In the previous post, Decision-making 101, I provided
evidence that selective attention to items that were retrieved into working
memory were a major factor in making good decisions. This has generally
unrecognized educational significance. Rarely is instructional material
packaged with foreknowledge of how it can be optimized in terms of reducing the
working memory cognitive load. New research from a cognitive neuroscience group
in the U.K. is demonstrating the particular importance this has for learning
how to correctly categorize new learning material. They show that learning is
more effective when the instruction is optimized ("idealized" in
their terminology).
Decisions often require categorizing novel stimuli, such as
normal/abnormal, friend/foe, helpful/harmful, right/wrong or even assignment to
one of multiple category options. Teaching students how to make correct
category assignments is typically based on showing them examples for each
category. Categorization issues routinely arise when learning is tested. For
example, the common multiple-choice testing in schools requires that a decision
be made on each potential answer as right or wrong.
In reviewing the literature on optimizing training, these
investigators found reports that one approach that works is to present training
in a specific order. For example, in teaching students how to classify by
category, people perform better when a number of examples from one category are
presented together followed by a number of contrasting examples from the other
category. Other ordering manipulations are learned better if simple, unambiguous
cases in either category are presented together early in training, while the
harder, more confusing cases are presented afterwards. Such training
strengthens the contrast between the two categories.
The British group has focused on the role of working memory
in learning. Their idea is that ambiguity during learning is a problem. In
real-world situations that require correct category identification, naturally
occurring ambiguities make correct decisions difficult. Think of these
ambiguities as cognitive "noise" that interferes with the training
that is recalled into working memory. This noise clutters the encoding during
learning and clutters the thinking process and impairs the rigorous thought
processes that may be needed to make a correct distinction. In the real world
of youngsters in school, other major cognitive noise sources are the
task-irrelevant stimuli that come from multi-tasking habits so common in
today's students.
The theory is that when performing a learned task, the
student recalls what has been taught into working memory. Working memory has
very limited capacity, so any "noise" associated with the initial
learning may be incompletely encoded and the remembered noise may also
complicate the thinking required to perform correctly. Thus, simplifying
learning material should reduce remembered ambiguities, lower the working
memory load, and enable better reasoning and test performance.
One example of optimizing learning is the study by Hornsby
and Love (2014) who applied the concept to training people with no prior
medical training to decide whether a given mammogram was normal or cancerous. They
hypothesized that learning would be more efficient if students were trained on
mammograms that were easily identified as normal or cancerous, and did not
include examples where the distinction was not so obvious. The underlying
premise is that decision-making involves recalling past remembered examples into
working memory and accumulating the evidence for the appropriate category. If the remembered items are noisy (i.e.
ambiguous) the noise also accumulates and makes the decision more difficult.
Thus, learners will have more difficulty if they are trained on examples across
the whole range of possibilities from clearly evident to obscure than if they
were separately trained on examples that were clearly evident as belong into
one category or another.
Initially a group of learners was trained on a full-range mixture
of mammograms so the images could be classified by diagnostic difficulty as
easy or hard or in between. On each trial, three mammograms were shown: the
left image was normal, the right was cancerous, and the middle was the test
item requiring a diagnosis of whether it was normal or cancerous.
In the actual experiment, one student group was trained to
classify a representative set of easy, medium, and hard images, while the other
group was trained only on easy samples. During training trials, learners looked
at the three mammograms, stated their diagnosis for the middle image, and were
then given feedback as to whether they were right or wrong. After completing
all 324 training trials, participants completed 18 test trials, which consisted
of three previously unseen easy, medium and hard items from each category
displayed in a random order. Test trials followed the same procedure as
training trials.
When both groups were tested on samples across the range in
both conditions, the optimized group was better able to distinguish normal from
cancerous mammograms in both the easy and medium images. Note that the
optimized group was not trained on medium images. However, no advantage was
found in the case of hard test items; both groups made many errors on the hard
cases, and optimized training yielded poorer results than regular training.
We need to explain why this strategy does not seem to work
on hard cases. I suspect that in easy and medium cases, not much understanding
is required. It is just a matter of pattern recognition, made easier because
the training was more straightforward and less ambiguous. The learner is just
making casual visual associations. For hard cases, a learner must know and
understand the criteria needed to make distinctions. The subtle differences go
unrealized if diagnostic criteria are not made explicit in the training. In
actual medical practice, many mammograms actually cannot be distinguished by
visual inspection—they really are hard. Other diagnostic tests are needed.
The basic premise of such research is that learning objects
or task should be pared down to the basics, eliminating extraneous and
ambiguous information, which constitute “noise” that confounds the ability to
make correct categorizations.
In common learning situations, a major source of noise is
extraneous information, such as marginally relevant detail. Reducing this noise
is achieved by focus on the underlying principle. Actually I stumbled on this
basic premise of simplification over 50 years ago when I was a student trying
to optimize my own learning. What I realized was the importance of homing in on
the basic principle of what I was trying to learn from instructional material.
If I understood a principle, I could use that understanding to think through to
many of the implications and applications.
In other words, the principle is: "don't memorize any
more than you have to." Use the principles as a way to figure out what was
not memorized. Once core principles are understood, much of the basic
information can be deduced or easily learned. This is akin to the standard
practice of moving from the general to the specific. Even so, general ideas
should emphasize principles.
Textbooks are sometimes quite poor in this regard. Too many
texts have so much ancillary information in them that they should be thought of
as reference books. That is why I have found a good market for my college-level
neuroscience electronic textbook, “Core Ideas in Neuroscience,” in which each
2-3 page chapter is based entirely on each of the 75 core principles that cover
the broad span of membrane biochemistry to human cognition.. A typical
neuroscience textbook by other authors can run up to 1,500 pages.
Source:
Hornsby, Adam, and Love, B. C. (2014). Improved
classification of mammograms following idealized training. J. Appl. Res. Memory
and Cognition. 3(2):72-76.
Dr. Klemm is a Senior
Professor of Neuroscience at Texas A&M. His latest books are Memory Power 101, (Skyhorse) and Mental Biology (Prometheus). He also
writes learning and memory blogs for Psychology
Today magazine and his own site at thankyoubrain.blogspot.com. His posts
have nearly 1.5 million reader views.
