Category Archives: Education and Poverty

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.

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