Learning and Memory: location, location, location!

Not long ago, we knew very little about learning and memory. Sure, we understood the basic concepts– people could learn things, and then recall them later– but they were black box processes, obfuscated by the complexity of the brain. Everyone knew what they were, but nobody could explain how they worked. We just sort of threw information at people and hoped it stuck.

Fast forward to 2010, and though we’re getting better at the practicalities of learning and we can roughly describe the contours of the process, we still don’t really understand the internal mechanics of learning and memory to any fundamentally satisfying degree. These internal mechanics are still mysteries, of which we have no large-scale, generally predictive models. Ask a neuroscientist to explain exactly how you remember where you parked your car, and you’ll get a convoluted answer involving mnemonic association, episodic memory, grid cells, the cerebral cortex, and a few other things in our current neuro-ontology. The description you get may be true as far as it goes, but certainly not satisfying, as our descriptions mostly involve hand-waving at major concepts we think are important, rather than telling a specifically predictive story.
But we’re getting closer. We do know a lot more than we did.
What is Learning?
We’ve made a great deal of progress dissecting the fundamental nature of learning. The organizing principle of our current understanding of learning is, with a nod to Pavlov, “neurons that fire together, wire together.” That is, the body’s neural wiring algorithms assume that neurons and neural networks which are often activated at the same time are likely connected, and form connections between them. This simple strategy, applied with great nuance and within the diverse and hierarchical structures of the brain, helps the brain find, internalize, and leverage patterns and drives the translation of conscious processes into subconscious aptitudes and habits of body and mind.
The Structure of Memory
A great deal of effort has been put into exploring the structures of memory. The result has been a set of fairly workable models, which have had some practical success at bringing various pathologies under the umbrella of theory and conforms well with folk psychology and the results of many thousands of memory experiments. They don’t handle all the edge cases well, and in many contexts they’re more descriptive than predictive, but they’re pretty good, as far as they go.
Perhaps the crown jewel of the consensus model is that our memory is more-or-less divided into short-term memory and long-term memory. Short-term memory is essentially our capacity to store static information in our brain without engaging the machinery of medium- and long-term memory. Many experiments peg the capacity as limited to 7 +-2 items; this can vary depending on their complexity, similarity, mnemonic strategies in use, familiarity, and the person in question[1,2], but at any rate, it’s a very finite quantity. Most of the stuff that passes through short-term memory is ultimately lost (or severely compressed): people keep stuff there until they don’t need it, then it’s gone.
Long-term memory, on the other hand, is where stuff in your short-term memory goes if your brain’s heuristics decide it’s worth keeping. To drastically simplify things, if your brain decides something’s important, it’ll send it to the hippocampus, then during sleep your brain processes, consolidates, and compresses what’s in the hippocampus, then sends it off to relevant parts of the brain for long-term storage. Much of it ends up in the cerebral cortex, where it’s more-or-less organized into ordered and linked lists. We know these lists have a directional preference, which is why it’s so difficult to say the alphabet backward (your brain needs to make a new list for it).
The Ephemeral Quality of Short-term Memory
A key finding in recent research is the plastic nature of short-term memory: there’s a window of time before they’re are translated into long-term storage where memories behave like putty and accessing a memory will change it. During this window, when we recall something it can easily be reinforced, altered, or destroyed… depending primarily what else is going on inside and around us and if we’re interrupted while recalling it. (The brain doesn’t always have a reliable autosave function.)
This seems odd– we think of memories as timeless records which may fade with age but are inherently stable. But the science does not back this up– and given that we don’t have memories of our memories, who are we to gainsay it? It appears that it can take between ~1-3 nights of sleep to consolidate a memory into long-term storage, hardening the proverbial putty of short-term memory into a more lasting form.
… and of Long-term memory
After memories are consolidated into long-term memory, they’re not ephemeral– but neither are they permanent, or even particularly stable. It appears that memories are similar to library books: you can ‘check them out’ from the recesses of your brain and use them, but if you alter the memory when it’s in short-term storage, those changes get ‘checked back in’ and change the original. If you think about a given memory often, you are changing it, for better or worse.
The Limits of our Memory Models
The consensus model has less to say about how the brain classifies and integrates different types of information into memory. We’ve established that different brain regions are strongly associated with certain functions, and it’s certain there’s some sort of elegant sorting mechanism the brain uses to direct information to appropriate regions. But we don’t really have ontologically firm concepts with which to speak about how the brain does sorting or (to some extent) classification. That said, a key result in recent years has been the identification of function-specific brain structures, such as grid and place cells. These functional structures are where a lot of the hottest research is happening, since their bounded contexts are accessible to experimentation and reductionism. Clearly, location and episodic memory must use grid cells in some fashion; clearly, mirror neurons must be deeply relevant to muscle memory and social learning. We just don’t know exactly how yet.
Unfortunately, if we push them hard in any specific direction our models of memory start to look like cardboard cutouts (much like memories themselves- but we digress). They’re wonderful guides to what’s roughly going on, but they don’t have a great deal of depth or precision. If we apply Karl Popper’s evaluative lens that ‘inherent in any good explanation is a prediction, and inherent in any good prediction is an explanation’, we find our models of memory rather constrained: they’re not particularly specifically predictive over most of human experience.
They’re also much more detailed in some areas than in others. We know the limit of short-term memory, for instance, with much more clarity than we know the details of how the hippocampus works or even how information gets recalled once stored.
The Future of Memory Research: Models and Measurement
The limits of any science emerge from what it can and can’t measure, and neuroscience is no exception. We have lots of phenomenological information, which is helpful, but the things we’re having trouble measuring include:
1. being able to tag and track information as it travels through the brain;
2. better quantifying how the brain splits information into chunks and ties them together;
3. measuring how different parts of the brain change information that passes through them (and likewise, how information changes the parts of the brain it passes through);
4. extracting deep functional data from activity scans;
5. designing roughly predictive digital models of brain subsections (other than certain exceptions like the cerebellum).
Progress in any of these areas would drive progress in the others. The productive frontiers in this seem to include:
– improving and melding many sorts of brain scans together. The golden standard today for functional research is fMRI; the next gold standard will be an composite of e.g., high-tesla fMRI, PET scans for gene expression data, EEG and MEG for better temporal resolution, etc.
– better identifying the computational principles which fit the contours of various brain activities (as we’ve done somewhat with memory structure in the cerebral cortex);
– better reverse-engineering the algorithmic approaches taken by brain circuitry (as we’ve done with the visual and auditory cortexes);
– charting out the ‘circuit diagram’ of brain subsections (as we’ve done with the cerebellum);
– simulating the brain.
Reverse-engineering and simulating the brain is a huge topic, one which I’ll cover in another post. Basically though, once we have high-quality neural simulations which allow us to tag and track information as it travels through a virtual brain we may be able to move from a fragmented understanding of memory to something more emergent, experimental, and predictive.
A Modest Proposal
So that’s the current story on learning.
What I want to talk about specifically is something that’s not in the current story. An implicit assumption running through this current consensus is that physical location of where peoples’ brains happen to store information doesn’t matter– in other words, 1. there is very little variability in *where* similar sets of information get encoded in peoples’ brains, and/or 2. when differences occur there are only trivial functional implications.
I think these assumptions will be shown to be significantly false, and if we look underneath them there’s a whole new realm of study waiting to be unlocked. In a nutshell, I’m arguing three things:
1. The regional localization of learned information can vary;
2. Regional localization of learned information commonly varies between individuals and learning approaches;
3. Differences in regional localization of learned information have practical significance in cognition and behavior.
I can solidly support (1): aside from the obvious example of right-brain-vs-left-brain lateralization of function, there are many examples of hemispherectomy– the physical removal of half of the brain– where patients fully recovered and exhibited no mental deficits.[3] The two significant variables seemed to be age and the speed of degeneration: young people did much better than old, and people who had a slow degenerative disease and thus gave their brains time to migrate information and function away from the diseased hemisphere did much better than those with quicker illnesses.
(2) is more arguable. We simply don’t have good ways to measure where people localize learned information. A lot of people who study the brain take localization invariance for granted– but once we get the technology, experiments on e.g., tracking information storage and retrieval in musicians and non-musicians where each are taught a song could be interesting. Differences in localization might arise from differences in aptitudes, genetics, some environmental cues, or just randomness.
(3) is still somewhat ambiguous, but I can appeal to the considerable functional significance of the brain’s computational topology, and some work on right vs left hemisphere specializations.[4]
The goal of this suggestion is to help us better quantify different ways of knowing, and to ground this in a functionally-predictive context.
What could cause information to be encoded in one region and not another? How could this guide our behavior and/or treatments? It’s hard to say (yet).
Further Musings:
– A closely related issue is topological constraints on information linkages, where the brain is physically limited from connect any arbitrary node of information to any other node. Consider, e.g., two nodes that are in the same region but 2cm away, and the regional neuronal configuration hinders attempts at making a strong connection. How functionally significant are these sorts of topological limitations? Are they responsible for mental blocks at the level of our experience, like not being able to connect two concepts together very well? Do such intra-regional topologies vary in interestingly distinct ways across individuals?
– I have phrased this in terms of differences in “regional localizations”. We can perhaps break this down into
   – which brain region information gets encoded into;
   – which part of each brain region information gets encoded into;
   – what the intra-region encoding patterns are.
I don’t think we know enough to estimate the relative contributions of each. But they all point toward the central concept I’m trying to convey, that the topology of information localization differs significantly between people and that this has functional implications.
Edit, 10-3-10: Research into the learning process is really moving quite fast. Recommended links:
Easier Way To Do Perceptual Learning: “20 minutes of training followed by 20 minutes of listening to a musical tone was just as effective as 40 minutes of training.”
Forget What You Know About Good Study Habits: “psychologists have discovered that some of the most hallowed advice on study habits is flat wrong.”
Again, we’re only able to see the outward-facing phenomena of learning and memory, not the internal mechanisms. But even this stuff is really interesting.

Edit, 10-28-10: Esquire has a particularly readable piece about how modern neuroscience research got its start. The point it makes is that, historically, we’ve been able to decipher basic brain region function by looking at what happens when that region gets damaged, through injury or surgery.

In 1848, an explosion drives a steel tamping bar through the skull of a twenty-five-year-old railroad foreman named Phineas Gage, obliterating a portion of his frontal lobes. He recovers, and seems to possess all his earlier faculties, with one exception: The formerly mild-mannered Gage is now something of a hellion, an impulsive shit-starter. Ipso facto, the frontal lobes must play some function in regulating and restraining our more animalistic instincts.

In 1861, a French neurosurgeon named Pierre-Paul Broca announces that he has found the root of speech articulation in the brain. He bases his discovery on a patient of his, a man with damage to the left hemisphere of his inferior frontal lobe. The man comes to be known as “Monsieur Tan,” because, though he can understand what people say, “tan” is the only syllable he is capable of pronouncing.

Thirteen years later, Carl Wernicke, a German neurologist, describes a patient with damage to his posterior left temporal lobe, a man who speaks fluently but completely nonsensically, unable to form a logical sentence or understand the sentences of others. If “Broca’s area,” as the damaged part of Monsieur Tan’s brain came to be known, was responsible for speech articulation, then “Wernicke’s area” must be responsible for language comprehension.

And so it goes. The broken illuminate the unbroken.

Edit, 5-25-11: There’s been some interesting research on using brain stimulation to aid learning: essentially using tiny amounts of electricity to induce changes in rats’ brains that makethem better learners. After the current is shut off, the rats’ brains go back to normal but they keep their learned skills. We don’t know what the specific trade-offs may be, but between this approach and approaches which could mimic developmental neuroplasticity triggers, we may have the basis for a very desirable form of cognitive enhancement.

Here’s “Scienceblog” on the a theory on how the brain picks which of its neural networks to use for a new skill:

The study by Reed and colleagues supports a theory that large-scale brain changes are not directly responsible for learning, but accelerate learning by creating an expanded pool of neurons from which the brain can select the most efficient, small “network” to accomplish the new skill.

This new view of the brain can be compared to an economy or an ecosystem, rather than a computer, Reed said. Computer networks are designed by engineers and operate using a finite set of rules and solutions to solve problems. The brain, like other natural systems, works by trial and error.

The first step of learning is to create a large set of diverse neurons that are activated by doing the new skill. The second step is to identify a small subset of neurons that can accomplish the necessary computation and return the rest of the neurons to their previous state, so they can be used to learn the next new skill.

By the end of a long period of training, skilled performance is accomplished by small numbers of specialized neurons not by large-scale reorganization of the brain. This research helps explain how brains can learn new skills without interfering with earlier learning.

Edit, 7-28-11: Scientists have traced the recall of a specific memory and found it partially activates other memories from around the same time. Unsurprising, given it’s common to experience memories as strongly linked, but still good science, and perhaps it supports the viewpoint that all memory is ultimately episodic in some real sense.

Researchers have long known that the brain links all kinds of new facts, related or not, when they are learned about the same time. Just as the taste of a cookie and tea can start a cascade of childhood memories, as in Proust, so a recalled bit of history homework can bring to mind a math problem — or a new dessert — from that same night.

For the first time, scientists have recorded traces in the brain of that kind of contextual memory, the ever-shifting kaleidoscope of thoughts and emotions that surrounds every piece of newly learned information. The recordings, taken from the brains of people awaiting surgery for epilepsy, suggest that new memories of even abstract facts — an Italian verb, for example — are encoded in a brain-cell firing sequence that also contains information about what else was happening during and just before the memory was formed, whether a tropical daydream or frustration with the Mets.

The new study suggests that memory is like a streaming video that is bookmarked, both consciously and subconsciously, by facts, scenes, characters and thoughts.

“When you activate one memory, you are reactivating a little bit of what was happening around the time the memory was formed,” Dr. Kahana said[.]


3 thoughts on “Learning and Memory: location, location, location!

  1. A great read. My best estimate so far is that information is present in the activity and structure of the brain in the form of the causes and effects of one area (of any size) on another. On a moment of reflection this seems true of information generally, but is still useful to think about. To ease my terminology, I'll explicitly assume for the moment that relevant mental processing centers and information are largely encoded in particular clusters of neurons, which may or may not be spatially clustered.

    To elaborate by way of example, suppose we have a memory which includes a visual component. It seems that when that memory is activated, it would in turn activate something in visual centers (information through effect). What does it activate there, what does this tell us? This in turn can be interpreted by the other sorts of experiences or memories that activate that subsection, or activate the visual center in that pattern (information through cause).

    If we can establish with good confidence a way in which to interpret activation in particular regions, this allows us to better interpret activation of clusters that feed into those regions. Plausibly, this might be easiest to start on with sensory areas, and indeed, could be the explanation of why we have so much more information on what is going on in those areas: we can correlate external, interpretable events with firing patterns in space and time. Sufficiently short causal chains from external experience to activation can explain the much tight correlations we see there, as opposed to the frontal cortex.

    A great jump would be going from correlating activity in areas with outside events, to correlating activity in areas to other areas that are already well understood. Assuming the process of recognizing concepts and ideas works as such, we could then interpret firing in say, the frontal lobe, by the activation of clusters we've already determined recognize 'cat' and 'milk'.

    At each step of this process, we can them jump to interpreting activity in a new area that feeds into the recently understood region. We also would gain better information about areas we've already understood fairly well, as clusters could activate "lower level" clusters as well. There will be surprises of course, reversing some of our earlier interpretations, but I expect there's a way to get enough good information to start the process.

    One difficulties is that, through differing genes and certainly through differing experiences, much (or ~all) of the clusters and organizations will be particular to a given person; this would have to be done for each person you sought to understand. Secondly, the brain would be changing as you undertook this study, so some conclusions could hold less true over time; you would want to work somewhat quickly.

  2. Very interesting– we seem to have thought along much the same lines. I'll email you an outline for a system to do exactly what you describe, though on a somewhat macro, rough level.

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