Anterior prefrontal cortex: A key to the human brain’s ability to rapidly learn new tasks

Humans have come to dominate the world largely due to our intelligence relative to other species. Where does this tremendous cognitive flexibility come from?

Many have considered prefrontal cortex as the origin of this boost in intelligence, yet it has remained unclear how this chunk of brain tissue could drive intelligent behavior. In a recently published study we used fMRI (functional magnetic resonance imaging) along with advanced machine learning approaches to read out what information is processed in prefrontal cortex during intelligent behavior.

We focused on one of the most remarkable abilities indicative of human intelligence: rapid instructed task learning (RITL). This is the ability to quickly learn new tasks from instructions. For instance, we use it to learn to use new technology (e.g., a new smartphone) and when learning to play a new game (e.g., Monopoly).

This ability may seem mundane because it’s familiar, but it’s actually quite remarkable both computationally and in terms of evolution. Computationally it’s remarkable because even modern computers can’t match the RITL abilities of human children. Evolutionarily it’s remarkable because other primates take much longer to learn many tasks that we can perform immediately upon hearing instructions.

We have investigated RITL in the past. What’s different here is that we were able to tie what information is in prefrontal cortex (instead of just how active it is) to how accurate a person is on a given trial. In other words, we were able to see the information processing in prefrontal cortex that drives successfully intelligent task performance .

Region Figure

Figure 1

We started out finding the brain regions (regions of interest; ROI) that were more responsive to novel that practice tasks. This helped us isolate RITL from general task processing. We found a variety of prefrontal areas that were preferentially involved in novel relative to practiced task performance (Figure 1).

We wanted to test whether any of these brain regions were especially important for RITL behavior. We therefore tested if there were any regions that also 1) represented task information and 2) were sensitive to task accuracy. We found three brain regions that showed all of these properties. Surprisingly, all three regions were in left anterior prefrontal cortex (regions 1, 4, & 8 in Figure 1).

Resting-state network partitions

Figure 2

Given how close these regions were to each other, we thought they might actually be the same large brain region. When we checked their overlap with brain connectivity patterns, however, we saw that they are each part of a different network (Figure 2).

This suggests there are three nearby regions in left anterior prefrontal cortex that play an especially important role in flexible and intelligent behavior in humans.

See the full scientific article for more info.

-MWCole

The evolutionary importance of rapid instructed task learning (RITL)

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.

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Cingulate Cortex and the Evolution of Human Uniqueness

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.

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Joaquin Fuster on Cortical Dynamics

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:

The Google Video link is broken, but you can see a related talk by Fuster here:
https://archive.org/details/Brain_Network_Dynamics_2007-13-Joaquin_Fuster

 

Here is an excerpt from Dr. Fuster’s amazing biography:

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Measuring Innate Functional Brain Connectivity

 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.

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