young adult

Tai Chi improves blood flow in young adults

A year-long study involving young adults has compared those who engaged in either tai chi or brisk walking or no exercise. Those who practiced tai chi had a significantly higher number of CD 34+ cells compared with those in the other groups.

Frequent multitaskers are the worst at it

A survey of college students found that those who scored highest in multitasking ability were also least likely to multitask, while those who scored lowest were most likely to engage in it.

I’ve reported often on the perils of multitasking. Here is yet another one, with an intriguing new finding: it seems that the people who multitask the most are those least capable of doing so!

The study surveyed 310 undergraduate psychology students to find their actual multitasking ability, perceived multitasking ability, cell phone use while driving, use of a wide array of electronic media, and personality traits such as impulsivity and sensation-seeking.

Those who scored in the top quarter on a test of multitasking ability tended not to multitask. Some 70% of participants thought they were above average at multitasking, and perceived multitasking ability (rather than actual) was associated with multitasking. Those with high levels of impulsivity and sensation-seeking were also more likely to multitask (with the exception of using a cellphone while driving, which wasn’t related to impulsivity, though it was related to sensation seeking).

The findings suggest that those who multitask don’t do so because they are good at multitasking, but because they are poor at focusing on one task.

The role of motivation on academic performance

A study shows how easily you can affect motivation, producing a significant effect on college test scores, while a large German study finds that motivational and strategy factors, but not intelligence, affects growth in math achievement at high school.

I’ve spoken before about the effects of motivation on test performance. This is displayed in a fascinating study by researchers at the Educational Testing Service, who gave one of their widely-used tests (the ETS Proficiency Profile, short form, plus essay) to 757 students from three institutions: a research university, a master's institution and a community college. Here’s the good bit: students were randomly assigned to groups, each given a different consent form. In the control condition, students were told: “Your answers on the tests and the survey will be used only for research purposes and will not be disclosed to anyone except the research team.” In the “Institutional” condition, the rider was added: “However, your test scores will be averaged with all other students taking the test at your college.” While in the “Personal” condition, they were told instead: “However, your test scores may be released to faculty in your college or to potential employers to evaluate your academic ability.”

No prizes for guessing which of these was more motivating!

Students in the “personal” group performed significantly and consistently better than those in the control group at all three institutions. On the multi-choice part of the test, the personal group performed on average .41 of the standard deviation higher than the control group, and the institutional group performed on average .26 SD higher than the controls. The largest difference was .68 SD. On the essay, the largest effect size was .59 SD. (The reason for the results being reported this way is because the focus of the study was on the use of such tests to assess and compare learning gains by colleges.)

The effect is perhaps less dramatic at the individual level, with the average sophomore score on the multichoice test being 460, compared to 458 and 455, for personal, institutional, and control groups, respectively. Interestingly, this effect was greater at the senior level: 469 vs 466 vs 460. For the essay question, however, the effect was larger: 4.55 vs 4.35 vs 4.21 (sophomore); 4.75 vs 4.37 vs 4.37 (senior). (Note that these scores have been adjusted by college admission scores).

Students also reported on motivation level, and this was found to be a significant predictor of test performance, after controlling for SAT or placement scores.

Student participants had received at least one year of college, or (for community colleges) taken at least three courses.

The findings confirm recently expressed concern that students don’t put their best efforts into low-stakes tests, and that, when such tests are used to make judgments about institutional performance (how much value they add), they may well be significantly misleading, if different institutions are providing different levels of motivation.

On a personal level, of course, the findings may be taken as further confirmation of the importance of non-academic factors in academic achievement. Something looked at more directly in the next study.

Motivation, study habits—not IQ—determine growth in math achievement

Data from a large German longitudinal study assessing math ability in adolescents found that, although intelligence was strongly linked to students' math achievement, this was only in the initial development of competence. The significant predictors of growth in math achievement, however, were motivation and study skills.

Specifically (and excitingly for me, since it supports some of my recurring themes!), at the end of Grade 5, perceived control was a significant positive predictor for growth, and surface learning strategies were a significant negative predictor. ‘Perceived control’ reflects the student’s belief that their grades are under their control, that their efforts matter. ‘Surface learning strategies’ reflect the use of rote memorization/rehearsal strategies rather than ones that encourage understanding. (This is not to say, of course, that these strategies don’t have their place — but they need to be used appropriately).

At the end of Grade 7, however, a slightly different pattern emerged, with intrinsic motivation and deep learning strategies the significant positive predictors of growth, while perceived control and surface learning strategies were no longer significant.

In other words, while intelligence didn’t predict growth at either point, the particular motivational and strategy variables that affected growth were different at different points in time, reflecting, presumably, developmental changes and/or changes in academic demands.

Note that this is not to say that intelligence doesn’t affect math achievement! It is, indeed, a strong predictor — but through its effect on getting the student off to a good start (lifting the starting point) rather than having an ongoing benefit.

There was, sadly but unfortunately consistent with other research, an overall decline in motivation from grade 5 to 7. There was also a smaller decline in strategy use (any strategy! — presumably reflecting the declining motivation).

It’s also worth noting that (also sadly but unsurprisingly) the difference between school types increased over time, with those in the higher track schools making more progress than those in the lowest track.

The last point I want to emphasize is that extrinsic motivation only affected initial levels, not growth. The idea that extrinsic motivation (e.g., wanting good grades) is of only short-term benefit, while intrinsic motivation (e.g., being interested in the subject) is far more durable, is one I have made before, and one that all parents and teachers should pay attention to.

The study involved 3,520 students, following them from grades 5 to 10. The math achievement test was given at the end of each grade, while intelligence and self-reported motivation and strategy use were assessed at the end of grades 5 and 7. Intelligence was assessed using the nonverbal reasoning subtest of Thorndike’s Cognitive Abilities Test (German version). The 42 schools in the study were spread among the three school types: lower-track (Hauptschule), intermediate-track (Realschule), and higher-track (Gymnasium). These school types differ in entrance standards and academic demands.

Rapamycin makes young mice learn better and prevents decline in old mice

Further evidence from mice studies that the Easter Island drug improves cognition, in young mice as well as old.

I have reported previously on research suggesting that rapamycin, a bacterial product first isolated from soil on Easter Island and used to help transplant patients prevent organ rejection, might improve learning and memory. Following on from this research, a new mouse study has extended these findings by adding rapamycin to the diet of healthy mice throughout their life span. Excitingly, it found that cognition was improved in young mice, and abolished normal cognitive decline in older mice.

Anxiety and depressive-like behavior was also reduced, and the mice’s behavior demonstrated that rapamycin was acting like an antidepressant. This effect was found across all ages.

Three "feel-good" neurotransmitters — serotonin, dopamine and norepinephrine — all showed significantly higher levels in the midbrain (but not in the hippocampus). As these neurotransmitters are involved in learning and memory as well as mood, it is suggested that this might be a factor in the improved cognition.

Other recent studies have suggested that rapamycin inhibits a pathway in the brain that interferes with memory formation and facilitates aging.

Nature walks improve cognition in people with depression

A small study provides more support for the idea that viewing nature can refresh your attention and improve short-term memory, and extends it to those with clinical depression.

I’ve talked before about Dr Berman’s research into Attention Restoration Theory, which proposes that people concentrate better after nature walks or even just looking at nature scenes. In his latest study, the findings have been extended to those with clinical depression.

The study involved 20 young adults (average age 26), all of whom had a diagnosis of major depressive disorder. Short-term memory and mood were assessed (using the backwards digit span task and the PANAS), and then participants were asked to think about an unresolved, painful autobiographical experience. They were then randomly assigned to go for a 50-minute walk along a prescribed route in either the Ann Arbor Arboretum (woodland park) or traffic heavy portions of downtown Ann Arbor. After the walk, mood and cognition were again assessed. A week later the participants repeated the entire procedure in the other location.

Participants exhibited a significant (16%) increase in attention and working memory after the nature walk compared to the urban walk. While participants felt more positive after both walks, there was no correlation with memory effects.

The finding is particularly interesting because depression is characterized by high levels of rumination and negative thinking. It seemed quite likely, then, that a solitary walk in the park might make depressed people feel worse, and worsen working memory. It’s intriguing that it didn’t.

It’s also worth emphasizing that, as in earlier studies, this effect of nature on cognition appears to be independent of mood (which is, of course, the basic tenet of Attention Restoration Theory).

Of course, this study is, like the others, small, and involves the same demographic. Hopefully future research will extend the sample groups, to middle-aged and older adults.

Myths about gender and math performance

Two new reviews debunk several theories for the reasons for gender gaps in math performance.

Is there, or is there not, a gender gap in mathematics performance? And if there is, is it biological or cultural?

Although the presence of a gender gap in the U.S. tends to be regarded as an obvious truth, evidence is rather more equivocal. One meta-analysis of studies published between 1990 and 2007, for example, found no gender differences in mean performance and nearly equal variability within each gender. Another meta-analysis, using 30 years of SAT and ACT scores, found a very large 13:1 ratio of middle school boys to girls at the highest levels of performance in the early 1980s, which declined to around 4:1 by 1991, where it has remained. A large longitudinal study found that males were doing better in math, across all socioeconomic classes, by the 3rd grade, with the ratio of boys to girls in the top 5% rising to 3:1 by 5th grade.

Regardless of the extent of any gender differences in the U.S., the more fundamental question is whether such differences are biological or cultural. The historical changes mentioned above certainly point to a large cultural component. Happily, because so many more countries now participate in the Trends in International Mathematics and Science Study (TIMSS) and the Programme in International Student Assessment (PISA), much better data is now available to answer this question. In 2007, for example, 4th graders from 38 countries and 8th graders from 52 countries participated in TIMSS. In 2009, 65 countries participated in PISA.

So what does all this new data reveal about the gender gap? Overall, there was no significant gender gap in the 2003 and 2007 TIMSS, with the exception of the 2007 8th graders, where girls outperformed boys.

There were, of course, significant gender gaps on a country basis. Researchers looked at several theories for what might underlie these.

Contradicting one theory, gender gaps did not correlate reliably with gender equity. In fact, both boys and girls tended to do better in math when raised in countries where females have better equality. The primary contributor to this appears to be women’s income and rates of participation in the work force. This is in keeping with the idea that maternal education and employment opportunities have benefits for their children’s learning regardless of gender.

The researchers also looked at the more specific hypothesis put forward by Steven Levitt, that gender inequity doesn’t hurt girls' math performance in Muslim countries, where most students attend single-sex schools. This theory was not borne out by the evidence. There was no consistent link between school type and math performance across countries.

However, math performance in the 29 wealthier countries could be predicted to a very high degree by three factors: economic participation and opportunity; GDP per capita; membership of one of three clusters — Middle Eastern (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia); East Asian (Hong Kong, Japan, South Korea, Singapore, Taiwan); rest (Russia, Hungary, Czech Republic, England, Canada, US, Australia, Sweden, Norway, Scotland, Cyprus, Italy, Malta, Israel, Spain, Lithuania, Malaysia, Slovenia, Dubai). The Middle Eastern cluster scored lowest (note the exception of Dubai), and the East Asian the highest. While there are many cultural factors differentiating these clusters, it’s interesting to note that countries’ average performance tended to be higher when students attribute less importance to mastering math.

The investigators also looked at the male variability hypothesis — the idea that males are more variable in their performance, and their predominance at the top is balanced by their predominance at the bottom. The study found however that greater male variation in math achievement varies widely across countries, and is not found at all in some countries.

In sum, the cross-country variability in performance in regard to gender indicates that the most likely cause of any differences lies in country-specific social factors. These could include perception of abilities as fixed vs malleable, attitude toward math, gender beliefs.

Stereotype threat

A popular theory of women’s underachievement in math concerns stereotype threat (first proposed by Spencer, Steele, and Quinn in a 1999 paper). I have reported on this on several occasions. However, a recent review of this research claims that many of the studies were flawed in their methodology and statistical analysis.

Of the 141 studies that cited the original article and related to mathematics, only 23 met the criteria needed (in the reviewers’ opinion) to replicate the original study:

  • Both genders tested
  • Math test used
  • Subjects recruited regardless of preexisting beliefs about gender stereotypes
  • Subjects randomly assigned to experimental conditions

Of these 23, three involved younger participants (< 18 years) and were excluded. Of the remaining 20 studies, only 11 (55%) replicated the original effect (a significant interaction between gender and stereotype threat, and women performing significantly worse in the threat condition than in the threat condition compared to men).

Moreover, half the studies confounded the results by statistically adjusting preexisting math scores. That is, the researchers tried to adjust for any preexisting differences in math performance by using a previous math assessment measure such as SAT score to ‘tweak’ the baseline score. This practice has been the subject of some debate, and the reviewers come out firmly against it, arguing that “an important assumption of a covariate analysis is that the groups do not differ on the covariate. But that group difference is exactly what stereotype threat theory tries to explain!” Note, too, that the original study didn’t make such an adjustment.

So what happens if we exclude those studies that confounded the results? That leaves ten studies, of which only three found an effect (and one of these found the effect only in a subset of the math test). In other words, overwhelmingly, it was the studies that adjusted the scores that found an effect (8/10), while those that didn’t adjust them didn’t find the effect (7/10).

The power of the adjustment in producing the effect was confirmed in a meta-analysis.

Now these researchers aren’t saying that stereotype threat doesn’t exist, or that it doesn’t have an effect on women in this domain. Their point is that the size of the effect, and the evidence for the effect, has come to be regarded as greater and more robust than the research warrants.

At a practical level, this may have led to too much emphasis on tackling this problem at the expense of investigating other possible causes and designing other useful interventions.

Reference: 

Kane, J. M., & Mertz, J. E. (2012). Debunking Myths about Gender and Mathematics Performance. Notices of the AMS, 59(1), 10-21.

[2698] Stoet, G., & Geary D. C. (2012).  Can stereotype threat explain the gender gap in mathematics performance and achievement?. Review of General Psychology;Review of General Psychology. No Pagination Specified - No Pagination Specified.

Decision-making, working memory, and age

In October I reported on a study that found older adults did better than younger adults on a decision-making task that reflected real-world situations more closely than most tasks used in such studies. It was concluded that, while (as previous research has shown) younger adults may do better on simple decision-making tasks, older adults have the edge when it comes to more complex scenarios. Unsurprisingly, this is where experience tells.

Dealing with math anxiety

A new study shows that some math-anxious students can overcome performance deficits through their ability to control their negative responses. The finding indicates that interventions should focus on anticipatory cognitive control.

Math-anxiety can greatly lower performance on math problems, but just because you suffer from math-anxiety doesn’t mean you’re necessarily going to perform badly. A study involving 28 college students has found that some of the students anxious about math performed better than other math-anxious students, and such performance differences were associated with differences in brain activity.

Math-anxious students who performed well showed increased activity in fronto-parietal regions of the brain prior to doing math problems — that is, in preparation for it. Those students who activated these regions got an average 83% of the problems correct, compared to 88% for students with low math anxiety, and 68% for math-anxious students who didn’t activate these regions. (Students with low anxiety didn’t activate them either.)

The fronto-parietal regions activated included the inferior frontal junction, inferior parietal lobe, and left anterior inferior frontal gyrus — regions involved in cognitive control and reappraisal of negative emotional responses (e.g. task-shifting and inhibiting inappropriate responses). Such anticipatory activity in the fronto-parietal region correlated with activity in the dorsomedial caudate, nucleus accumbens, and left hippocampus during math activity. These sub-cortical regions (regions deep within the brain, beneath the cortex) are important for coordinating task demands and motivational factors during the execution of a task. In particular, the dorsomedial caudate and hippocampus are highly interconnected and thought to form a circuit important for flexible, on-line processing. In contrast, performance was not affected by activity in ‘emotional’ regions, such as the amygdala, insula, and hypothalamus.

In other words, what’s important is not your level of anxiety, but your ability to prepare yourself for it, and control your responses. What this suggests is that the best way of dealing with math anxiety is to learn how to control negative emotional responses to math, rather than trying to get rid of them.

Given that cognitive control and emotional regulation are slow to mature, it also suggests that these effects are greater among younger students.

The findings are consistent with a theory that anxiety hinders cognitive performance by limiting the ability to shift attention and inhibit irrelevant/distracting information.

Note that students in the two groups (high and low anxiety) did not differ in working memory capacity or in general levels of anxiety.

Reference: 

[2600] Lyons, I. M., & Beilock S. L. (2011).  Mathematics Anxiety: Separating the Math from the Anxiety. Cerebral Cortex.

You can download a copy of the study here: Math anxiety.pdf

Even mild head injuries can seriously affect the brain

Traumatic brain injury is the biggest killer of young adults and children in the U.S., and in a year more Americans suffer a TBI than are diagnosed with breast, lung, prostate, brain and colon cancer combined. There are many causes of TBI, but one of the more preventable is that of sports concussion.

When age helps decision making

New study modifies findings that younger adults are better decision-makers by showing older adults are better when the scenarios involve multiple considerations.

Research has shown that younger adults are better decision makers than older adults — a curious result. A new study tried to capture more ‘real-world’ decision-making, by requiring participants to evaluate each result in order to strategize the next choice.

This time (whew!), the older adults did better.

In the first experiment, groups of older (60-early 80s) and younger (college-age) adults received points each time they chose from one of four options and tried to maximize the points they earned.  For this task, the younger adults were more efficient at selecting the options that yielded more points.

In the second experiment, the rewards received depended on the choices made previously.  The “decreasing option” gave a larger number of points on each trial, but caused rewards on future trials to be lower. The “increasing option” gave a smaller reward on each trial but caused rewards on future trials to increase.  In one version of the test, the increasing option led to more points earned over the course of the experiment; in another, chasing the increasing option couldn’t make up for the points that could be accrued grabbing the bigger bite on each trial.

The older adults did better on every permutation.

Understanding more complex scenarios is where experience tells. The difference in performance also may reflect the different ways younger and older adults use their brains. Decision-making can involve two different reward learning systems, according to recent thinking. In the model-based system, a cognitive model is constructed that shows how various actions and their rewards are connected to each other. Decisions are made by simulating how one decision will affect future decisions. In the model-free system, on the other hand, only values associated with each choice are considered.

These systems are rooted in different parts of the brain. The model-based system uses the intraparietal sulcus and lateral prefrontal cortex, while the model-free system uses the ventral striatum. There is some evidence that younger adults use the ventral striatum (involved in habitual, reflexive learning and immediate reward) for decision-making more than older adults, and older adults use the dorsolateral prefrontal cortex (involved in more rational, deliberative thinking) more than younger adults.

Reference: 
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