Delay discounting in the context of the COVID-19 pandemic
Creators: name: Mason, Liam
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Social and Behavioral Sciences

description: Author(s): Liam Mason – l.mason@ucl.ac.uk Steve Fleming – stephen.fleming@ucl.ac.uk Alex Lloyd – Alex.Lloyd.2018@live.rhul.ac.uk Ryan McKay – Ryan.McKay@rhul.ac.uk 1) Data collection. Have any data been collected for this study already? Data collection is in progress for a first time point (established stage of pandemic); analyses will be performed after data collection completes on 11/05/20. 2) Hypothesis. What's the main question being asked or hypothesis being tested in this study? Our overarching research aim is to study information processing and decision-making in the context of the COVID-19 pandemic. Our specific predictions here concern delay discounting, a measure of impulsivity, and how this drives real-life health-related behaviours, particularly adherence to social distancing. There are two alternative underlying theoretical assumptions. Either: 1) Delay discounting is a trait-like characteristic that will be relatively unchanged by current events (e.g. Odum, 2011; Harrison &amp; McKay, 2012). or 2) Delay discounting is relatively flexible and therefore likely increased by current events. In animals (Mazur &amp; Logue, 1978) and humans (Neef et al., 2001), delay discounting depends on the environmental reward statistics that subjects received. Delay discounting was overall less steep in subjects undergoing a "fading procedure" in which the delay to the smaller reward option was gradually reduced. Delay discounting has also been shown to be dependent on available working memory (Shamosh et al., 2008). Experimentally, delay discounting can be reduced within-participants following interventions aimed at increase working memory (Bickel et al, 2011) and improving financial management skills (Black &amp; Rosen, 2011). On balance, it seems plausible that an acute global crisis might modulate delay discounting despite this typically being conceptualised as a relatively stable characteristic. We therefore have three key hypotheses: 1. In the current COVID-19 crisis, people will adapt their discounting of the future to discount more steeply (H1). H1a) Higher levels of trait catastrophising will predict greater discounting. H1b) Higher specific COVID19-related and general anxiety will predict greater delay discounting. 2. We will test between two opposing predictions that follow the different theoretical assumptions about adapting discounting of the future (H2): H2a) We assume that participants who have adapted more in response to the COVID-19 crisis will be those exhibiting greater delay discounting at the time point we are assessing them. The specific prediction follows: greater delay discounting will be associated with a greater reduction in leaving the house, and in face-to-face contacts with others, compared to usual). H2b) Alternatively, if delay discounting is a relatively trait-like characteristic, we would predict the opposite i.e. participants with higher levels of delay discounting will be those who discount future outcomes more in favour of immediate reward. The specific prediction follows: greater delay discounting will be associated with a smaller reduction in leaving the house and face-to-face contacts with others, compared to usual). 3. Delay discounting will predict over-purchasing behaviour (H3). 3) Dependent variable. Describe the key dependent variable(s) specifying how they will be measured. Delay discounting will be quantified using the 27-item delay discounting measure (Kirby et al, 1999). This is in the context of a wider online task battery. We will administer the following self-report measures: • Trait Catastrophising Questionnaire (short form; Pike et al, preprint) • Generalized Anxiety Disorder questionnaire (GAD-7; Spitzer et al., 2006) • Patient Health Questionnaire depression scale (PHQ-8; Kroenke et al., 2009) • Mood Zoom, a momentary assessment of mood states (MZ; Tsanas et al., 2016) • Hypomanic Personality Scale (HPS; Eckblad &amp; Chapman, 1986) • A series of bespoke items tapping worry about the Covid-19 pandemic as well as social distancing and other health behaviours (e.g. handwashing) in relation to COVID-19. 4) Conditions. How many and which conditions will participants be assigned to? N/A 5) Analyses. Specify exactly which analyses you will conduct to examine the main question/hypothesis. The slope and intercepts of participants’ delay discounting responses will be quantified using a hierarchical Bayesian model implemented in a widely available analysis toolbox (Vincent, 2016). Delay discounting parameters will be entered as predictors in a regression predicting self-report COVID-19 behaviours and mood symptoms. 6) Outliers and Exclusions. Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. N/A. 7) Sample Size. How many observations will be collected or what will determine sample size? We will recruit a representative subset (target n=400) of the second wave cohort of the COVID-19 Psychological Research Consortium (C19PRC) Study. C19PRC is a longitudinal, multi-country study that aims to assess the psychological impact of the COVID-19 virus in the adult general population, commencing in the UK (McBride et al. 2020). The first wave of the study, C19PRC-UKW1, involved the recruitment of a large representative sample of UK adults (N=2025; quota sampling methods ensured that the sample was representative in terms of age, sex and gross household income), and was conducted by the survey company Qualtrics between 23-28 March 2020. The second wave of the study, C19PRC-UKW2, was launched 22 April 2020. Qualtrics will contact a subset of the C19PRC-UKW2 cohort and will embed our tasks which will be hosted on a partner platform (Gorilla). 8) Other. Anything else you would like to pre-register? N/A. 9) Name. Give a title for this AsPredicted pre-registration Delay discounting in the context of the COVID-19 pandemic References Arnott, D., &amp; O'Donnell, P. (2008). A note on an experimental study of DSS and forecasting exponential growth. Decision Support Systems, 45(1), 180-186. Black AC, Rosen MI. A money management-based substance use treatment increases valuation of future rewards. 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