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:

Link to Joaquin Fuster’s talk [Google Video]

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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