A protocol for linking participants' retailer 'loyalty card' records into the Avon Longitudinal Study of Parents and Children (ALSPAC).

Longitudinal population studies (LPS) have a long history of providing insights into how individual consumption patterns and other lifestyle choices affect health and socio-economic outcomes. LPS typically operate as research databanks, integrating rich and deep phenotypic data - covering diverse aspects of individual, family and household status - with genomic data and linked records on health and socio-economic outcomes. However, individual consumption and behavioural choices are traditionally studied solely using self-report methods which are prone to known biases. We propose to enrich LPS databanks with a new form of digital footprint data - individual shopping history records. These are collected by supermarkets through "loyalty" card schemes and can provide a new perspective on real world behaviours and history of consumption. However, as a novel class of data in the context of longitudinal research, our ability to assess the quality and completeness of the data is unknown, as is our ability to effectively triangulate between self-reported and linked data. This paper describes a protocol for linking individual level shopping history data into a LPS using Avon Longitudinal Study of Parents and Children (ALSPAC) as a testbed. The protocol covers the process of establishing participant fair processing, an ethical and legal basis for the linkage framework itself, and how these data will be integrated into the ALSPAC databank. It does not cover the subsequent research use of these data. The protocol was built on an extensive participant engagement and acceptability work and has been approved by the ALSPAC Law and Ethics committee.

Unpacking privacy: Valuation of personal data protection.

Information about individual behaviour is collected regularly by organisations. This information has value to businesses, the government and third parties. It is not clear what value this personal data has to consumers themselves. Much of the modern economy is predicated on people sharing personal data, however if individuals value their privacy, they may choose to withhold this data unless the perceived benefits of sharing outweigh the perceived value of keeping the data private. One technique to assess how much individuals value their privacy is to ask them whether they might be willing to pay for an otherwise free service if paying allowed them to avoid sharing personal data. Our research extends previous work on factors affecting individuals' decisions about whether to share personal data. We take an experimental approach and focus on whether consumers place a positive value on protecting their data by examining their willingness to share personal data in a variety of data sharing environments. Using five evaluation techniques, we systematically investigate whether members of the public value keeping their personal data private. We show that the extent to which participants value protecting their information differs by data type, suggesting there is no simple function to assign a value for individual privacy. The majority of participants displayed remarkable consistency in their rankings of the importance of different types of data through a variety of elicitation procedures, a finding consistent with the existence of stable individual privacy preferences in protecting personal data. We discuss our findings in the context of research on the value of privacy and privacy preferences.

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.

Participant acceptability of digital footprint data collection strategies: an exemplar approach to participant engagement and involvement in the ALSPAC birth cohort study.

Introduction: Digital footprint records - the tracks and traces amassed by individuals as a result of their interactions with the internet, digital devices and services - can provide ecologically valid data on individual behaviours. These could enhance longitudinal population study databanks; but few UK longitudinal studies are attempting this. When using novel sources of data, study managers must engage with participants in order to develop ethical data processing frameworks that facilitate data sharing whilst safeguarding participant interests. Objectives: This paper aims to summarise the participant involvement approach used by the ALSPAC birth cohort study to inform the development of a framework for using linked participant digital footprint data, and provide an exemplar for other data linkage infrastructures. Methods: The paper synthesises five qualitative forms of inquiry. Thematic analysis was used to code transcripts for common themes in relation to conditions associated with the acceptability of sharing digital footprint data for longitudinal research. Results: We identified six themes: participant understanding; sensitivity of location data; concerns for third parties; clarity on data granularity; mechanisms of data sharing and consent; and trustworthiness of the organisation. For cohort members to consider the sharing of digital footprint data acceptable, they require information about the value, validity and risks; control over sharing elements of the data they consider sensitive; appropriate mechanisms to authorise or object to their records being used; and trust in the organisation. Conclusion: Realising the potential for using digital footprint records within longitudinal research will be subject to ensuring that this use of personal data is acceptable; and that rigorously controlled population data science benefiting the public good is distinguishable from the misuse and lack of personal control of similar data within other settings. Participant co-development informs the ethical-governance framework for these novel linkages in a manner which is acceptable and does not undermine the role of the trusted data custodian.

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.

Attitudes towards transactional data donation and linkage in a longitudinal population study: evidence from the Avon Longitudinal Study of Parents and Children.

Background: Commercial transaction records, such as data collected through banking and retail loyalty cards, present a novel opportunity for longitudinal population studies to capture data on participants' real-world behaviours and interactions. However, little is known about participant attitudes towards donating transactional records for this purpose. This study aimed to: (i) explore the attitudes of longitudinal population study participants towards sharing their transactional records for health research and data linkage; and (ii) explore the safeguards that researchers should consider implementing when looking to request transactional data from participants for data linkage studies. Methods: Participants in the Avon Longitudinal Study of Parents and Children were invited to a series of three focus groups with semi-structured discussions designed to elicit opinions. Through asking participants to attend three focus groups we aimed to facilitate more in-depth discussions around the potentially complex topic of data donation and linkage. Thematic analysis was used to sort data into overarching themes addressing the research questions. Results: Participants (n= 20) expressed a variety of attitudes towards data linkage, which were associated with safeguards to address concerns. This data was sorted into three themes: understanding, trust, and control. We discuss the importance of explaining the purpose of data linkage, consent options, who the data is linked with and sensitivities associated with different parts of transactional data. We describe options for providing further information and controls that participants consider should be available when studies request access to transactional records. Conclusions: This study provides initial evidence on the attitudes and concerns of participants of a longitudinal cohort study towards transactional record linkage. The findings suggest a number of safeguards which researchers should consider when looking to recruit participants for similar studies, such as the importance of ensuring participants have access to appropriate information, control over their data, and trust in the organisation.

Corrigendum: Guilty repair sustains cooperation, angry retaliation destroys it.

This corrects the article DOI: 10.1038/srep46709.

Guilty repair sustains cooperation, angry retaliation destroys it.

Sustained cooperative social interactions are key to successful outcomes in many real-world contexts (e.g., climate change and energy conservation). We explore the self-regulatory roles of anger and guilt, as well as prosocial or selfish social preferences in a repeated social dilemma game framed around shared electricity use at home. We explore the proposal that for sustained cooperation, guilty repair needs to override angry retaliation. We show that anger is damaging to cooperation as it leads to retaliation and an increase of defection, while, through guilt, cooperation is repaired resulting in higher levels of cooperation. We demonstrate a disconnect between the experience of anger and subsequent retaliation which is a function of participants' social preferences. While there is no difference in reports of anger between prosocial and selfish individuals after finding out that others use more energy from the communal resource, prosocials are less likely to act on their anger and retaliate. Selfish individuals are motivated by anger to retaliate but not motivated by guilt to repair and contribute disproportionately to the breakdown of cooperation over repeated interactions. We suggest that guilt is a key emotion to appeal to when encouraging cooperation.

Cognitive control predicts use of model-based reinforcement learning.

Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information--in the service of overcoming habitual, stimulus-driven responses--in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior.

Why do different people choose different university degrees? Motivation and the choice of degree.

Different people choose undergraduate degrees to study at university for different reasons. To date, there have been limited attempts to identify individual differences in motivation that drive undergraduate degree choice. We identified that people choose university degrees for four reasons: career concerns (Career), intrinsic interest in the subject (Interest), an opportunity to help others (Helping) and because they are looking for an easy option to get into higher education (Loafing). We investigated whether these motivations apply to the choice of undergraduate degree in two samples: (1) undergraduate (N = 989) and (2) prospective (N = 896) students. We developed the Motivations Influencing Course Choice (MICC) questionnaire to measure these motivations. Scales of Helping, Career, Loafing, and Interest showed good psychometric properties, showed validity with respect to general life goals and personality traits, and predicted actual and prospective degree choices. We demonstrated that medical degrees were chosen due to a mixture of Helping and Career, while engineering degrees were associated with Career and low Interest in the degree. The choice of arts and humanities degrees was driven by Interest and low concern about future career, accompanied with high Loafing. We also demonstrated gender differences: females were high in Helping (both samples) and Interest (only in the undergraduate sample) motivation, while males scored higher in Career (only in the undergraduate sample) and Loafing (both samples). The findings can feed into both theoretical accounts of proximal motivation as well as provide help to improve degree programmes at universities and support better career advice.

The 'dark side' and 'bright side' of personality: when too much conscientiousness and too little anxiety are detrimental with respect 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.

Extraversion differentiates between model-based and model-free strategies in a reinforcement learning task.

Prominent computational models describe a neural mechanism for learning from reward prediction errors, and it has been suggested that variations in this mechanism are reflected in personality factors such as trait extraversion. However, although trait extraversion has been linked to improved reward learning, it is not yet known whether this relationship is selective for the particular computational strategy associated with error-driven learning, known as model-free reinforcement learning, vs. another strategy, model-based learning, which the brain is also known to employ. In the present study we test this relationship by examining whether humans' scores on an extraversion scale predict individual differences in the balance between model-based and model-free learning strategies in a sequentially structured decision task designed to distinguish between them. In previous studies with this task, participants have shown a combination of both types of learning, but with substantial individual variation in the balance between them. In the current study, extraversion predicted worse behavior across both sorts of learning. However, the hypothesis that extraverts would be selectively better at model-free reinforcement learning held up among a subset of the more engaged participants, and overall, higher task engagement was associated with a more selective pattern by which extraversion predicted better model-free learning. The findings indicate a relationship between a broad personality orientation and detailed computational learning mechanisms. Results like those in the present study suggest an intriguing and rich relationship between core neuro-computational mechanisms and broader life orientations and outcomes.

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.

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.