All posts by Gemma

A Summary: Working Memory Differences between Children Living in Rural and Urban Poverty – Michele Tine

Working memory (WM) is the process by which we transiently hold and process information required for obtaining a short-term goal. It can be broadly considered to have two main components, verbal WM and visuospatial WM. WM is essential to everyday life, for example, it is used when remembering your friends’ order in a coffee shop or adding up the correct change to pay the man at the checkout counter. WM is essential to higher-processing and cognition including decision-making, learning, complex problem-solving, strategic thinking and sustained attention. In recent years, research has indicated that children from a low socioeconomic background display deficits in WM capacity when compared with children from a high socioeconomic background. (For further reading on this see: Fernald, Weber, Galasso and Ratisfandrihamanana, 2011; Noble, Norman and Farah, 2005.) With the knowledge that a lower WM capacity impacts on attention and learning this finding is of particular significance when considering children’s education. It has been shown that children with a low WM capacity can exhibit poor attention in class, poorer academic performance, learning difficulties and more difficult behaviours. (See our previous post for more insight into the relationship between WM and ADHD https://forgingconnectionsblog.wordpress.com/2015/02/05/adhd-and-low-working-memory-a-comparison/ ).

Given that a low socioeconomic background can impact on WM in children, it is important to explore why this occurs and which socioeconomic factors are the largest contributors. The following paper, by Michele Tine, (http://www.tandfonline.com/doi/full/10.1080/15248372.2013.797906#tabModule) considers how coming from a low-income background can impact on working memory (WM) in children whilst distinguishing between participants based on their ‘developmental context’ (settlement area), either rural or urban. Whilst it has been established that low-income children have deficits in verbal and visuospatial WM when compared with their high-income counterparts, this paper is the first, to my knowledge, that considers whether poverty is associated with a greater deficit in verbal or visuospatial WM and how settlement area influences this relationship.

Considering settlement area is a good way of determining if environmental factors mediate the relationship between low-income and decreased WM capacity. Whilst each settlement area will be unique in it is own way we can assume that some features are more prominent in one environment than another. For example, stress is thought to be a highly important factor when considering the impacts of socioeconomic background on an individual. Grouping via settlement area, takes into account exposure to different stressors in different environments.

Some of the differing stressors identified in the paper for those from a low-socioeconomic  background  include:

For an URBAN environment For a RURAL environment
Substandard and crowded housing Isolation from people
Excessive noise Isolation due to reduced access to technologies the internet and reduced computer ownership
Inadequate health care, education and inadequate health care Isolation from institutions and services
Increased crime rate Decreased social support*
Increased physiological disorders Less everyday visual stimulation
Increased divorce rates
Exposure to chronic aircraft noise

* Social support is a known buffer for stressors

Prior research has determined that the relationship between socioeconomic status and visuospatial WM is ‘fully mediated by allostatic load’, where allostatic load is the strain on the individual that accumulates over time with chronic stress. Thus, we would expect that the higher the amount of stress experienced, the greater the reduction in visuospatial WM capacity. On this basis we would perhaps expect there to be a difference in WM deficits once settlement area is considered as a result of exposure to differing stressors. Whilst the relationship between stress, socioeconomic status and visuospatial WM has been investigated, there has been little definitive research into how this compares to the relationship between stress, socioeconomic status and verbal WM.

 The individuals that participated in this study were recruited via their schools and their income measures (high or low) were determined by the following thresholds:

Low-income:

The individual attends a school that ‘serves a community with a median family income below the national median family income’. The school must have >75% of students qualified for free or reduced school meals and the individual themselves must qualify for free school meals.

High-income:

The individual attends a school that ‘serves a community with a median family income above the national median family income’. The school must have <25% of students qualified for free or reduced school meals and the individual must not qualify for reduced or free school meals.

To determine if an area was rural or urban The US Census Bureau (2010) and McCracken and Barcina’s (1991) work was utilised along with population per county and the average number of students per class in the county.

With these thresholds, four groups of individuals could be defined:

1) Urban and high-income

2) Rural and high-income

3) Urban and low-income

4) Rural and low-income

There were no significant differences in gender between the four groups but there was, however, a significant difference in ethnicity distribution across the groups.

Each individual completed four WM tasks from the Automated Working Memory Assessment battery. Two of the subtests measured verbal WM and two measured visuospatial WM.  As expected, the low-income children were found to have significantly lower composite WM percentile scores than the high-income students and there were no differences between the urban high-income and the rural high-income groups. The urban low-income group had significantly lower WM scores than both the high-income groups and the rural low-income group showed the same trend, having significantly lower WM scores than both of the high-income groups.

However, a comparison of the two low-income groups showed that they had differing WM profiles. The rural low-income group had a significantly higher verbal WM profile than the urban low-income group but also a significantly lower visuospatial WM profile than the urban low-income group. The urban low-income group showed no significant differences across the verbal and visuospatial WM scores, whilst the rural low-income group had significantly different verbal and visuospatial WM scores.

Based on past literature, Tine speculates that the differences in verbal WM between the two low-income groups may be accounted for by chronic noise pollution amongst the low-income urban population, for example through living close to airports and train tracks. However, the reason for this difference and for the decreased visuospatial WM capacity in the rural low-income group needs further investigation before any conclusion can be drawn.

Whilst this paper presents solid and much needed research into how income and settlement area/developmental context impacts on WM capacity in children, the author states that caution must be taken in inferring any causal relationships from studies such as this one. Due to a lack of information on the participants’ parents, although unlikely, it is possible that ‘low-income parents (and in turn, children) with particularly weak verbal WM abilities tend to seek out urban low-income areas, or on the contrary, low-income parents (and children) with relatively weak visuospatial WM capacity self-select into rural low-income areas.

Additionally, as previously mentioned, there was a significant difference in racial distribution across the groups and thus it cannot be ruled out that some of the differences seen in the WM profiles are a result of cultural differences and the ‘experience of race-based stereotype threat’ which has been shown to reduce WM capacity (Schmader and Johns, 2003). The study also does not consider language ability amongst the populations or the proportion of non-native English speakers, which is also known to be related to WM ability. Whilst the insights this paper provides are unique, the research could be furthered still by taking into account factors other than income and developmental context, for example, maternal education, parental employment or family structure, other well recognised and substantial components of what it means to be from a low-socioeconomic background.

So, in conclusion, whist we cannot assume causality from the findings, this paper presents, in my opinion, a very good first step into developing a much deeper understanding of the impact of SES on cognitive ability in children.  The hope is that this kind of research will eventually allow us to develop a successful intervention to aid those children from disadvantaged backgrounds so that they do not experience the cognitive deficits and disadvantages in education that their high-socioeconomic peers evade.

The full paper can be accessed here: http://www.tandfonline.com/doi/full/10.1080/15248372.2013.797906#tabModule)

References

Noble, K.G. Norman, M.F. and Farah, M.J. (2005) Neurocognitive correlates of socioeconomic status in kindergarten children. Developmental Science, 8, 74-87.

Fernald, L.C.H. Weber, A. Galasso, E. and Ratisfandrihamanana, L. (2011) Socioeconomic gradients and child development in a very low income population: Evidence from Madagascar. Developmental Science, 14, 832-847.

McCracken, J.D. and Barcinas, J.D. (1991) Differences between rural and urban schools, student characteristics, and student aspirations in Ohio. Journal or Research in Rural Education, 7, 29-40.

Schmader, T. and Johns, M. (2003) Converging evidence that stereotype threat reduces working memory capacity. Journal of Personality and Social Psychology, 85, 440-452.

ADHD and Low Working Memory: A comparison

I was recently directed to a paper titled ‘Children with low working memory and children with ADHD: same or different?’ From the title alone this paper piqued my curiosity. As scientists we often focus on collecting large amounts of data, and in some cases forget the importance of forming new theories and ideas. For me, this paper casts the profiles of an ADHD diagnosis and a deficit in working memory, a common childhood issue, in a new light. What does it mean (if anything) to have a clinical diagnosis? And what is the best way of characterising children with problems of attention and memory?

Whilst I will summarise the main findings below, for those that want to read the paper for themselves it can be accessed here:

http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00976/full

This paper provides a novel comparison of cognitive skills, executive function, educational attainment and behaviour between those with a low WM and those with a diagnosis of ADHD. It utilised three groups of children (8-11yrs): one group identified by routine screening as having a low WM, one group with diagnoses of ADHD and one group of typically developing children.

Age was controlled for across the groups and for each group the following measures were taken: Working memory (Automated working memory assessment); Executive function (Delis-Kaplan Executive Function System, The K-test of the Continuous Performance Test and Walk/Don’t Walk from the Test of Everyday Attention for Children); Academic ability (Wechsler reading, spelling, reading comprehension, mathematical reasoning and number operations and the four WASI subtests); And classroom behaviour problems (Connors teacher rating and the Behaviour rating inventory of executive function). Those children taking medication for their ADHD stopped taking it at least 24hrs prior to the session to ensure the effects of the drugs were eliminated by the time of testing.

The theory that makes up the basis for the research, includes the fact that both ADHD and low WM in childhood are significantly associated with poor educational attainment. It has also been established in the literature that there are associations between poor WM and inattentive behaviour and poor WM and executive function deficits. These elements combined suggest a high degree of overlap between the profiles of those with a deficit in WM and those with an ADHD diagnosis. In ADHD, we see executive function problems (deficits in response inhibition, attentional switching, planning and sustained attention) and also excessively high levels of motor activity and impulsive behaviour.

Some have argued that the executive function deficits in ADHD are a result of an underlying low WM, whilst others have proposed that ADHD involves two distinct neurodevelopmental systems:

1) a ‘cool/cognitive system’ that affects executive functions and WM

and

2) a ‘hot/affective system’ that leads to ‘aversion to delay that manifests as impulsive behaviour’.

It is this impulsive behaviour that manifests as excessive motor activity and problems in impulse control that are core characteristics of ADHD and are not associated with low WM. Thus, this study hypothesised that the two groups share a common deficit in the ‘cool executive function system’ reflected in the fact that both profiles exhibit lack of attentional control, but only those with the ADHD diagnosis have impairments in the ‘hot executive function system’ that results in hyperactivity and impulsivity.

The ADHD and low WM group, as expected, performed more poorly than the typically developing children on the tests of working memory (necessarily so for the latter group, because they were selected on this basis). Performance was significantly decreased for the low WM group in the two WM subtests that they were initially screened on. This suggests a selection artefact as ‘the two groups did not differ on the verbal WM task that was not used at screening’. In the executive function switching task no significant difference was seen between the low WM and ADHD group when considering accuracy, however, the low WM group showed a significantly slower performance than the ADHD and typical group. The sustained attention executive function task showed that both the ADHD and low WM children were significantly less accurate than the typically developing group and that they both made a significantly greater number of omissions than the typically developing group. The ADHD group also further made a significantly greater number of commission errors than the other two groups. No other significant differences were seen between the low WM group and the ADHD group on any other test of executive function. It is also worth noting that although there are a number of different models proposed for the functional structure of WM, the findings of this paper remain consistent across models on the basis that the ‘storage-only capacities of short-term memory (STM) and the capacity-limited attentional control functions of WM can be distinguished’.

They concluded that the ADHD and low WM groups have very similar profiles of WM ability and executive function impairment. They did however find two important differences: the low WM group were ‘slower to respond on several tasks’ and the ADHD group were ‘more hyperactive and exhibited more difficulties in controlling impulsivity in sustained attention’.  The processing speed impairments in the low WM group were an unexpected finding. They state that ‘it does not appear to be a part of a broader problem in fluid intelligence, as controlling statistically for performance IQ had little impact’. They present the idea that the impairment in response time may be a result of sluggish cognitive tempo (SCT). Sluggish cognitive tempo is a ‘set of symptoms strongly associated with the predominantly inattentive form of ADHD that includes high levels of daydreaming, slow response times, poor mental alertness and hypoactivity.’ It could perhaps be the case that those with a low WM may correspond to those with SCT, a predominantly inattentive subtype of ADHD. At present SCT is not a commonly utilised diagnosis in the UK. The more hyperactive behaviour in the ADHD children however was expected. ‘They violated rules more frequently during a planning task, and made more commission errors on the Continuous Performance Test of sustained attention’. This elevated hyperactivity was also shown in the Connor’s teacher rating as increased impulsivity and oppositional behaviour.

Interestingly they found the two groups to have equivalently high levels of inattentive behaviour. Whilst it has been shown before that children with low WM often exhibit inattentiveness, this paper is the first to show that this inattentive behaviour is of a very similar degree to that of  children with ADHD. In terms of the deficits in working memory, both groups exhibited deficits in the same regions and of a similar magnitude. They had impairment in ‘visuospatial short term memory, verbal working memory and visuo-spatial working memory’; but ‘their verbal short term memory scores fell within the typical age range’. The novel finding here is that there is such a high degree of correspondence between the profile and severity of the WM impairments in the two groups.

Previous research has also suggested a close relationship between ‘controlled cognitive attention and working memory’. This paper takes this understanding one step further suggesting that it is now looking more likely that there is a ‘link between poor working memory and overt inattentive and distractible behaviours’ as seen through the inattentiveness in the behaviour of those children with a low WM. In addition to being comparable in inattentiveness and working memory impairment, the two groups both exhibited ‘high rates of problem behaviours across a wide range of executive function behaviours’. Previous studies have also indicated that problem behaviours associated with executive function are seen in children with a low WM. This paper supports and extends this idea by showing that both groups ‘performed poorly on direct measures of switching, inhibition, sorting, planning, sustained attention and response suppression’.

When considering again, the cool (cognitively based) and the hot (affective) model of executive function, the pattern found in the results from this study suggest that there is some shared general executive deficit between the ADHD and low WM groups. The cool deficits manifest as inattention and low WM in both groups whilst the hot deficits, only present in the ADHD group, manifests as the hyperactivity. The additional delay in response times in the low WM group is the only component that does not fit with this theory, but the authors have identified that it ‘may be symptomatic of a subgroup of children with the predominantly inattentive form of ADHD who are characterised by SCT’.

In conclusion, this paper shows some very interesting preliminary findings into two conditions that are generally considered distinct. Both groups showed equal levels of underachievement when IQ differences were taken into account (‘they are indistinguishable in terms of their poor learning progress in mathematics and reading’). This is of great significance as those with a diagnosis of ADHD will almost certainly have learning support provided to them whilst those with a low WM will not. The educational needs of the group of children with a low WM are being overlooked as they do not show the disruptive hyperactivity of ADHD, even though in many respects the groups are indistinguishable.

Holmes J, Hilton KA, Place M, Alloway TP, Elliott JG and Gathercole SE (2014) Children with low working memory and children with ADHD: same or different? Front. Hum. Neurosci. 8:976. doi: 10.3389/fnhum.2014.00976

State of the Nation Report: Linking Research to Policy

We have recently started a new project focused around children that are considered to be “at risk” of poor educational attainment due to their socioeconomic status. Indexes of socioeconomic status include complex measures of interrelated components, with one key component being family income. As a result, socioeconomic status gives an index of poverty that incorporates other forms of deprivation. There is, at present, an increasing effort to provide better links between research on socioeconomic status, its impact on cognition, and government policy. However, when considering educational attainment, there is still little being done to support children from a lower socioeconomic background. The recently released State of the Nation Report [1] indicates the need for further research into this area. It calls for increased interest and action in order to help close the performance gap between children from different socioeconomic backgrounds and reduce the effects of poverty on children.

Whilst the UK is beginning to recover from the economic crisis, social recovery is lagging behind. For example, a family is described as being in relative poverty when the level of family income is less than 60% of the median UK family income. The State of the Nation report from the Social Mobility and Child Poverty Commission states that, before consideration for housing costs, 17% of children in the UK are living in in a state of relative poverty. After housing costs, 27% are living in relative poverty – that is 3.7 million children. Additionally, the report gives values for absolute child poverty, which are also still worryingly high despite government interventions. The House of Commons Scottish Affairs Committee defined absolute poverty as “the lack of sufficient resources with which to keep body and soul together”. [2]

The State of the Nation Report calls for the UK government to re-think their existing approach to child poverty if they wish to achieve the targets set out in the “2020 challenge” to “reduce child poverty by half and prevent Britain becoming a permanently divided society.” The knock-on implications of poverty on educational attainment are well noted in the scientific literature. In 2013, 38.5% of children receiving free school meals, which is a commonly used indicator for poverty, achieved an A*- C grade in Maths and English, compared to 65.3% of children not receiving free school meals. In addition, research published by the Social Mobility and Child Poverty Commission has shown that children from deprived backgrounds that are considered to be high achieving aged 7 fall behind children from the most affluent backgrounds that were considered to be low achieving at the age of 7. This crossing over in attainment levels is seen by 14-16 years of age (see Figure 1) [3]. This pattern of crossing over indicates that the environmental components of socioeconomic status have an impact on a child’s potential for academic attainment above and beyond genetic influences. Since 2005, the attainment gap in education between children from a low socioeconomic background and children from a high socioeconomic background has only closed by 1.6%. The implication is that those children with a low level of educational attainment are 4 times more likely to remain in poverty.

Figure 1. Trajectories from key stage 1 to key stage 4 by early achievement (defined using key stage 1 writing) for the most deprived and least deprived quintiles of socioeconomic status (state school only)SES attainment crossover_KS1 writing

It is well known that working protects against poverty. Children in working households are only a third as likely to suffer from poverty as those in workless households. However, the report also highlights that working is simply not enough to prevent childhood poverty. In 2012/2013 62% of children deemed to be living in poverty lived in a household where at least one person worked. And perhaps most concerning is that if trends continue, “2010-2020 is set to be the first decade with a rise in absolute poverty since records began in the early 1960s.”

With all this considered, further research is needed to provide a clearer picture of how socioeconomic status influences cognition and education. A key focus of this should involve identification of the factors within socioeconomic status and cognition that mark children as being more susceptible to the risk of poor educational attainment. In turn, greater resources need to be devoted to identifying the most effective interventions. These studies are difficult and complicated to conduct, but as The State of the Nation Report identifies, improving the educational prospects of children growing up in poverty provides one of the best mechanisms for creating a more equal society.

In conclusion, although the issues surrounding the socioeconomic impact on education have in part been researched and evaluated, there is yet to be a thorough exploration of all the factors involved.  Further investigation is needed to identify which cognitive, neural and environmental measures provide the most prominent markers of risk and resilience for children growing up in poverty. Research along these lines is sorely needed if government policy is to target those most in need of support.

[1] https://www.gov.uk/government/publications/state-of-the-nation-2014-report

[2] http://www.bbc.co.uk/news/uk-politics-29686628

[3]https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/324501/High_attainers_progress_report_final.pdf