school-age child

Video games and impaired attention: a vicious circle

April, 2012

A large, long-running study suggests both that children with attention difficulties tend to spend more time playing video games, and that extensive video game playing is bad for attention.

A three-year study involving 3,034 Singaporean children and adolescents (aged 8-17) has found that those who spent more time playing video games subsequently had more attention problems, even when earlier attention problems, sex, age, race, and socioeconomic status were statistically controlled. Those who were more impulsive or had more attention problems subsequently spent more time playing video games, even when initial video game playing was statistically controlled. These findings suggest that the cause-effect relationship between video game playing and attention problems/impulsiveness goes both ways.

While the particular content may have an effect on attention problems and impulsiveness (violent games appeared to be an additional, independent, factor in attention problems), it was the total time spent that was more important.

Participants completed questionnaires about their video game playing habits annually for three years running. They also completed questionnaires aimed to measure attention and impulsiveness (the Current ADHD Symptoms Scale Self-Report, and the Barratt Impulsiveness Scale-11, respectively). Regarding attention, the children answered questions such as how often they "fail to give close attention to details or make careless mistakes" in their work or "blurt out answers before questions have been completed." For the impulsivity test, they selected points they felt described themselves, such as "I often make things worse because I act without thinking" or "I concentrate easily."

How does this finding relate to other evidence showing that playing video games can improve visual attention for rapid and accurate recognition of information from the environment? The answer lies in the different nature of attention — the attention needed for visual search differs in important ways from the attention necessary for sustained concentration in contexts that are often effortful and/or boring.

The example of many attention-challenged individuals makes this more understandable. Many parents of children with ADHD find that the only thing their child can concentrate on for a lengthy period is video games. The answer to that riddle is the rapidly changing nature of video games, and the way they are designed to grab the attention, with flashing lights and loud noises and moving images etc. The young person is not, therefore, improving their ability to focus in a way that is helpful for the school environment, or indeed for everyday life.

Unfortunately, this study suggests that it is precisely those people who are most in need of such ‘external supports’ for attention (‘grabbing’ stimuli such as lights and sounds and movement) — that is, those individuals who are least able to control their own attention — who are most likely to spend a lot of time playing such games. The games then weaken their attentional control even more, and so the cycle continues.

So this research answers the question ADHD parents tend to have: should I encourage my child to play video games a lot (given that it’s the only thing that holds their attention) or not? The answer, unfortunately, would seem to be: not. However, all is not lost. There are computer ‘games’ that are designed to help those with ADHD learn to concentrate in a way that is more useful (see the Topic collection on ADHD for more on this).

The American Academy of Pediatrics recommends one hour per day of total media screen time (including TV, DVDs, video games, Internet, iPad, etc.) for children in elementary school, and two hours for children in secondary school.

Reference: 

Gentile, D.A., Swing, E.L., Lim, C.G. & Khoo, A. 2012. Video game playing, attention problems, and impulsiveness: Evidence of bidirectional causality. Psychology of Popular Media Culture, Vol 1(1), Jan 2012, 62-70. doi: 10.1037/a0026969

Full text available at http://www.apa.org/pubs/journals/releases/ppm-1-1-62.pdf

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Support for link between physical activity & academic success

March, 2012

A review supports the benefits of physical activity for children’s and adolescent’s scholastic performance, but points to the need for better studies. A recent study looks at the effects on attention of different types of physical activity.

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!

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Myths about gender and math performance

January, 2012

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.

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High levels of city pollution linked to brain damage in children

November, 2011

A small Mexican study provides more evidence for the negative effect of pollution on developing brains, with cognitive impairment linked to reduced white matter in specific regions.

In yet another study of the effects of pollution on growing brains, it has been found that children who grew up in Mexico City (known for its very high pollution levels) performed significantly worse on cognitive tests than those from Polotitlán, a city with a strong air quality rating.

The study involved 30 children aged 7 or 8, of whom 20 came from Mexico City, and 10 from Polotitlán. Those ten served as controls to the Mexico City group, of whom 10 had white matter hyperintensities in their brains, and 10 had not. Regardless of the presence of lesions, MC children were found to have significantly smaller white matter volumes in right parietal and bilateral temporal regions. Such reduced volumes were correlated with poorer performance on a variety of cognitive tests, especially those relating to attention, working memory, and learning.

It’s suggested that exposure to air pollution disturbs normal brain development, resulting in cognitive deficits.

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Dealing with math anxiety

November, 2011

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 lobule, 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.

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Inattention, not hyperactivity, is associated with educational failure

October, 2011

A large, long-running study reveals that academic achievement for those with ADHD is hindered by attention problems not hyperactivity.

Data from parents and teachers of 2000 randomly selected children has revealed that only 29% of children with attention problems finished high school compared to 89% of children without such problems. When it came to hyperactivity, the difference was smaller: 40% versus 77%. After taking account of factors such as socioeconomic status and health issues that are correlated with ADHD, inattention was still a highly significant contributor, but hyperactivity was not.

Yearly assessments of the children were taken from age 6 to 12, and high school graduation status was obtained from official records. Attention problems were evaluated by teachers on the basis of behavior such as an inability to concentrate, absentmindedness, or a tendency to give up or be easily distracted. Hyperactivity was identified by behavior such as restlessness, running around, squirming and being fidgety.

The researchers make the excellent point that those with attention difficulties are often forgotten because, unlike hyperactive children, they don't disturb the class.

The findings point to the need to distinguish inattention and hyperactivity, and to provide early preventive intervention for attention problems.

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Ability to remember memories' origin develops slowly

October, 2011

A study comparing the brains of children, adolescents, and young adults has found that the ability to remember the origin of memories is slow to mature. As with older adults, impaired source memory increases susceptibility to false memories.

In the study, 18 children (aged 7-8), 20 adolescents (13-14), and 20 young adults (20-29) were shown pictures and asked to decide whether it was a new picture or one they had seen earlier. Some of the pictures were of known objects and others were fanciful figures (this was in order to measure the effects of novelty in general). After a 10-minute break, they resumed the task — with the twist that any pictures that had appeared in the first session should be judged “new” if that was the first appearance in the second session. EEG measurements (event-related potentials — ERPs) were taken during the sessions.

ERPs at the onset of a test stimulus (each picture) are different for new and old (repeated) stimuli. Previous studies have established various old/new effects that reflect item and source memory in adults. In the case of item memory, recognition is thought to be based on two processes — familiarity and recollection — which are reflected in ERPs of different timings and location (familiarity: mid-frontal at 300-500 msec; recollection: parietal at 400-70 msec). Familiarity is seen as a fast assessment of similarity, while recollection varies according to the amount of retrieved information.

Source memory appears to require control processes that involve the prefrontal cortex. Given that this region is the slowest to mature, it would not be surprising if source memory is a problematic memory task for the young. And indeed, previous research has found that children do have particular difficulty in sourcing memories when the sources are highly similar.

In the present study, children performed more poorly than adolescents and adults on both item memory and source memory. Adolescents performed more poorly than adults on item memory but not on source memory. Children performed more poorly on source memory than item memory, but adolescents and adults showed no difference between the two tasks.

All groups responded faster to new items than old, and ERP responses to general novelty were similar across the groups — although children showed a left-frontal focus that may reflect the transition from analytic to a more holistic processing approach.

ERPs to old items, however, showed a difference: for adults, they were especially pronounced at frontal sites, and occurred at around 350-450 msec; for children and adolescents they were most pronounced at posterior sites, occurring at 600-800 msec for children and 400-600 msec for adolescents. Only adults showed the early midfrontal response that is assumed to reflect familiarity processing. On the other hand, the late old/new effect occurring at parietal sites and thought to reflect recollection, was similar across all age groups. The early old/new effect seen in children and adolescents at central and parietal regions is thought to reflect early recollection.

In other words, only adults showed the brain responses typical of familiarity as well as recollection. Now, some research has found evidence of familiarity processing in children, so this shouldn’t be taken as proof against familiarity processing in the young. What seems most likely is that children are less likely to use such processing. Clearly the next step is to find out the factors that affect this.

Another interesting point is the early recollective response shown by children and adolescents. It’s speculated that these groups may have used more retrieval cues — conceptual as well as perceptual — that facilitated recollection. I’m reminded of a couple of studies I reported on some years ago, that found that young children were better than adults on a recognition task in some circumstances — because children were using a similarity-based process and adults a categorization-based one. In these cases, it had more to do with knowledge than development.

It’s also worth noting that, in adults, the recollective response was accentuated in the right-frontal area. This suggests that recollection was overlapping with post-retrieval monitoring. It’s speculated that adults’ greater use of familiarity produces a greater need for monitoring, because of the greater uncertainty.

What all this suggests is that preadolescent children are less able to strategically recollect source information, and that strategic recollection undergoes an important step in early adolescence that is probably related to improvements in cognitive control. But this process is still being refined in adolescents, in particular as regards monitoring and coping with uncertainty.

Interestingly, source memory is also one of the areas affected early in old age.

Failure to remember the source of a memory has many practical implications, in particular in the way it renders people more vulnerable to false memories.

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Brain continues to develop well into our 20s

October, 2011

A new study shows that the wiring that connects the frontal lobes to other parts of the cerebral cortex continues to develop well into young adulthood — except for a small minority that show degradation.

Brain imaging data from 103 healthy people aged 5-32, each of whom was scanned at least twice, has demonstrated that wiring to the frontal lobe continues to develop after adolescence.

The brain scans focused on 10 major white matter tracts. Significant changes in white matter tracts occurred in the vast majority of children and early adolescents, and these changes were mostly complete by late adolescence for projection and commissural tracts (projection tracts project from the cortex to non-cortical areas, such as the senses and the muscles, or from the thalamus to the cortex; commissural tracts cross from one hemisphere to the other). But association tracts (which connect regions within the same hemisphere) kept developing after adolescence.

This was particularly so for the inferior and superior longitudinal and fronto-occipital fascicule (the inferior longitudinal fasciculus connects the temporal and occipital lobes; the superior longitudinal fasciculus connects the frontal lobe to the occipital lobe and parts of the temporal and parietal lobes). These frontal connections are needed for complex cognitive tasks such as inhibition, executive functioning, and attention.

The researchers speculated that this continuing development may be due to the many life experiences in young adulthood, such as pursing post-secondary education, starting a career, independence and developing new social and family relationships.

But this continuing development wasn’t seen in everyone. Indeed, in some people, there was evidence of reductions, rather than growth, in white matter integrity. It may be that this is connected with the development of psychiatric disorders that typically develop in adolescence or young adulthood — perhaps directly, or because such degradation increases vulnerability to other factors (e.g., to drug use). This is speculative at the moment, but it opens up a new avenue to research.

Reference: 

[2528] Lebel, C., & Beaulieu C.
(2011).  Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood.
The Journal of Neuroscience. 31(30), 10937 - 10947.

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Helping students & children get enough sleep

October, 2011

Simple interventions can help college students improve their sleep. Regular sleep habits are important for young children. Sleep deprivation especially affects performance on open-ended problems.

One survey of nearly 200 undergraduate college students who were not living with a parent or legal guardian found that 55% reported getting less than seven hours sleep. This is consistent with other surveys. The latest study confirms such a result, but also finds that students tend to think their sleep quality is better than it is (70% of students surveyed described their sleep as "fairly good" or better). It’s suggested that this disconnect arises from students making comparisons in an environment where poor sleep is common — even though they realized, on being questioned, that poor sleep undermined their memory, concentration, class attendance, mood, and enthusiasm.

None of this is surprising, of course. But this study did something else — it tried to help.

The researchers launched a campuswide media campaign consisting of posters, student newspaper advertisements and a "Go to Bed SnoozeLetter", all delivering information about the health effects of sleep and tips to sleep better, such as keeping regular bedtime and waking hours, exercising regularly, avoiding caffeine and nicotine in the evening, and so on. The campaign cost less than $2,500, and nearly 10% (90/971) said it helped them sleep better.

Based on interviews conducted as part of the research, the researchers compiled lists of the top five items that helped and hindered student sleep:

Helpers

  • Taking time to de-stress and unwind
  • Creating a room atmosphere conducive to sleep
  • Being prepared for the next day
  • Eating something
  • Exercising

Hindrances

  • Dorm noise
  • Roommate (both for positive/social reasons and negative reasons)
  • Schoolwork
  • Having a room atmosphere not conducive to sleep
  • Personal health issues

In another study, this one involving 142 Spanish schoolchildren aged 6-7, children who slept less than 9 hours and went to bed late or at irregular times showed poorer academic performance. Regular sleep habits affected some specific skills independent of sleep duration.

69% of the children returned home after 9pm at least three evenings a week or went to bed after 11pm at least four nights a week.

And a recent study into the effects of sleep deprivation points to open-ended problem solving being particularly affected. In the study, 35 West Point cadets were given two types of categorization task. The first involved cate­gorizing drawings of fictional animals as either “A” or “not A”; the second required the students to sort two types of fic­tional animals, “A” and “B.” The two tests were separated by 24 hours, during which half the students had their usual night’s sleep, and half did not.

Although the second test required the students to learn criteria for two animals instead of one, sleep deprivation impaired performance on the first test, not the second.

These findings suggest the fault lies in attention lapses. Open-ended tasks, as in the first test, require more focused attention than those that offer two clear choices, as the second test did.

News reports on sleep deprivation are collated here.

Reference: 

[2521] Orzech, K. M., Salafsky D. B., & Hamilton L A.
(2011).  The State of Sleep Among College Students at a Large Public University.
Journal of American College Health. 59, 612 - 619.

[2515] Cladellas, R., Chamarro A., del Badia M M., Oberst U., & Carbonell X.
(2011).  Efectos de las horas y los habitos de sueno en el rendimiento academico de ninos de 6 y 7 anos: un estudio preliminarEffects of sleeping hours and sleeping habits on the academic performance of six- and seven-year-old children: A preliminary study.
Cultura y Educación. 23(1), 119 - 128.

Maddox WT; Glass BD; Zeithamova D; Savarie ZR; Bowen C; Matthews MD; Schnyer DM. The effects of sleep deprivation on dissociable prototype learning systems. SLEEP 2011;34(3):253-260.

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Childhood amnesia shifts with time

August, 2011

A new study finds that the earliest memories children can recall shifts with time, providing support for the theory that children’s memories don’t consolidate in the way adults’ memories do.

Childhood amnesia — our inability to remember almost everything that happened to us when very young — is always interesting. It’s not as simple as an inability to form long-term memories. Most adults can’t remember events earlier than 3-4 years (there is both individual and cultural variability), even though 2-year-olds are perfectly capable of remembering past events (side-note: memory durability increases from about a day to a year from age six months to two years). Additionally, research has shown that young children (6-8) can recall events that happened 4-6 years previously.

Given that the ability to form durable memories is in place, what governs which memories are retained? The earliest memories adults retain tend to be of events that have aroused emotions. Nothing surprising about that. More interesting is research suggesting that children can only describe memories of events using words they knew when the experience occurred — the study of young children (27, 33 or 39 months) found that, when asked about the experimental situation (involving a "magic shrinking machine") six months later, the children easily remembered how to operate the device, but were only able to describe the machine in words they knew when they first learned how to operate it.

Put another way this isn’t so surprising: our memories depend on how we encode them at the time. So two things may well be in play in early childhood amnesia: limited encoding abilities (influenced but not restricted to language) may mean the memories made are poor in quality (whatever that might mean); the development of encoding abilities means that later attempts to retrieve the memory may be far from matching the original memory. Or as one researcher put it, the format is different.

A new study about childhood amnesia looks at a different question: does the boundary move? 140 children (aged 4-13) were asked to describe their three earliest memories, and then asked again two years later (not all could provide as many as three early memories; the likelihood improved with age).

While more than a third of the 10- to 13-year-olds described the same memory as their very earliest on both occasions, children between 4 and 7 at the first interview showed very little overlap between the memories (only 2 of the 27 4-5 year-olds, and 3 of the 23 6-7 year-olds). There was a clear difference between the overlap seen in this youngest group (4-7) and the oldest (10-13), with the in-between group (8-9) being placed squarely between the two (20.7% compared to 10% and 36%).

Moreover, children under 8 at the first interview mostly had no overlap between any of the memories they provided at the two interviews, while those who were at least 8 years old did. For the oldest groups (10-13), more than half of all the memories they provided were the same.

The children were also given recall cues for memories they hadn’t spontaneously recalled. That is, they were told synopses of memories belonging to both their own earlier memories, and other children’s earlier memories. Almost all of the false memories were correctly rejected (the exceptions mostly occurred with the youngest group, those initially aged 4-5). However, the youngest children didn’t recognize over a third of their own memories, while almost all the oldest children’s memories were recognized (90% by 8-11 year-olds; all but one by 12-13 year-olds). Their age at the time of the event didn’t seem to affect the oldest or the very youngest groups, but 6-9 year-olds were more likely to recall after cuing events that happened at least a year later than those events that weren’t recalled after cuing.

In general, the earliest memories were several months later at the follow-up than they had been previously. The average age at the time of the earliest memory was 32 months, and 39.6 months on the follow-up interview. This shift in time occurred across all ages. Moreover, for the very earliest memory, the time-shift was even greater: a whole year.

In connection with the earlier study I mentioned, regarding the importance of language and encoding, it is worth noting that by and large, when the same memories were recalled, the same amount of information was recalled.

There was no difference between the genders.

The findings don’t rule out theories of the role of language. It seems clear to me that more than one thing is going on in childhood amnesia. These findings bear on another aspect: the forgetting curve.

It has been suggested that forgetting in children reflects a different function than forgetting in adults. Forgetting in adults matches a power function, reflecting the fact that forgetting slows over time (as is often quoted, most forgetting occurs in the first 24 hours; the longer you remember something, the more likely you are to remember it forever). However, there is some evidence that forgetting in children is best modeled in an exponential function, reflecting the continued vulnerability of memories. It seems they are not being consolidated in the way adults’ memories are. This may be because children don’t yet have the cognitive structures in place that allow them to embed new memories in a dense network.

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