Our life-experiences contain a wealth of new and old information. The relative proportions of these change, of course, as we age. But how do we know whether we should be encoding new information or retrieving old information? It’s easy if the information is readily accessible, but what if it’s not? Bear in mind that (especially as we get older) most information / experiences we meet share some similarity to information we already have.

This question is made even more meaningful when you consider that it is the same brain region — the hippocampus — that’s involved in both encoding and retrieval, and these two processes depend (it is thought) on two quite opposite processes. While encoding is thought to rely on pattern separation (looking for differences), retrieval is thought to depend on pattern completion.

A recent study looked at what happens in the brain when people rapidly switch between encoding new objects and retrieving recently presented ones. Participants were shown 676 pictures of objects and asked to identify each one as being shown for the first time (‘new’), being repeated (‘old’), or as a modified version of something shown earlier (‘similar’). Recognizing the similar items as similar was the question of interest, as these items contain both old and new information and so the brain’s choice between encoding and retrieval is more difficult.

What they found was that participants were more likely to recognize similar items as similar (rather than old) if they had viewed a new item on the preceding trial. In other words, the experience of a new item primed them to notice novelty. Or to put it in another way: context biases the hippocampus toward either pattern completion or pattern separation.

This was supported by a further experiment, in which participants were shown both the object pictures, and also learned associations between faces and scenes. Critically, each scene was associated with two different faces. In the next learning phase, participants were taught a new scene association for one face from each pair. Each face-scene learning trial was preceded by an object recognition trial (new and old objects were shown and participants had to identify them as old or new) — critically, either a new or old object was consistently placed before a specific face-scene association. In the final test phase, participants were tested on the new face-scene associations they had just learned, as well as the indirect associations they had not been taught (that is, between the face of each pair that had not been presented during the preceding phase, and the scene associated with its partnered face).

What this found was that participants were more likely to pair indirectly related faces if those faces had been consistently preceded by old objects, rather than new ones. Moreover, they did so more quickly when the faces had been preceded by old objects rather than new ones.

This was interpreted as indicating that the preceding experience affects how well related information is integrated during encoding.

What all this suggests is that the memory activities you’ve just engaged in bias your brain toward the same sort of activities — so whether or not you notice changes to a café or instead nostalgically recall a previous meal, may depend on whether you noticed anyone you knew as you walked down the street!

An interesting speculation by the researchers is that such a memory bias (which only lasts a very brief time) might be an adaptive mechanism, reflecting the usefulness of being more sensitive to changes in new environments and less sensitive to irregularities in familiar environments.

A British study looking at possible gender differences in the effects of math anxiety involved 433 secondary school children (11-16 years old) completing customized (year appropriate) mental mathematics tests as well as questionnaires designed to assess math anxiety and (separately) test anxiety. These sources of anxiety are often confounded in research studies (and in real life!), and while they are indeed related, reported correlations are moderate, ranging from .30 to .50.

Previous research has been inconsistent as regards gender differences in math anxiety. While many studies have found significantly greater levels of math anxiety in females, many studies have found no difference, and some have even found higher levels in males. These inconsistencies may stem from differences in how math anxiety is defined or measured.

The present study looked at a rather more subtle question: does the connection between math anxiety and math performance differ by gender? Again, previous research has produced inconsistent findings.

Findings in this study were very clear: while there was no difference between boys and girls in math performance, there were marked differences in both math and test anxiety. Girls showed significantly greater levels of both. Both boys and girls showed a positive correlation between math anxiety and test anxiety, and a negative correlation between math anxiety and math performance, and test anxiety and performance. However, these relationships between anxiety and performance were stronger for girls than boys, with the correlation between test anxiety and performance being only marginally significant for boys (p<0.07), and the correlation between math anxiety and performance disappearing once test anxiety was controlled for.

In other words, greater math anxiety was linked to poorer math performance, but it was significant only for girls. Moreover, anxiety experienced by boys may simply reflect test anxiety, rather than specific math anxiety.

It is worth emphasizing that there was no gender difference in performance — that is, despite laboring under the burden of greater levels of anxiety, the girls did just as well as boys. This suggests that girls might do better than boys if they were free of anxiety. It is possible, however, that levels of anxiety didn’t actually differ between boys and girls — that the apparent difference stems from girls feeling more free to express their anxiety.

However, the finding that anxiety is greater in girls than boys is in line with evidence that anxiety (and worry in particular) is twice as prevalent in women as men, and more support for the idea that the girls are under-performing because of their anxiety comes from another recent study.

In this study, 149 college students performed a relatively simple task while their brain activity was measured. Specifically, they had to identify the middle letter in a series of five-letter groups. Sometimes the middle letter was the same as the other four ("FFFFF") while sometimes it was different ("EEFEE"). Afterward the students completed questionnaires about their anxiety and how much they worry (Penn State Worry Questionnaire and the Anxious Arousal subscale of the Mood and Anxiety Symptom Questionnaire).

Anxiety scores were significantly negatively correlated with accuracy on the task; worry scores were unrelated to performance.

Only girls who identified themselves as particularly anxious or big worriers recorded high brain activity when they made mistakes during the task (reflecting greater performance-monitoring). Although these women performed about the same as others on simple portions of the task, their brains had to work harder at it. Then, as the test became more difficult, the anxious females performed worse, suggesting worrying got in the way of completing the task.

Greater performance monitoring was not evident among anxious men.

[A reminder: these are group differences, and don't mean that all men or all women react in these ways.]

A study involving 75 perimenopausal women aged 40 to 60 has found that those with memory complaints tended to show impairments in working memory and attention. Complaints were not, however, associated with verbal learning or memory.

Complaints were also associated with depression, anxiety, somatic complaints, and sleep disturbance. But they weren’t linked to hormone levels (although estrogen is an important hormone for learning and memory).

What this suggests to me is that a primary cause of these cognitive impairments may be poor sleep, and anxiety/depression. A few years ago, I reported on a study that found that, although women’s reports of how many hot flashes they had didn’t correlate with memory impairment, an objective measure of the number of flashes they experienced during sleep did. Sleep, as I know from personal experience, is of sufficient importance that my rule-of-thumb is: don’t bother looking for any other causes of attention and memory deficits until you have sorted out your sleep!

Having said that, depressive symptoms showed greater relationship to memory complaints than sleep disturbance.

It’s no big surprise to hear that it is working memory in particular that is affected, because what many women at this time of life complain of is ‘brain fog’ — the feeling that your brain is full of cotton-wool. This doesn’t mean that you can’t learn new information, or remember old information. But it does mean that these tasks will be impeded to the extent that you need to hold on to too many bits of information. So mental arithmetic might be more difficult, or understanding complex sentences, or coping with unexpected disruptions to your routine, or concentrating on a task for a long time.

These sorts of problems are typical of those produced by on-going sleep deprivation, stress, and depression.

One caveat to the findings is that the study participants tended to be of above-average intelligence and education. This would protect them to a certain extent from cognitive decline — those with less cognitive reserve might display wider impairment. Other studies have found verbal memory, and processing speed, impaired during menopause.

Note, too, that a long-running, large population study has found no evidence for a decline in working memory, or processing speed, in women as they pass through perimenopause and menopause.

A review of 10 observational and four intervention studies as said to provide strong evidence for a positive relationship between physical activity and academic performance in young people (6-18). While only three of the four intervention studies and three of the 10 observational studies found a positive correlation, that included the two studies (one intervention and one observational) that researchers described as “high-quality”.

An important feature of the high-quality studies was that they used objective measures of physical activity, rather than students' or teachers' reports. More high-quality studies are clearly needed. Note that the quality score of the 14 studies ranged from 22%! to 75%.

Interestingly, a recent media report (NOT, I hasten to add, a peer-reviewed study appearing in an academic journal) spoke of data from public schools in Lincoln, Nebraska, which apparently has a district-wide physical-fitness test, which found that those were passed the fitness test were significantly more likely to also pass state reading and math tests.

Specifically, data from the last two years apparently shows that 80% of the students who passed the fitness test either met or exceeded state standards in math, compared to 66% of those who didn't pass the fitness test, and 84% of those who passed the fitness test met or exceeded state standards in reading, compared to 71% of those who failed the fitness test.

Another recent study looks at a different aspect of this association between physical exercise and academic performance.

The Italian study involved138 normally-developing children aged 8-11, whose attention was tested before and after three different types of class: a normal academic class; a PE class focused on cardiovascular endurance and involving continuous aerobic circuit training followed by a shuttle run exercise; a PE class combining both physical and mental activity by involving novel use of basketballs in varying mini-games that were designed to develop coordination and movement-based problem-solving. These two types of physical activity offered the same exercise intensity, but very different skill demands.

The attention test was a short (5-minute) paper-and-pencil task in which the children had to mark each occurrence of “d” with double quotation marks either above or below in 14 lines of randomly mixed p and d letters with one to four single and/or double quotation marks either over and/or under each letter.

Processing speed increased 9% after mental exercise (normal academic class) and 10% after physical exercise. These were both significantly better than the increase of 4% found after the combined physical and mental exertion.

Similarly, scores on the test improved 13% after the academic class, 10% after the standard physical exercise, and only 2% after the class combining physical and mental exertion.

Now it’s important to note is that this is of course an investigation of the immediate arousal benefits of exercise, rather than an investigation of the long-term benefits of being fit, which is a completely different question.

But the findings do bear on the use of PE classes in the school setting, and the different effects that different types of exercise might have.

First of all, there’s the somewhat surprising finding that attention was at least as great, if not better, after an academic class than the PE class. It would not have been surprising if attention had flagged. It seems likely that what we are seeing here is a reflection of being in the right head-space — that is, the advantage of continuing with the same sort of activity.

But the main finding is the, also somewhat unexpected, relative drop in attention after the PE class that combined mental and physical exertion.

It seems plausible that the reason for this lies in the cognitive demands of the novel activity, which is, I think, the main message we should take away from this study, rather than any comparison between physical and mental activity. However, it would not be surprising if novel activities that combine physical and mental skills tend to be more demanding than skills that are “purely” (few things are truly pure I know) one or the other.

Of course, it shouldn’t be overlooked that attention wasn’t hampered by any of these activities!

We’re all familiar with the experience of going to another room and forgetting why we’ve done so. The problem has been largely attributed to a failure of attention, but recent research suggests something rather more specific is going on.

In a previous study, a virtual environment was used to explore what happens when people move through several rooms. The virtual environment was displayed on a very large (66 inch) screen to provide a more immersive experience. Each ‘room’ had one or two tables. Participants ‘carried’ an object, which they would deposit on a table, before picking up a different object. At various points, they were asked if the object was, say, a red cube (memory probe). The objects were not visible at the time of questioning. It was found that people were slower and less accurate if they had just moved to a new room.

To assess whether this effect depends on a high degree of immersion, a recent follow-up to this study replicated the study using standard 17” monitors rather than the giant screens. The experiment involved 55 students and once again demonstrated a significant effect of shifting rooms. Specifically, when the probe was positive, the error rate was 19% in the shift condition compared to 12% on trials when the participant ‘traveled’ the same distance but didn’t change rooms. When the probe was negative, the error rate was 22% in the shift condition vs 7% for the non-shift condition. Reaction time was less affected — there was no difference when the probes were positive, but a marginally significant difference on negative-probe trials.

The second experiment went to the other extreme. Rather than reducing the immersive experience, researchers increased it — to a real-world environment. Unlike the virtual environments, distances couldn’t be kept constant across conditions. Three large rooms were used, and no-shift trials involved different tables at opposite ends of the room. Six objects, rather than just one, were moved on each trial. Sixty students participated.

Once again, more errors occurred when a room-shift was involved. On positive-probe trials, the error rate was 28% in the shift condition vs 23% in the non-shift. On negative-probe trials, the error rate was 21% and 18%, respectively. The difference in reaction times wasn’t significant.

The third experiment, involving 48 students, tested the idea that forgetting might be due to the difference in context at retrieval compared to encoding. To do this, the researchers went back to using the more immersive virtual environment (the 66” screen), and included a third condition. In this, either the participant returned to the original room to be tested (return) or continued on to a new room to be tested (double-shift) — the idea being to hold the number of spatial shifts the same.

There was no evidence that returning to the original room produced the sort of advantage expected if context-matching was the important variable. Memory was best in the no-shift condition, next best in the shift and return conditions (no difference between them), and worst in the double shift condition. In other words, it was the number of new rooms entered that appears to be important.

This is in keeping with the idea that we break the action stream into separate events using event boundaries. Passing through a doorway is one type of event boundary. A more obvious type is the completion of an action sequence (e.g., mixing a cake — the boundary is the action of putting it in the oven; speaking on the phone — the boundary is the action of ending the call). Information being processed during an event is more available, foregrounded in your attention. Interference occurs when two or more events are activated, increasing errors and sometimes slowing retrieval.

All of this has greater ramifications than simply helping to explain why we so often go to another room and forget why we’re there. The broader point is that everything that happens to us is broken up and filed, and we should look for the boundaries to these events and be aware of the consequences of them for our memory. Moreover, these contextual factors are important elements of our filing system, and we can use that knowledge to construct more effective tags.

Read an article on this topic at Mempowered