What is the Difference between 'Good' and 'Bad' Stress? Understanding Possible Effects of Socio-economic Status on Learning, 2016-2018

Wass, Samuel (2021). What is the Difference between 'Good' and 'Bad' Stress? Understanding Possible Effects of Socio-economic Status on Learning, 2016-2018. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-854793

Stress energizes learning. The Autonomic Nervous System (ANS), which is the pattern of nerves running through the body that enacts the body's stress response, acts to maintain a state of anticipatory readiness - one in which we are alert and ready to receive new information. Information presented during this alert state is subsequently better retained.

For my recent research, hosted at the Medical Research Council Cognition and Brain Sciences Unit in Cambridge, I have been leading a small research unit to study stress and learning in typical, middle-class young children. Our research has focused on exploring these 'good' aspects of stress. We have shown, for the first time, that children who show a larger spontaneous response to an experimentally presented stressor are also better at learning pictures of other children's faces. Using new, advanced analytical techniques based on time-series analyses, we have also shown that children who show more spontaneous, sudden fluctuations in stress levels show better learning.

However, there is also a down-side to stress. This is shown most markedly in individuals from low socio-economic status (SES) backgrounds. A number of recent studies have concluded that the associations widely observed between low SES and poor academic performance may be entirely attributable to the fact that individuals from low SES backgrounds tend to experience more frequent, and intense, stressful early life events. Although the exact mechanisms are unknown, it is thought that increased stress during early life associates with a poorer ability to concentrate, and therefore to learn.

So how to reconcile these 'good' and 'bad' aspects of stress during early development? Understanding this question is vital - both for understanding the mechanisms that disrupt early learning in high-risk individuals, and for developing new techniques to improve learning across all children. Yet remarkably little previous research has recorded whether different individuals are exposed to different levels of external, environmental noise during early development - nor investigated how these associate with differences in their internal stress reactivity. Under this Fellowship, I would use recently developed technologies to do this for the first time.

To address these questions, I shall take a cohort of infants from mixed socio-economic status backgrounds, recruited at birth in East London, and quantitatively track how attention, learning, ANS activity and external environmental stressors vary during early life. Using cutting-edge new technologies I shall examine whether children differ in the total amount of environmental noise to which they are exposed - and whether relationships can be found between how much noise and individual is exposed to, and how well they perform on attention and learning.

To mentor me on this project I have been fortunate to secure the support of three leading international scientists. Professor Cynthia Fu, based at the University of East London, will assist me in setting up the recruitment of children from mixed SES backgrounds. Professor John Duncan, in Cambridge, is an internationally renowned expert on attention, and will advise me on the cognitive and analytical aspects of the project. Professor Mark Johnson, at the Centre for Brain and Cognitive Development, is an expert on understanding early typical and atypical development, including the early development of Autism and Attention Deficit Disorder, and will advise me on potential links to clinical populations. The proposal also includes a visit to the lab of Dr Ronny Geva, in Israel, to learn new techniques for measuring early stress from experts in her lab.

Data description (abstract)

These files are the raw data files on which the results for these publications were based:

Wass, S. V., Smith, C. G., Daubney, K. R., Suata, Z. M., Clackson, K., Begum, A., & Mirza, F. U. (2019). Influences of environmental stressors on autonomic function in 12‐month‐old infants: understanding early common pathways to atypical emotion regulation and cognitive performance. Journal of Child Psychology and Psychiatry, 60(12), 1323-1333.

Wass, S. V., Smith, C. G., Clackson, K., Gibb, C., Eitzenberger, J., & Mirza, F. U. (2019). Parents mimic and influence their infant’s autonomic state through dynamic affective state matching. Current biology, 29(14), 2415-2422.

Further details on the device used are given in these publications.

The data uploaded here consist of the raw ECG data, raw accelerometer data and time files for these devices. In addition we also have GPS data, microphone recordings and video recordings, but these have not be uploaded as they contain identifiable information. If you wish to obtain these recordings, or to obtain the processed versions of the raw data uploaded here, please contact s.v.wass@uel.ac.uk.

Results/accel/raw csv - these results contain the raw accelerometer data, recorded at 30Hz. The recording duration is identical to the ECG and times files (but the file lengths are different due to the different sampling frequency). Cols 2-4 show the x/y/z dimensions, and col 1 is an average of cols 2-4. The number shows the participant number, _B indicates baby and _M indicates mum. So 222_B and 222_M were recorded from an infant and mother concurrently.

Results/ECG/raw csv - these results contain the raw ECG data, recorded at 250Hz. The labelling is identical to the accelerometer data.

Results/times/raw csv - these results contain the timings of the recordings, recorded at 1Hz. The recording duration is identical to the accelerometer and ECG files. The format of the columns is: [phonetime clockhr clockmins clocksec day month year linecounter].

Results/startstoptimes - these files contain the start and stop times of good usable data segments. First two columns are start and stop time of the good segments at 1Hz. Third and fourth columns are the same but in minutes. Before analysing the ECG or accelerometer data you should score out the sections of data before the start time and after the stop time.

Results/accel/touch syncs - these files are needed to synchronise the baby and mum recordings. The first two columns are two separate estimations of the difference in seconds in recording start time between the baby and mum devices. Always Mum-Baby.

Any questions - please contact Sam s.v.wass@uel.ac.uk

Data creators: