Individual differences in behavioural inhibition explain free riding in public good games when punishment is expected but not implemented

Background: The literature on social dilemmas and punishment focuses on the behaviour of the punisher. However, to fully explain the effect of punishment on cooperation, it is important to understand the psychological mechanisms influencing the behaviour of those who expect to be punished. This paper examines whether the expectation of punishment, rather than the implementation of punishment is sufficient to prevent individuals from free riding. Individual differences in the punishment sensitivity have been linked to both threat responses (flight, fight, fear system, or the FFFS) and to the response to the uncertainty of punishment (BIS-anxiety).The paper, therefore, examines if individual differences in BIS-anxiety and FFFS can explain some of the variability in free riding in the face of implemented and non-implemented punishment. Methods: Participants took part in a series of one-shot Public Goods Games (PGGs) facing two punishment conditions (implemented and non-implemented) and two standard non-punishment PGGs. The punishment was implemented as a centralized authority punishment (i.e., if one participant contributed less than their group members, they were automatically fined). Individual contribution levels and presence/absence of zero contributions indexed free riding. Individual differences in behavioural inhibition were assessed. Results: Individuals contributed more under the threat of punishment (both implemented and non-implemented). However, individuals contributed less when the punishment was not implemented compared to when it was. Those scoring high in BIS-anxiety contributed more when the punishment expectations were not implemented. This effect was not observed for FFFS. Conclusion: Supporting previous research, punishment had a powerful effect in increasing contribution levels in the PGGs. However, when expected punishment was not implemented, individual differences in punishment sensitivity, specifically in BIS-anxiety, were related to fewer contributions (increased free riding) as compared to the situation when punishment was not implemented. This has implications for our understanding of why some people cannot resist the temptation to free ride, even when facing possible punishment for their actions. Our findings suggest that the diminished functioning of mechanisms, associated with trait behavioural inhibition, can partly explain such behaviours

Modelling the effect of individual differences in punishment sensitivity on behaviour in a public goods game

Previous research on social dilemmas demonstrated that various forms of punishment for free-riding can increase contribution levels in public goods games. The way individual group members react to the possibility of punishment can be also affected by individual differences in punishment sensitivity. Therefore, depending individual differences in punishment sensitivity of group members, different levels of punishment can be more or less effective to prevent free riding behaviour. This paper uses agent-based modelling to model the effect of punishment sensitivity on contribution levels in a public goods game. The paper then examines the correlation between punishment sensitivity and variability of free riding behaviour under different punishment conditions

Psychology of personal data donation

Advances in digital technology have led to large amounts of personal data being recorded and retained by industry, constituting an invaluable asset to private organizations. The implementation of the General Data Protection Regulation in the EU, including the UK, fundamentally reshaped how data is handled across every sector. It enables the general public to access data collected about them by organisations, opening up the possibility of this data being used for research that benefits the public themselves; for example, to uncover lifestyle causes of poor health outcomes. A significant barrier for using this commercial data for academic research, however, is the lack of publicly acceptable research frameworks. Data donation-the act of an individual actively consenting to donate their personal data for research-could enable the use of commercial data for the benefit of society. However, it is not clear which motives, if any, would drive people to donate their personal data for this purpose. In this paper we present the results of a large-scale survey (N = 1,300) that studied intentions and reasons to donate personal data. We found that over half of individuals are willing to donate their personal data for research that could benefit the wider general public. We identified three distinct reasons to donate personal data: an opportunity to achieve self-benefit, social duty, and the need to understand the purpose of data donation. We developed a questionnaire to measure those three reasons and provided further evidence on the validity of the scales. Our results demonstrate that these reasons predict people's intentions to donate personal data over and above generic altruistic motives. We show that a social duty is the strongest predictor of the intention to donate personal data, while understanding the purpose of data donation also positively predicts the intentions to donate personal data. In contrast, self-serving motives show a negative association with intentions to donate personal data. The findings presented here examine people's reasons for data donation to help inform the ethical use of commercially collected personal data for academic research for public good

Daily, Weekly and Monthly Variation in Lunch Time Calories

Introduction & Background Despite the level of attention that healthy and unhealthy eating receive from academic research, policymakers and the wider public, objective data on food consumption is limited. This is because studies of individual eating patterns using food diaries are subject to underreporting, particularly by people who are overweight. For example, the UK population is estimated to consume between 30% to 50% more calories than they report in surveys. New data sources such as office canteen ordering systems and individual records of supermarket transactions recorded through supermarket loyalty or bonus cards offer larger and potentially more robust data on real world individual eating behaviours. Objectives & Approach We used 2,831,403 machine-recorded ‘meal deal’ transactions from 205,781 individuals over the course of one year from one of the UK’s largest suppliers of lunch time foods to investigate whether there is a relationship between patterns of choice and higher calorie consumption. A meal deal comprises three items; a main (e.g., a sandwich or a salad), a snack (e.g., crisps, fruit or a chocolate bar) and a drink (e.g., a smoothie or a bottle of water). In contrast to diary studies or aggregate transactional data from supermarkets, our dataset included “meal deal’ purchase which is highly likely to be made by an individual for their own consumption or soon afterwards. Relevance to Digital Footprints Lunch time food consumption can reflect the overall diet the individual is exposed to, helping to understand population level patterns of people’s food choices through a type of digital footprints data - shopping history records. Results Controlling for gender, general index of variety in the choice of lunch food items, income and education, we found that individuals who vary in their calorie consumption most across the time of day, day of the week, and month of the year are the individuals who consume the greatest number of calories overall. These time sensitivity effects are large, together explaining a substantial amount of variance in calorie consumption. Time sensitivity effects are strongly correlated across all three time scales suggesting they measure a stable underlying trait. Conclusions & Implications Individuals vary calorific composition of their lunch over time of the day, day of the week and month of the year by 100 calories per meal between highest and lowest in sensitivity which is about 9% of the recommended amount of lunchtime calories. Those whose consumption varies the most with time consume the most calories, independently of income and gender. The variation in calories at all three time scales demonstrates the properties of an individual disposition. These findings can be used to understand why and when people make unhealthy food choices

Public attitudes towards sharing loyalty card data for academic health research: a qualitative study

Background: A growing number of studies show the potential of loyalty card data for use in health research. However , research into public perceptions of using this data is limited. This study aimed to investigate public attitudes towards donating loyalty card data for academic health research, and the safeguards the public would want to see implemented. The way in which participant attitudes varied according to whether loyalty card data would be used for either cancer or COVID-19 research was also examined. Methods: Participants (N = 40) were recruited via Prolific Academic to take part in semi-structured telephone interviews , with questions focused on data sharing related to either COVID-19 or ovarian/bowel cancer as the proposed health condition to be researched. Content analysis was used to identify sub-themes corresponding to the two a priori themes, attitudes and safeguards. Results: Participant attitudes were found to fall into two categories, either rational or emotional. Under rational, most participants were in favour of sharing loyalty card data. Support of health research was seen as an important reason to donate such data, with loyalty card logs being considered as already within the public domain. With increased understanding of research purpose, participants expressed higher willingness to donate data. Within the emotional category, participants shared fears about revealing location information and of third parties obtaining their data. With regards to safeguards, participants described the importance of anonymisation and the level of data detail; the control, convenience and choice they desired in sharing data; and the need for transparency and data security. The change in hypothetical purpose of the data sharing, from Covid-19 to cancer research, had no impact on participants' decision to donate, although did affect their understanding of how loyalty card data could be used. Conclusions: Based on interviews with the public, this study contributes recommendations for those researchers and the wider policy community seeking to obtain loyalty card data for health research. Whilst participants were largely in favour of donating loyalty card data for academic health research, information, choice and appropriate safeguards are all exposed as prerequisites upon which decisions are made

Health specific traits beyond the Five Factor Model, cognitive processes and trait expression: replies to Watson (2012), Matthews (2012) and Haslam, Jetten, Reynolds, and Reicher (2012)

In this article we reply to the issues raised by the three commentaries on Ferguson's (2012) article. Watson argues that the four traits identified by Ferguson (2012) – health anxiety, alexithymia, empathy and Type D – do not lie outside the Five Factor Model (FFM). We present factor analytic data showing that health anxiety forms a separate factor from positive and negative affectivity, alexithymia forms a factor outside the FFM and while emotional empathy loads with agreeableness, cognitive empathy forms a separate factor outside the FFM. Across these analyses there was no evidence for a general factor of personality. We also show that health anxiety, empathic facets and alexithymia show incremental validity over FFM traits. However, the evidence that Type D lies outside the FFM is less clear. Matthews (2012) argues that traits have a more distributed influence on cognitions and that attention is not part of Ferguson's framework. We agree; but Ferguson's original statement concerned where traits have their maximal effect. Finally, Haslam et al. suggest that traits should be viewed from a dynamic interactionist perspective. This is in fact what Ferguson (2012) suggested and we go on to highlight that traits can also influence group processes

Life-swap:how discussions around personal data can motivate desire for change

Personal informatics technologies support the collection of and reflection on personal data, but enabling people to learn from and act on this data is still an on-going challenge. Sharing and discussing data is one way people can learn from it, but as yet, little research explores how peer discourses around data can shape understandings and promote action. We ran 3 workshops with 5-week follow-ups, giving 18 people the opportunity to swap their data and discuss it with another person. We found that these workshops helped them to recontextualise and to better understand their data, identify new strategies for changing their behaviour and motivated people to commit to changes in the future. These findings have implications for how personal informatics tools could help people identify opportunities for change and feel motivated to try out new strategies

The ‘Dark Side’ and ‘Bright Side’ of Personality: When Too Much Conscientiousness and Too Little Anxiety Are Detrimental to the Acquisition of Medical Knowledge and Skill

Theory suggests that personality traits evolved to have costs and benefits, with the effectiveness of a trait dependent on how these costs and benefits relate to the present circumstances. This suggests that traits that are generally viewed as positive can have a ‘dark side’ and those generally viewed as negative can have a ‘bright side’ depending on changes in context. We test this in a sample of 220 UK medical students with respect to associations between the Big 5 personality traits and learning outcomes across the 5 years of a medical degree. The medical degree offers a changing learning context from pre-clinical years (where a more methodical approach to learning is needed) to the clinical years (where more flexible learning is needed, in a more stressful context). We argue that while trait conscientiousness should enhance pre-clinical learning, it has a ‘dark side’ reducing the acquisition of knowledge in the clinical years. We also suggest that anxiety has a ‘bright side’ enhancing the acquisition of skills in the clinical years. We also explore if intelligence enhances learning across the medical degree. Using confirmatory factor analysis and structural equation modelling we show that medical skills and knowledge assessed in the pre-clinical and clinical years are psychometrically distinguishable, forming a learning ‘backbone’, whereby subsequent learning outcomes are predicted by previous ones. Consistent with our predictions conscientiousness enhanced preclinical knowledge acquisition but reduced the acquisition of clinical knowledge and anxiety enhanced the acquisition of clinical skills. We also identified a curvilinear U shaped association between Surgency (extraversion) and pre-clinical knowledge acquisition. Intelligence predicted initial clinical knowledge, and had a positive total indirect effect on clinical knowledge and clinical skill acquisition. For medical selection, this suggests that selecting students high on conscientiousness may be problematic, as it may be excluding those with some degree of moderate anxiety

Forecasting local COVID-19/Respiratory Disease mortality via national longitudinal shopping data: the case for integrating digital footprint data into early warning systems

Introduction & Background The COVID-19 pandemic led to unparalleled pressure on healthcare services, highlighting the need for improved healthcare planning for respiratory disease outbreaks. With rapid virus diversification, and correspondingly rapid shifts in symptom expression, there is often a complete lack of representative clinical testing data available to modellers. This is especially true at the onset in outbreaks, where traditional epidemiological and statistical approaches that utilise case data ‘ground truths’ are extremely challenging to apply. In this abstract we preview the results of two novel studies that investigate how the use of digital footprint data - in the form of over-the-counter medication sales - might serve as a predictive proxy for underlying and often hidden disease incidence, and the extent to which such data might improve mortality rate forecasting at local area levels. Objectives & Approach Over 2 billion transactions logged by a UK high-street health retailer were collated across English local authorities (n=314), generating weekly variables corresponding to a range of health purchase behaviours (e.g cough mixture / pain-relief sales) in each authority. These purchase data were additionally linked to a set of independent variables describing each local authority’s 1. weekly environment (e.g. weather, temperature, pollution), 2. socio-demographics (e.g. age distributions, deprivation levels, population densities) and 3. available local test case data. Machine learning regression models were then deployed to investigate the ability of each of these variable sets to underpin predictions of weekly registered deaths in the 314 authorities that were due to: COVID-19 between Apr 2020 - Dec 2021 (Study 1) or general respiratory disease between March 2016 - Mar 2020 (Study 2). All models were rigorously tested out-of-sample via walk forward cross-validation, and across a range of forecast windows. Relevance to Digital Footprints Epidemics such as COVID-19 are recognised as being driven as much by behavioural factors as they are by clinical ones. Indicators of infection rates may be revealed in purchasing and self-medication logs, where there exists rich data: in 2022 UK citizens were reported to generate >1 billion prescriptions; consume ~6,300 tonnes of paracetamol; and spend £572m on cough, cold and sore throat treatments. Application of the digital footprint data logs generated by such activities may hold potential to reveal hidden disease incidence and risk to vulnerable communities, without reliance on prohibitively expensive testing infrastructures. Results Evidence was found that models incorporating digital footprint sales data were able to significantly out-perform models that used variables traditionally associated with respiratory disease alone (e.g. sociodemographics, weather, or case data). In Study 1, XGBoost models were able to optimally predict the number of COVID deaths 21 days in advance (R2=0.71***), significantly outperforming models based on official COVID case data alone at local-area levels (R2=0.44**). For the pre-COVID period, where registered deaths express a far greater seasonal pattern, models optimally predicted registered respiratory deaths 17 days in advance (R2=0.78***), with highest accuracy gains over models without digital footprint data (increases in R2 between 0.09 to 0.11) occurring in periods of maximum risk to the general public (winter periods). Conclusions & Implications Over-the-counter medication purchases related to management of respiratory illness are correlated with registered deaths at a 17-21 day window. Results demonstrate the potential for sales data to support early warning population health mechanisms at local area levels, and the need for ongoing research into their application to support health planning