successfully integrates with the children’s play, and Dr. Diamond’s research shows convincingly that it is able to improve cognitive control and subsequent school success. The above photo is of two children “playing” the program’s ‘Buddy Reading’ task, which promotes inhibition of inappropriate impulses using a reminder icon held by the child in the role of listener (on the right in the above photo).

The final talk, by Bradley Schlaggar of Washington University at St. Louis, described tracking changes in resting state connectivity with development. As presented by Steven Petersen at HBM 2007, Dr. Schlaggar showed how dorsal anterior cingulate changes its membership in networks over time. The idea of showing how regional membership in global networks can change with development is very exciting and will certainly lead to future insights into human developmental processes.

-MWCole

I recently watched this talk (below) by Joaquin Fuster. His theories provide a good integration of cortical functions and distributed processing in working and long-term memory. He also has some cool videos of likely network interactions across cortex (in real time) in his talk.

Here is a diagram of Dr. Fuster’s view of cortical hierarchies:

Joaquin Fuster’s talk:

Link to Joaquin Fuster’s talk [Google Video]

Here is an excerpt from Dr. Fuster’s amazing biography:
Read the rest of this entry »

Modern technologies allow eye movements to be used as a tool for studying language processing during tasks such as natural reading. Saccadic eye movements during reading turn out to be highly sensitive to a number of linguistic variables. A number of computational models of eye movement control have been developed to explain how these variables affect eye movements. Although these models have focused on relatively low-level cognitive, perceptual and motor variables, there has been a concerted effort in the past few years (spurred by psycholinguists) to extend these computational models to syntactic processing.

During a modeling symposium at ECEM2007 (the 14th European Conference on Eye Movements), Dr. Ronan Reilly presented a first attempt to take syntax into account in his eye-movement control model (GLENMORE; Reilly & Radach, Cognitive Systems Research, 2006). Read the rest of this entry »

Functional magnetic resonance imaging (fMRI), a method for safely measuring brain activity, has been around for about 15 years. Within the last 10 of those years a revolutionary, if mysterious, method has been developing using the technology. This method, resting state functional connectivity (rs-fcMRI), has recently gained popularity for its putative ability to measure how brain regions interact innately (outside of any particular task context).

Being able to measuring innate functional brain connectivity would allow us to know if a set of regions active during a particular task is, in fact, well connected enough generally to be considered a network. We could then predict what brain regions are likely to be active together in the future. This could, in turn, motivate us to look deeper at the nature of each brain region and how it contributes to the neuronal networks underlying our behavior.

Rs-fcMRI uses correlations of very slow fluctuations in fMRI signals (< 0.1 Hz) when participants are at rest to determine how regions are connected. The origin of these slow fluctuations has been unclear.

Some have argued that the thoughts and day dreams of participants “at rest” may explain the strong correlations typically found between brain regions. Recently, Vincent et al., 2007 sought to address this possibility using fMRI with anesthetized monkeys.

The idea is that if unconscious monkey brains show low-frequency correlated activity across known brain networks, then such findings in humans at conscious rest are likely not due to spurious thoughts, but something more innate. Read the rest of this entry »

Several months ago I posted The Will to be Free, Part I. In that post I explained that memory is the key to free will. However, this insight isn’t quite satisfactory. We need three additional things to complete the picture: the ability to choose based on predictions, internal desires, and self-awareness. (A quick disclaimer: These ideas are all extremely speculative. I’ll probably test most of them at some point, but right now I’m just putting them out there to hopefully allow for refinement of these hypotheses.) First, the ability to choose based on predictions. As mentioned last time, free will comes down to decision making. Specifically it comes down to our ability to make a decision based on internal sources (or at least condoned by them), rather than external coercive forces. If we cannot predict the outcome of our decision with any certainty, then decision making is pointless. For instance, if no matter what I choose to order at dinner a random dish is served then I had no freedom to choose in the first place. Thus, our ability to predict is necessary for free will. What are these “internal sources” involved in decision making that I mentioned earlier? They are the second new idea needed to complete our picture of free will: desires. Read the rest of this entry »

I recently published my first primary-author research study (Cole & Schneider, 2007).

The study used functional MRI to discover a network of brain regions responsible for conscious will (i.e., cognitive control). It also revealed the network’s specialized parts, which each uniquely contribute to creating the emergent property of conscious will.

I believe this research contributes substantially to our understanding of how we control our own thoughts and actions based on current goals. Much remains a mystery, but this study clearly shows the existence of a functionally integrated yet specialized network for cognitive control.

What is cognitive control? It is the set of brain processes necessary for goal-directed thought and action. Remembering a phone number before dialing requires cognitive control. Also, anything outside routine requires cognitive control (because it’s novel and/or conflicting with what you normally do). This includes, among other things, voluntarily shifting attention and making decisions.

What brain regions are involved? A mountain of evidence is accumulating that a common set of brain regions are involved in cognitive control. We looked for these regions specifically, and verified that they were active during our experiment [see top figure]. The brain regions are spread across the cortex, from the front to the back to either side. However, it’s not the whole brain: there are distinct parts that are involved in cognitive control and not other behavioral demands. Read the rest of this entry »

Here’s an exchange of emails between PL and MC on a recently published paper (Balleine et al., 2007).

Email 1 (from PL):
Have a look at this introductory paragraph from a recent (Aug 2007) J Neurosci article by Balleine, Delgado and Hikosaka. What do they mean by “cognition” here?

The Role of the Dorsal Striatum in Reward and Decision-Making
To choose appropriately between distinct courses of action requires the ability to integrate an estimate of the causal relationship between an action and its consequences, or outcome, with the value, or utility, of the outcome. Any attempt to base decision-making solely on cognition fails fully to determine action selection because any information, such as “action A leads to outcome O,” can be used both to perform A and to avoid performing A. It is interesting to note in this context that, although there is an extensive literature linking the cognitive control of executive functions specifically to the prefrontal cortex (Goldman-Rakic, 1995; Fuster, 2000), more recent studies suggest that these functions depend on reward-related circuitry linking prefrontal, premotor, and sensorimotor cortices with the striatum (Chang et al., 2002; Lauwereyns et al., 2002; Tanaka et al.,2006).

Email 2 (from MC):

It sounds like they are distinguishing cognition from reward processing. I’m not sure why, since ‘cognition’ typically encompasses reward processing now days.

The distinction I think they’re really trying to make is between cognitive control and reward processing. Given that, it’s still a ridiculous paragraph. Why must it be either cognitive control or reward processing? It’s likely (no, virtually certain!) that the two interact during reward-based decision making. For instance, O’Reilly’s stuff shows how this might happen.

Another problem with this paragraph: They equate causal knowledge with cognitive control. Well-known causal knowledge doesn’t involve cognitive control! For instance, routine decision making would involve lower perceptuo-motor circuits, and if it involved differential reward then reward circuits would be engaged as well. Cognitive control has little/no role here.

When cognitive control is involved it’s probably doing a lot more than just retrieving causal relations from semantic memory. For instance, perceptual decision making studies show that cognitive control is involved even in deciding what is being perceived when uncertainty arises.

I guess what they’re trying to do is show that cognitive control doesn’t explain all of decision making since there must be a reward component as well. Perhaps this is a good point to make; they just didn’t do it well.

Email 3 (from PL):
Ahhh, ok I think I see now what they’re trying to say.  It really just struck me as an excessively divisive statement to start out what appeared to be an interesting article.  Can you say “flamebait”?  Perhaps they’re trying to be provocative.

- PL & MC