Category Archives: Psychology

Drawing better than writing for memory retention (for older people for sure)

This study doesn’t surprise me that much. Everybody who read about dual coding knows how drawing can help retention. This study adds to this knowledge by showing that older adults who take up drawing could enhance their memory. Do note while the 3 experiments are interesting and relevant, they only used 3 times 2 groups of 24 participants (24 students, 24 older people).

From the press release:

Researchers from the University of Waterloo found that even if people weren’t good at it, drawing, as a method to help retain new information, was better than re-writing notes, visualization exercises or passively looking at images.

“We found that drawing enhanced memory in older adults more than other known study techniques,” said Melissa Meade, PhD candidate in cognitive neuroscience at Waterloo. “We’re really encouraged by these results and are looking into ways that it can be used to help people with dementia, who experience rapid declines in memory and language function.”

As part of a series of studies, the researchers asked both young people and older adults to do a variety of memory-encoding techniques and then tested their recall. Meade conducted this study with Myra Fernandes a Psychology professor in cognitive neuroscience at Waterloo and recent UW PhD graduate Jeffrey Wammes.

The researchers believe that drawing led to better memory when compared with other study techniques because it incorporated multiple ways of representing the information–visual, spatial, verbal, semantic and motoric.

“Drawing improves memory across a variety of tasks and populations, and the simplicity of the strategy means that it can be used in many settings,” said Myra Fernandes.

As part of the studies, the researchers compared different types of memory techniques in aiding retention of a set of words, in a group of undergraduate students and a group of senior citizens. Participants would either encode each word by writing it out, by drawing it, or by listing physical attributes related to each item. Later on after performing each task, memory was assessed. Both groups showed better retention when they used drawing rather than writing to encode the new information, and this effect was especially large in older adults.

Retention of new information typically declines as people age, due to deterioration of critical brain structures involved in memory such as the hippocampus and frontal lobes. In contrast, we know that visuospatial processing regions of the brain, involved in representing images and pictures, are mostly intact in normal aging, and in dementia. “We think that drawing is particularly relevant for people with dementia because it makes better use of brain regions that are still preserved, and could help people experiencing cognitive impairment with memory function,” said Meade. “Our findings have exciting implications for therapeutic interventions to help dementia patients hold on to valuable episodic memories throughout the progression of their disease”

Abstract of the study:

Background/Study Context. In a recent study, drawing pictures relative to writing words at encoding has been shown to benefit later memory performance in young adults. In the current study, we sought to test whether older adults’ memory might also benefit from drawing as an encoding strategy. Our prediction was that drawing would serve as a particularly effective form of environmental support at encoding as it encourages a more detailed perceptual representation.

Methods. Participants were presented 30 nouns, one at a time, and asked to either draw a picture or repeatedly write out the word, which was followed by a free recall test for all words (Experiment 1). In Experiment 2, we added an elaborative processing task in which we asked participants to list physical characteristics of the objects. In Experiment 3, we probed recognition memory for the words.

Results. Of the words recalled in Experiment 1, a larger proportion had been drawn than written at encoding, and this effect was larger in older relative to younger adults. In Experiment 2, we demonstrated that drawing improves memory in both younger and older adults more than does an elaborative encoding task consisting of listing descriptive characteristics of the target nouns. In Experiment 3, older and younger adults drew or wrote out words at encoding, and subsequently provided Remember-Know-New recognition memory decisions. We showed that drawing reduced age-related differences in Remember responses.

Conclusions. We suggest that incorporating visuo-perceptual information into the memory trace, by drawing pictures at study, increases reliance of the memory trace on visual sensory regions, which are relatively intact in normal aging, relative to simply writing out or elaborately encoding words. Overall, results indicate that drawing is a highly valuable form of environmental support that can significantly enhance memory performance in older adults.

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About nature versus nurture: the four laws of behavioural genetics

This tweet by Steve Stewart-Williams is so relevant I wanted to share it here on this blog as I know a lot of people who follow my posts aren’t on Twitter.

If you feel angry after reading the first two laws, do read on. Both articles mentioned in the tweet are also must reads.

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What if a failed replication… somehow fails to replicate?

Have I told you already that science can be messy? If not, welcome to this blog! 2 years ago I posted this replication of the infamous pencil in the mouth study. It has become one of the more well known examples of the replication crisis. But it also spurred a lot of debate. Was the replication really a true replication of the original research?

A new study adds fuel to this debate as it failed to replicate the failed replication. Ok, just kidding, the study is actually showing the original study might have been correct! But we can’t be really sure, as it’s actually even more complicated:

The paradigm diverged from the original facial feedback experiment in several respects. They include the classroom setting in which testing was conducted; the fact that each participant rated two cartoons rather than four; the fact that it featured a within-subjects rather than between- subjects design; the absence of a cover story about piloting a study for future research regarding populations with disabilities to explain the manipulation; the use of a 7-point scale rather than a 10-point scale; the fact that the experiment was part of a classroom lecture about learning (specifically, about the acquisition of conditioned associations) rather than following a line- drawing task; the fact that correct positioning of pens could be monitored only within the limits of a group setting; the fact that participants selected but did not write down their ratings with their pens in their mouths; and the lack of individualized follow-up with participants regarding their beliefs about the experiment, precluding exclusion of participants for suspicions regarding the study goals. (It is notable, however, that when the instructor presented students with their results in the ensuing class, the most commonly verbalized reaction was surprise or disbelief that the manipulation could have possibly affected their ratings.)

It seems the only thing that we seem to know for sure is that more research is needed…

Abstract of this new study:

The facial feedback effect refers to the influence of unobtrusive manipulations of facial behavior on emotional outcomes. That manipulations inducing or inhibiting smiling can shape positive affect and evaluations is a staple of undergraduate psychology curricula and supports theories of embodied emotion. Thus, the results of a Registered Replication Report indicating minimal evidence to support the facial feedback effect were widely viewed as cause for concern regarding the reliability of this effect. However, it has been suggested that features of the design of the replication studies may have influenced the study results. Relevant to these concerns are experimental facial feedback data collected from over 400 undergraduates over the course of 9 semesters. Circumstances of data collection met several criteria broadly recommended for testing the effect, including limited prior exposure to the facial feedback hypothesis, conditions minimally likely to induce self-focused attention, and the use of moderately funny contemporary cartoons as stimuli. Results yielded robust evidence in favor of the facial feedback hypothesis. Cartoons that participants evaluated while holding a pen or pencil in their teeth (smiling induction) were rated as funnier than cartoons they evaluated while holding a pen or pencil in their lips (smiling inhibition). The magnitude of the effect overlapped with original reports. Findings demonstrate that the facial feedback effect can be successfully replicated in a classroom setting and are in line with theories of emotional embodiment, according to which internal emotional states and relevant external emotional behaviors exert mutual influence on one another. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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A very interesting debate on knowledge and 21st Century Learners

I was reminded of this debate today and is still very relevant:

With endless amounts of information available at the touch of a button or click of a cursor, too many of today’s students are operating with what E.D. Hirsch calls a knowledge deficit. Watch the Debate Chamber at GESF 2017 as the House argues that facts are the building blocks upon which critical thinking and personal development skills are established and the mastery of facts will ensure students are prepared to thrive in the 21st Century. @GESForum #GESF

Speakers:
Mr Nick Ferrari, Broadcaster & Journalist, Global Radio | Mr Nick Gibb, Minister of School Standards, Department for Education | Ms Daisy Christodoulou, Head of Assessment , Ark Schools | Mr Andreas Schleicher, Director for the Directorate of Education and Skills, OECD | Mr Gabriel Sanchez Zinny, Executive Director, Instituto Nacional de Educación Tecnológica Argentina

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What do you need to succeed in life?

The answer is of course sheer luck, besides talent and intelligence. This new systematically review doesn’t say intelligence and talent aren’t needed, but suggests that non-cognitive skills can also be important, although there are also some serious warning lights surrounding the existing body of evidence.

From the press release:

The study, published in the journal Nature Human Behaviour is the first to systematically review the entire literature on effects of non-cognitive skills in children aged 12 or under, on later outcomes in their lives such as academic achievement, and cognitive and language ability.

“Traits such as attention, self-regulation, and perseverance in childhood have been investigated by psychologists, economists, and epidemiologists, and some have been shown to influence later life outcomes,” says Professor John Lynch, School of Public Health, University of Adelaide and senior author of the study.

“There is a wide range of existing evidence under-pinning the role of non-cognitive skills and how they affect success in later life but it’s far from consistent,” he says.

One of the study’s co-authors, Associate Professor Lisa Smithers, School of Public Health, University of Adelaide says: “There is tentative evidence from published studies that non-cognitive skills are associated with academic achievement, psychosocial, and cognitive and language outcomes, but cognitive skills are still important.”

One of the strongest findings of their systematic review was that the quality of evidence in this field is lower than desirable. Of over 550 eligible studies, only about 40% were judged to be of sufficient quality.

“So, while interventions to build non-cognitive skills may be important, particularly for disadvantaged children, the existing evidence base underpinning this field has the potential for publication bias and needs to have larger studies that are more rigorously designed. That has important implications for researchers and funding agencies who wish to study effects of non-cognitive skills,” says Professor Lynch.

Abstract of the study:

Success in school and the labour market relies on more than high intelligence. Associations between ‘non-cognitive’ skills in childhood, such as attention, self-regulation and perseverance, and later outcomes have been widely investigated. In a systematic review of this literature, we screened 9,553 publications, reviewed 554 eligible publications and interpreted results from 222 better-quality publications. Better-quality publications comprised randomized experimental and quasi-experimental intervention studies (EQIs) and observational studies that made reasonable attempts to control confounding. For academic achievement outcomes, there were 26 EQI publications but only 14 were available for meta-analysis, with effects ranging from 0.16 to 0.37 s.d. However, within subdomains, effects were heterogeneous. The 95% prediction interval for literacy was consistent with negative, null and positive effects (−0.13 to 0.79). Similarly, heterogeneous findings were observed for psychosocial, cognitive and language, and health outcomes. Funnel plots of EQIs and observational studies showed asymmetric distributions and potential for small study bias. There is some evidence that non-cognitive skills associate with improved outcomes. However, there is potential for small study and publication bias that may overestimate true effects, and the heterogeneity of effect estimates spanned negative, null and positive effects. The quality of evidence from EQIs underpinning this field is lower than optimal and more than one-third of observational studies made little or no attempt to control confounding. Interventions designed to develop children’s non-cognitive skills could potentially improve outcomes. The interdisciplinary researchers interested in these skills should take a more strategic and rigorous approach to determine which interventions are most effective.

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A dissection of Howard Gardner’s Frames

This Twitter-rant is too good not to share here (H/T Tim van der Zee):

 

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Are the youngest in class more likely to be diagnosed with ADHD? (Best Evidence in Brief)

There is a new Best Evidence in Brief with among others, this study:

Findings from a study published in the Journal of Child Psychology and Psychiatry suggest that children who are the youngest in their classroom are more likely to be diagnosed with attention deficit hyperactivity disorder (ADHD) than their older classmates.
Martin Whitely and colleagues conducted a systematic review of 22 studies that examined the relationship between a child’s age relative to their classmates and their chances of being diagnosed with, or medicated for, ADHD. Seventeen studies (with a total of more than 14 million children) found that it was more common for the youngest children in a school year to be diagnosed as ADHD than their older classmates. This effect was found for both countries that have a high diagnosis rate, like the USA, Canada and Iceland, and countries where diagnosis is less common, like Finland and Sweden.
The researchers suggest that some teachers may be mistaking normal age-related immaturity of the youngest children in their class for ADHD, and that these findings highlight the importance of being aware of the impact of relative age and give the youngest children in class the extra time they may need to mature.

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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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