Category Archives: Psychology

Some skills needed for literacy may be developed in infancy: complex babble linked with better reading

A study published in PLOSOne is again something rather nice to know than showing us something new to do, infants capable of complex babble may grow into stronger readers, except it may help us in a future to identify reading disabilities at an early age.

From the press release:

Infants’ early speech production may predict their later literacy, according to a study published October 10, 2018 in the open-access journal PLOS ONE by Kelly Farquharson from Florida State University and colleagues.

Children with difficulties in identifying letters are more likely to develop reading impairments, but such difficulties cannot be uncovered until the child is 3 to 5 years old. The authors of the present study investigated whether assessing language ability even earlier, by measuring speech complexity in infancy, might predict later difficulties.

The authors tracked nine infants from English-speaking US families between the ages of 9 and 30 months. They recorded each infant’s babble as the child interacted with their primary caregiver, looking specifically at the consonant-vowel (CV) ratio, a demonstrated measure of speech complexity. The authors then met each child again when they were six years old to examine their ability to identify letters, a known predictor of later reading impairment.

They found that those children with more complex babble as infants performed better when identifying specific letters in their later reading test. Though the sample size was relatively small and all 9 children participating in this study all developed normally (meaning the range of variability was restricted), these results may indicate a link between early speech production and literacy skill.

The authors suggest that in the future, the complexity of infant babble may be useful as an earlier predictor of reading impairments in children than letter identification tests, enabling parents and professionals to earlier identify and treat children at risk of reading difficulties.

Farquharson adds: “This paper provides exciting data to support an early and robust connection between speech production and later literacy skills. There is clinical utility in this work – we are moving closer to establishing behavioral measures that may help us identify reading disabilities sooner.”

Abstract of the study:

Letter identification is an early metric of reading ability that can be reliability tested before a child can decode words. We test the hypothesis that early speech production will be associated with children’s later letter identification. We examined longitudinal growth in early speech production in 9 typically developing children across eight occasions, every 3 months from 9 months to 30 months. At each occasion, participants and their caregivers engaged in a speech sample in a research lab. This speech sample was transcribed for a variety of vocalizations, which were then transformed to calculate consonant-vowel ratio. Consonant-vowel ratio is a measure of phonetic complexity in speech production. At the age of 72 months, children’s letter knowledge was measured. A multilevel model including fixed quadratic age change and a random intercept was estimated using letter identification as a predictor of the growth in early speech production from 9–30 months, measured by the outcome of consonant-vowel ratio. Results revealed that the relation between early speech production and letter identification differed over time. For each additional letter that a child identified, their consonant-vowel ratio at the age of 9 months increased. As such, these results confirmed our hypothesis: more robust early speech production is associated with more accurate letter identification.

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How much English do non-english children learn outside the classroom?

This morning my colleague Vanessa De Wilde shared her first soon to be published scientific paper with me and I like to share the insights here too as they can be relevant to other people too. The study is soon to be published in Bilingualism: Language and Cognition and was co-authored by Marc Brysbaert and June Eyckmans.

I first want to share with you the abstract as it already summarizes the study clearly:

In this study we examined the level of English proficiency children can obtain through out-of- school exposure in informal contexts prior to English classroom instruction. The second aim was to determine the input types that fuel children’s informal language acquisition. Language learning was investigated in 780 Dutch-speaking children (aged 10-12), who were tested on their English receptive vocabulary knowledge, listening, speaking, reading and writing skills. Information about learner characteristics and out-of-school English exposure was gathered using questionnaires. The results show large language gains for a substantial number of children but also considerable individual differences. The most beneficial types of input were gaming, use of social media and speaking. These input types are interactive and multimodal and they involve language production. We also found that the various language tests largely measure the same proficiency component.

But I want to share some of the findings more in depth:

“The mean score for the receptive vocabulary test was 65% (53% when cognates were left out of the test), attesting to the degree of vocabulary that can be acquired when children areexposed repeatedly to a language through activities that do not focus on language learning but on the negotiation of meaning (e.g. while playing a game).”

“English is seen as a high-status language by the participants in our study (733 participants answered they think English is a fun language, only 27 claimed not to like English), which probably means that they enjoy engaging in (digital) interactions in English.”

“…our findings show the high divergence in the scores obtained, a finding that was also present in Lefever (2010). About a quarter of the students did not pick up much English (yet). ”

A considerable part of the differences in test results could be explained by the amount of exposure the children had received (exposure to the language explained 22% of the variability in the children’s overall proficiency scores). Other variables likely to be involved are individual differences in intelligence and language aptitude (Paradis, 2011; Sun, Steinkrauss, Tendeiro & De Bot 2016; Unsworth, Persson, Prins & De Bot, 2014), which unfortunately could not be addressed in the present study.

“…the two most regularly investigated in studies on contextual learning in a formal context did not turn out to be the most important. These are reading L2 books and watching subtitled television programs. Although both variables are positively correlated with L2 knowledge, the correlations are much lower than those of three other variables.”

“The three most important types of input for children’s language proficiency were: use of social media in English, gaming in English, and speaking English. These three types of exposure are the types which offer ample opportunities for social interaction and authentic communication in contrast with watching television, listening to music, and reading, which are far less interactive. Apparently, passive perception of a language is less effective than active use of the language,…”

“…listening to English music seems to have a negative influence on children’s contextual language learning, when the effects of the other variables are partialled out. This is in line with the finding that productive and multimodal types of input are more effective. The fact that the negative effect is significant is probably due to the nature of the input. Listening or even singing along to a song does not necessarily lead to understanding and learning the language. Furthermore, it takes away time from other activities that are more effective. At the same time, even though the variable is significant, it only explains some 1% of the variation.”



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Using AI to discover learning disabilities in children

Whenever you hear or read artificial intelligence one starts to dream. Ok, I admit: I do. Every time I use Siri I’m reminded what a long way we still have to go, except for when my children ask silly questions. But this study uses AI in a whole different way and way more serious: to check if the humans made a mistake when labelling a child with learning disabilities.

From the press release:

Scientists using machine learning – a type of artificial intelligence – with data from hundreds of children who struggle at school, identified clusters of learning difficulties which did not match the previous diagnosis the children had been given.

The researchers from the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge say this reinforces the need for children to receive detailed assessments of their cognitive skills to identify the best type of support.

The study, published in Developmental Science, recruited 550 children who were referred to a clinic – the Centre for Attention Learning and Memory – because they were struggling at school.

The scientists say that much of the previous research into learning difficulties has focussed on children who had already been given a particular diagnosis, such as attention deficit hyperactivity disorder (ADHD), an autism spectrum disorder, or dyslexia. By including children with all difficulties regardless of diagnosis, this study better captured the range of difficulties within, and overlap between, the diagnostic categories.

Dr Duncan Astle from the MRC Cognition and Brain Sciences Unit at the University of Cambridge, who led the study said: “Receiving a diagnosis is an important landmark for parents and children with learning difficulties, which recognises the child’s difficulties and helps them to access support. But parents and professionals working with these children every day see that neat labels don’t capture their individual difficulties – for example one child’s ADHD is often not like another child’s ADHD.

“Our study is the first of its kind to apply machine learning to a broad spectrum of hundreds of struggling learners.”

The team did this by supplying the computer algorithm with lots of cognitive testing data from each child, including measures of listening skills, spatial reasoning, problem solving, vocabulary, and memory. Based on these data, the algorithm suggested that the children best fit into four clusters of difficulties.

These clusters aligned closely with other data on the children, such as the parents’ reports of their communication difficulties, and educational data on reading and maths. But there was no correspondence with their previous diagnoses. To check if these groupings corresponded to biological differences, the groups were checked against MRI brain scans from 184 of the children. The groupings mirrored patterns in connectivity within parts of the children’s brains, suggesting that that the machine learning was identifying differences that partly reflect underlying biology.

Two of the four groupings identified were: difficulties with working memory skills, and difficulties with processing sounds in words.

Difficulties with working memory – the short-term retention and manipulation of information – have been linked with struggling with maths and with tasks such as following lists. Difficulties in processing the sounds in words, called phonological skills, has been linked with struggling with reading.

Dr Astle said: “Past research that’s selected children with poor reading skills has shown a tight link between struggling with reading and problems with processing sounds in words. But by looking at children with a broad range of difficulties we found unexpectedly that many children with difficulties with processing sounds in words don’t just have problems with reading – they also have problems with maths.

“As researchers studying learning difficulties, we need to move beyond the diagnostic label and we hope this study will assist with developing better interventions that more specifically target children’s individual cognitive difficulties.”

Dr Joni Holmes, from the MRC Cognition and Brain Sciences Unit at the University of Cambridge, who was senior author on the study said: “Our work suggests that children who are finding the same subjects difficult could be struggling for very different reasons, which has important implications for selecting appropriate interventions.”

The other two clusters identified were: children with broad cognitive difficulties in many areas, and children with typical cognitive test results for their age. The researchers noted that the children in the grouping that had cognitive test results that were typical for their age may still have had other difficulties that were affecting their schooling, such as behavioural difficulties, which had not been included in the machine learning.

Dr Joanna Latimer, Head of Neurosciences and Mental Health at the MRC, said: “These are interesting, early-stage findings which begin to investigate how we can apply new technologies, such as machine learning, to better understand brain function. The MRC funds research into the role of complex networks in the brain to help develop better ways to support children with learning difficulties.”

Abstract of the paper:

Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within group homogeneity and between group differences, and fails to capture comorbidity. Here, we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n = 530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behavior questionnaires. Within the network we could identify four groups of children: (a) children with broad cognitive difficulties, and severe reading, spelling and maths problems; (b) children with age‐typical cognitive abilities and learning profiles; (c) children with working memory problems; and (d) children with phonological difficulties. Despite their contrasting cognitive profiles, the learning profiles for the latter two groups did not differ: both were around 1 SD below age‐expected levels on all learning measures. Importantly a child’s cognitive profile was not predicted by diagnosis or referral reason. We also constructed whole‐brain structural connectomes for children from these four groupings (n = 184), alongside an additional group of typically developing children (n = 36), and identified distinct patterns of brain organization for each group. This study represents a novel move toward identifying data‐driven neurocognitive dimensions underlying learning‐related difficulties in a representative sample of poor learners.

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What predicts best how much you will earn? A very novel way to study this…

This morning Jan Demol sent me this study and at first I was looking at the outcomes – remembering the present Marshmallow debate. But tonight when I looked again at the study I noticed that the method is the bigger news. The researchers used machine learning to compute a whole lot of data, and it left me wondering how this will influence further research.

Let’s look at the summary in the press release:

For the first time, Temple University researchers have used machine learning to rank the most important determinants of future affluence. Education and occupation were the best predictors — but surprisingly, a person’s ability to delay instant gratification was also among the most important determinants of higher income, beating age, race, ethnicity and height. Published in Frontiers in Psychology, the study suggests that interventions to improve this “delay discounting” could have literal payoffs in terms of higher income attainment.

Many factors are related to how much money a person will earn, including age, occupation, education, gender, ethnicity and even height. Behavioral variables are also implicated, such as one relating to the famous “marshmallow test.” This study of delay discounting, or how much a person discounts the value of future rewards compared to immediate ones, showed children with greater self-control were more likely to have higher salaries later in life.

But the study’s lead author, Dr William Hampton, now at the University of St. Gallen in Switzerland, says more traditional ways of analyzing data have been unable to indicate which of these factors are more important than others.

“All sorts of things predict income. We knew that this behavioral variable, delay discounting, was also predictive — but we were really curious how it would stack up against more common-sense predictors like education and age. Using machine learning, our study was the first to create a validated rank ordering of age, occupation, education, geographic location, gender, race, ethnicity, height, age and delay discounting in income prediction.”

Traditional methods used by psychologists (such as correlations and regression) haven’t allowed for a simultaneous comparison of different factors relating to an individual’s affluence. This study collected a large amount of data — from more than 2,500 diverse participants — and split them into a training set and a test set. The test set was put aside while the training set produced model results. The researchers then went back to the test set to test the accuracy of their findings.

Unsurprisingly, the models indicated that occupation and education were the best predictors of high income, followed by location (as determined by zip code) and gender — with males earning more than females. Delay discounting was the next most-important factor, being more predictive than age, race, ethnicity or height.

Dr Hampton hopes the research approach will be part of a new era in data analysis. “This was amazing because it allowed us to check our findings and replicate them, giving us much greater confidence that they were accurate. This is particularly important given the recent wave of findings across science that do not seem to replicate. Using this machine learning approach could lead to more research that replicates — and we hope this spurs the use of more sophisticated analytic approaches in general.”

The study’s authors caution that the data sample was purposely limited to the United States and it is possible that the rank order of variables that predict salary may differ in other countries. Dr Hampton says he is looking forward to exploring this analytical approach in a broader context.

“I would love to see a replication of this study in another culture. I also would be very interested in future studies aiming to reduce delay discounting. There is much debate about whether delay discounting is a stable trait or whether it is malleable — longitudinal studies could help settle that.”

Finally, Dr Hampton has an interesting observation for parents, “if you want your child to grow up to earn a good salary, consider instilling in them the importance of passing on smaller, immediate rewards in favor of larger ones that they have to wait for. This is probably easier said than done, as very few people naturally enjoy waiting, but our results suggest that those who develop the ability to delay gratification are likely investing in their own earning potential.”

Why I am still a bit puzzled if I’m a fan of this new approach, is that it could regarded as being pretty close to p-hacking. On the other hand it can be regarded as an interesting new way to do exploratory research.

Abstract of the study:

Income is a primary determinant of social mobility, career progression, and personal happiness. It has been shown to vary with demographic variables like age and education, with more oblique variables such as height, and with behaviors such as delay discounting, i.e., the propensity to devalue future rewards. However, the relative contribution of each these salary-linked variables to income is not known. Further, much of past research has often been underpowered, drawn from populations of convenience, and produced findings that have not always been replicated. Here we tested a large (n = 2,564), heterogeneous sample, and employed a novel analytic approach: using three machine learning algorithms to model the relationship between income and age, gender, height, race, zip code, education, occupation, and discounting. We found that delay discounting is more predictive of income than age, ethnicity, or height. We then used a holdout data set to test the robustness of our findings. We discuss the benefits of our methodological approach, as well as possible explanations and implications for the prominent relationship between delay discounting and income.


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Our learning capabilities are limited during slow wave sleep… (no, really)

It’s a myth we already discussed in our first book on myths about learning and education, but people keep dreaming of learning in our sleep.

This new study gives more insights about what is and isn’t possible: while the human brain is still able to perceive sounds during sleep, it is unable to group these sounds according to their organization in a sequence.

From the press release:

Hypnopedia, or the ability to learn during sleep, was popularized in the ’60s, with for example the dystopia Brave New World by Aldous Huxley, in which individuals are conditioned to their future tasks during sleep. This concept has been progressively abandoned due to a lack of reliable scientific evidence supporting in-sleep learning abilities.

Recently however, few studies showed that the acquisition of elementary associations such as stimulus-reflex response is possible during sleep, both in humans and in animals. Nevertheless, it is not clear if sleep allows for more sophisticated forms of learning.

A study published this August 6 in the journal Scientific Reportsby researchers from the ULB Neuroscience Institute (UNI) shows that while our brain is able to continue perceiving sounds during sleep like at wake, the ability to group these sounds according to their organization in a sequence is only present at wakefulness, and completely disappears during sleep.

Juliane Farthouat, while a Research Fellow of the FNRS under the direction of Philippe Peigneux, professor at the Faculty of Psychological Science and Education at Université libre de Bruxelles, ULB, used magnetoencephalography (MEG) to record the cerebral activity mirroring the statistical learning of series of sounds, both during slow wave sleep (a part of sleep during which brain activity is highly synchronized) and during wakefulness.

During sleep, participants were exposed to fast flows of pure sounds, either randomly organized or structured in such a way that the auditory stream could be statistically grouped into sets of 3 elements.

During sleep, brain MEG responses demonstrated preserved detection of isolated sounds, but no response reflecting statistical clustering.

During wakefulness, however, all participants presented brain MEG responses reflecting the grouping of sounds into sets of 3 elements.

The results of this study suggest intrinsic limitations in de novo learning during slow wave sleep, that might confine the sleeping brain’s learning capabilities to simple, elementary associations.

Abstract of the study:

Hypnopedia, or the capacity to learn during sleep, is debatable. De novo acquisition of reflex stimulus-response associations was shown possible both in man and animal. Whether sleep allows more sophisticated forms of learning remains unclear. We recorded during diurnal Non-Rapid Eye Movement (NREM) sleep auditory magnetoencephalographic (MEG) frequency-tagged responses mirroring ongoing statistical learning. While in NREM sleep, participants were exposed at non-awakenings thresholds to fast auditory streams of pure tones, either randomly organized or structured in such a way that the stream statistically segmented in sets of 3 elements (tritones). During NREM sleep, only tone-related frequency-tagged MEG responses were observed, evidencing successful perception of individual tones. No participant showed tritone-related frequency-tagged responses, suggesting lack of segmentation. In the ensuing wake period however, all participants exhibited robust tritone-related responses during exposure to statistical (but not random) streams. Our data suggest that associations embedded in statistical regularities remain undetected during NREM sleep, although implicitly learned during subsequent wakefulness. These results suggest intrinsic limitations in de novo learning during NREM sleep that might confine the NREM sleeping brain’s learning capabilities to simple, elementary associations. It remains to be ascertained whether it similarly applies to REM sleep.

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Are narcissist doing better in school?

This study is interesting, although I do think there is one element that makes me wonder for the rest of the paper:

But hey, maybe that could well be the reason for the reversed Flynn-effect?

From the press release:

Dr Kostas Papageorgiou, Director of the InteRRaCt lab in the School of Psychology at Queen’s University Belfast, has discovered that adolescents who score high on certain aspects of subclinical narcissism may be more mentally tough and can perform better at school.

The findings are the result of an international collaboration, which included Professor Yulia Kovas, Director of InLab at Goldsmiths University of London (UK); as well as leading experts from King’s College London, Manchester Metropolitan University, Huddersfield University and the University of Texas at Austin, USA.

In the study, 340 adolescent students, taking part in the Multi-Cohort Investigation into Learning and Educational Success study (MILES), were recruited from three different Italian high schools in the Milan Province. They took part in two assessment waves.

The research has been published in Personality and Individual Differences.

Dr Papageorgiou explains: “Narcissism is considered as a socially malevolent trait and it is part of the Dark Triad of personality traits — narcissism, psychopathy and Machiavellianism.

“Previous studies indicate that narcissism is a growing trend in our society but this does not necessarily mean that an individual who displays high narcissistic qualities has a personality disorder. In our research, we focused on subclinical or “normal” narcissism. Subclinical narcissism includes some of the same features of clinical syndrome — grandiosity, entitlement, dominance, and superiority.

“If you are a narcissist you believe strongly that you are better than anyone else and that you deserve reward. Being confident in your own abilities is one of the key signs of grandiose narcissism and is also at the core of mental toughness. If a person is mentally tough, they are likely to embrace challenges and see these as an opportunity for personal growth.”

Dr Papageorgiou’s research suggests that in some ways, narcissism might actually be a positive attribute. He says: “People who score high on subclinical narcissism may be at an advantage because their heightened sense of self-worth may mean they are more motivated, assertive, and successful in certain contexts.

“Previous research is our lab has shown that subclinical narcissism may increase mental toughness. If an individual scores high on mental toughness this means they can perform at their very best in pressured and diverse situations.

The research suggests that the relationship between narcissism and mental toughness could be one of the personality mechanisms that leads to variation in school achievement. However, at this stage, the findings have mainly theoretical rather than applied implications.

Dr Papageorgiou explains: “It is important that we reconsider how we, as a society, view narcissism. We perceive emotions or personality traits as being either bad or good but psychological traits are the products of evolution; they are neither bad nor good — they are adaptive or maladaptive. Perhaps we should expand conventional social morality to include and celebrate all expressions of human nature.”

Dr Papageorgiou is continuing this research and will explore if subclinical narcissism decreases symptoms of psychopathology through mental toughness.

Abstract of the study:

Mental toughness has been associated with optimal performance across diverse contexts including academic achievement. MT is positively associated with subclinical narcissism. Cross-sectional research reported that high narcissism may contribute indirectly to enhanced positive outcomes, through MT. This study is the first to explore longitudinally the development of the association between MT, narcissism and achievement in a sample of adolescents. MT correlated positively with narcissism and predicted a small percentage of the variation in school achievement. Narcissism did not correlate significantly with school achievement. However, subclinical narcissism exerted a significant positive indirect effect on school achievement through MT. The findings suggest that the relationship between narcissism and MT could be one of the non-cognitive mechanisms that underlie individual variation in school achievement.

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Follow your passion? Well, maybe that’s not the best advice…

It’s a study related to growth mindset, but before you start shouting ‘debunked‘ (check Dweck’s reply), the study is not about applying a growth mindset approach but all about how people think about passion being nature or nurture and the consequences of these views on giving up. And it seems people who think that passion is something magical placed in you (nature) will quit faster than people who think you can develop a passion for something.

From the press release:

As the world becomes increasingly interdisciplinary, having diverse interests can help people make important connections across fields, such as between the Arts and Sciences. A new study by Yale-NUS College Assistant Professor of Psychology Paul A O’Keefe and colleagues suggests that one’s belief about the nature of interests might prevent those insights from happening. Those who endorse a “fixed theory” about interest tend to think of it as something already there that simply needs to be found. Therefore, they are unlikely to stray beyond the interests they already have. By contrast, those with a “growth theory” tend to believe that interests can be developed and cultivated. The common advice to “find your passion” supports a fixed theory and may eventually be limiting.

Dr O’Keefe collaborated with Stanford University Professor Carol S Dweck, a psychologist known for her work in fixed and growth theories, as well as Associate Professor Gregory M Walton, also from Stanford. While fixed and growth theories about intelligence–beliefs about the malleability of intellectual abilities–have been heavily researched, applying this idea to people’s interests is a new area of investigation. The team’s research is forthcoming in Psychological Science, in which they examined the implications of fixed and growth theories of interest.

The research is of particular relevance to countries like Singapore, where students typically begin to specialise early in their education. Such early specialisation might discourage a growth theory by limiting the exploration of academic interests. However, since 2006, Singapore’s education system began requiring GCE A-level students to take at least one contrasting subject for admission into one of the six local autonomous universities. Research investigating a growth theory of interest will become more important in terms of understanding how to encourage students to explore new or different topics and value them more.

Across five studies, the team showed that a fixed theory, as compared to a growth theory, causes people to be less receptive to topics that are outside their existing interests. For example, in one study, the researchers recruited undergraduates with a well-established interest in either the Arts or the Sciences. Then, they had the students read two academic articles, one appealing to each of the two academic areas. Those led to endorse a fixed theory, as compared to a growth theory, reported less interest in the article outside of their established interest.

The researchers also found that fixed and growth theories influence one’s motivational expectations for pursuing their interests and passions. In one study, the researchers sparked students’ interest in astrophysics by having them watch a fun, animated video on the topic. Then, participants read a challenging academic article on the same topic. Those with a fixed theory reported losing more interest in the topic once engaging in it became difficult, as compared to those with a growth theory. This is because people with a fixed theory tend to expect that pursuing a newly discovered interest will be relatively easy, and might give up on it when engaging in it becomes difficult. They may come to believe that it was not a true interest after all.

The finding that a growth theory can make people more open to new interests, and that it can help sustain their interest despite difficulties, has important implications. Dr O’Keefe highlighted that in an increasingly complex and interconnected world, viewing interests as developable is important for encouraging innovation as new and interdisciplinary solutions are needed. Believing one’s interests are fixed might hinder exploration into other areas.

Instead of finding your passion, the researchers suggest that people should develop their passion.

“Encouraging people to develop their passion can not only promote a growth theory, but also suggests that it is an active process, not passive. A hidden positive implication of a growth theory is the expectation that pursuing one’s interests and passions will be difficult at times because people are less likely to give up on them when faced with a challenge,” Dr O’Keefe explained.

Dr O’Keefe is currently researching the impact of fixed and growth theories of interest in Singapore schools, as well as how teaching students to develop a growth theory can improve their learning and achievement.

Abstract of the study:

People are often told to find their passion as though passions and interests are pre-formed and must simply be discovered. This idea, however, has hidden motivational implications. Five studies examined implicit theories of interest—the idea that personal interests are relatively fixed (fixed theory) or developed (growth theory). Whether assessed or experimentally induced, a fixed theory was more likely to dampen interest in areas outside people’s existing interests (Studies 1–3). Those endorsing a fixed theory were also more likely to anticipate boundless motivation when passions were found, not anticipating possible difficulties (Study 4). Moreover, when engaging in a new interest became difficult, interest flagged significantly more for people induced to hold a fixed than a growth theory of interest (Study 5). Urging people to find their passion may lead them to put all their eggs in one basket but then to drop that basket when it becomes difficult to carry.

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Children have a nuanced understanding of fairness from a young age

This study reminded me of one of the many little experiments that Piaget did and while there has been many issues with the research of the infamous biologist and psychologist from Switzerland, the age group in this experiment isn’t that dissimilar…

From the press release:

New University of Michigan research indicates that children as young as 5 incorporate market concerns–the idea that what you get is in line with what you give or offer–into their decision making, and increasingly do so with age.

Some people think children are innately selfish–they want to get goodies for themselves. Other people think children are innately altruistic–they care about helping others. Most people think children are both.

“The trick is knowing when and how to balance self interest and concern for others–what is appropriate in different circumstances,” said lead author Margaret Echelbarger, a recent U-M psychology doctoral graduate.

By studying how children engage in different types of exchanges, researchers can discern the origins of these behaviors, as well as their developmental course.

“This in turn tells us a bit more about ourselves as adults,” Echelbarger said.

The U-M research included 195 children ages 5-10 and 60 adults helping a giver distribute stickers to friends. They distributed stickers equally between friends when offers were the same, but unequally when different offers were made.

There were times when the participants distributed more stickers to the friends offering more money, which meant children–as they aged–were willing to abandon equal norms for distribution. More specifically, older children distributed more stickers to friends who paid more even when the other friend wanted to pay but couldn’t.

“These findings are especially interesting in light of young children’s limited exposure to market/economic instruction,” Echelbarger said. “We show that, from a young age, children are developing an understanding of the ‘rules’ of market exchanges.”

Echelbarger and colleagues also found that children are sensitive to the reasons underlying the different offers. Children penalize recipients refusing to pay more than recipients willing but unable to pay, she said.

The findings, which appear in Child Development, are also consistent with prior research that children incorporate equity concerns, such as merit and need, into their distribution decisions.

Abstract of the study:

Children are sensitive to a number of considerations influencing distributions of resources, including equality, equity, and reciprocity. We tested whether children use a specific type of reciprocity norm—market norms—in which resources are distributed differentially based strictly on amount offered in return. In two studies, 195 children 5–10 years and 60 adults distributed stickers to friends offering same or different amounts of money. Overall, participants distributed more equally when offers were the same and more unequally when offers were different. Although sensitive to why friends offered different amounts of money, children increasingly incorporated market norms into their distributions with age, as the oldest children and adults distributed more to those offering more, irrespective of the reasons provided.

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Again: the power of forgetting

People who read my book or who saw a presentation probably know it already, but I’m a big fan of Ebbinghaus who described the forgetting curve in 1885. His influence on things such as spaced repetition – one of the most effective ways to remember stuff – is big. Spaced repetition already shows the power of forgetting, this announcement of a talk by Bjork, Robert A. that is, gives a good short overview:

Contextual clues play a role in what people are able to store and retrieve from their memory, says Robert A. Bjork, PhD, distinguished research professor in the department of psychology at the University of California, Los Angeles. A change in context can cause forgetting, but it can also change–and enrich–how information is encoded and retrieved, which can enhance learning. Bjork defines forgetting as “a decrease in how readily accessible some information or procedure is at a given point in time.” For example, some items may be strongly imprinted in our memories (referred to as “strong storage strength”)–such as a childhood phone number–but may be difficult to retrieve quickly due to the length of time since that information has been accessed (“weak retrieval strength”).


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Just don’t be a helicopter parent

We’ve seen before that being a tiger mom is not a good idea. But being a helicopter parent isn’t a good idea neither. A new study suggests that children with overcontrolling parents may later struggle to adjust in school and social environments.

From the press release:

“Our research showed that children with helicopter parents may be less able to deal with the challenging demands of growing up, especially with navigating the complex school environment,” said Nicole B. Perry, PhD, from the University of Minnesota, and lead author of the study. “Children who cannot regulate their emotions and behavior effectively are more likely to act out in the classroom, to have a harder time making friends and to struggle in school.”

Children rely on caregivers for guidance and understanding of their emotions. They need parents who are sensitive to their needs, who recognize when they are capable of managing a situation and who will guide them when emotional situations become too challenging. This helps children develop the ability to handle challenging situations on their own as they grow up, and leads to better mental and physical health, healthier social relationships and academic success. Managing emotions and behavior are fundamental skills that all children need to learn and overcontrolling parenting can limits those opportunities, according to Perry.

The researchers followed the same 422 children over the course of eight years and assessed them at ages 2, 5 and 10, as part of a study of social and emotional development. Children in the study were predominantly white and African-American and from economically diverse backgrounds. Data were collected from observations of parent-child interactions, teacher-reported responses and self-reports from the 10-year-olds.

During the observations, the research team asked the parents and children to play as they would at home.

“Helicopter parenting behavior we saw included parents constantly guiding their child by telling him or her what to play with, how to play with a toy, how to clean up after playtime and being too strict or demanding,” said Perry. “The kids reacted in a variety of ways. Some became defiant, others were apathetic and some showed frustration.”

Overcontrolling parenting when a child was 2 was associated with poorer emotional and behavioral regulation at age 5, the researchers found. Conversely, the greater a child’s emotional regulation at age 5, the less likely he or she was to have emotional problems and the more likely he or she was to have better social skills and be more productive in school at age 10. Similarly, by age 10, children with better impulse control were less likely to experience emotional and social problems and were more likely to do better in school.

“Children who developed the ability to effectively calm themselves during distressing situations and to conduct themselves appropriately had an easier time adjusting to the increasingly difficult demands of preadolescent school environments,” said Perry. “Our findings underscore the importance of educating often well-intentioned parents about supporting children’s autonomy with handling emotional challenges.”

Perry suggested that parents can help their children learn to control their emotions and behavior by talking with them about how to understand their feelings and by explaining what behaviors may result from feeling certain emotions, as well as the consequences of different responses. Then parents can help their children identify positive coping strategies, like deep breathing, listening to music, coloring or retreating to a quiet space.

“Parents can also set good examples for their children by using positive coping strategies to manage their own emotions and behavior when upset,” said Perry.

Abstract of the study:

We examined longitudinal associations across an 8-year time span between overcontrolling parenting during toddlerhood, self-regulation during early childhood, and social, emotional, and academic adjustment in preadolescence (N 422). Overcontrolling parenting, emotion regulation (ER), and inhibitory control (IC) were observed in the laboratory; preadolescent adjustment was teacher-reported and child self-reported. Results from path analysis indicated that overcontrolling parenting at age 2 was associated negatively with ER and IC at age 5, which, in turn, were associated with more child-reported emotional and school problems, fewer teacher-reported social skills, and less teacher-reported academic productivity at age 10. These effects held even when controlling for prior levels of adjustment at age 5, suggesting that ER and IC in early childhood may be associated with increases and decreases in social, emotional, and academic functioning from childhood to preadolescence. Finally, indirect effects from overcontrolling parenting at age 2 to preadolescent outcomes at age 10 were significant, both through IC and ER at age 5. These results support the notion that parenting during toddlerhood is associated with child adjustment into adolescence through its relation with early developing self-regulatory skills.

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