Individual differences in risk preference: insights from self-report, behavioral and neural measures, and their convergence

From the time of conception until the time of death, the path of the human organism is created and shaped by decisions. Some decisions we make ourselves, some are made for us; some will make us, some will break us. What most decisions have in common, however, is that they are made under risk, that is, without complete information regarding the potential decision outcomes. One interesting feature about decisions under risk is variability: different individuals make different choices, and even the same individual may, given repeated occasions, make different choices. This doctoral thesis aims to address the issue of individual differences by looking at several specific variables which may impact inter- and intra-individual differences in risk taking, namely age, the measures used to assess risk-taking, neural function and neural structure. In a set of four studies, the following questions were addressed: (1) To what extent do life span trajectories of risk taking change as a function of whether self-report or behavioral measures are used to assess risk taking? (2) Do younger and older individuals differ in the neural functional representation of risk and reward? (3) Do the neural representations of described and experienced risk converge, both at group and individual level? To what extent is neural function predictive of risky choice? (4) To what extent do individual differences in neural structure explain variance in psychometrically derived risk preference factors? The main findings are: (1) Self-report and behavioral measures of risk taking do not converge and lead to different life span trajectories. (2) The ventromedial prefrontal cortex is differentially activated in younger and older adults, with activation differences possessing differential explanatory power for choice in the two age groups. (3) Described and experienced risks show convergence at group level, divergence at the individual level, and are differentially predictive of risky choice. (4) Neural structural indices explain variance in the general risk preference factor, but not domain-specific risk preference factors. Based on the findings from all four studies, this thesis provides corroborating evidence for the argument that not all risk-taking measures are created equal and that a taxonomy of risk-taking measures and their respective cognitive and affective demands is required to understand individual differences in risk taking

Tisdall et al. (2020)

These are the scripts for the analyses reported in Tisdall, Frey, Horn, Ostwald, Horvath, Pedroni, Rieskamp, Blankenburg, Hertwig, & Mata (2020). This project aims to investigate the overlap and explanatory power of the neural representation of two popular behavioral measures of risk taking, the Balloon Analogue Risk Taking Task, and a Monetary Gambles task. We examined within-participant data from the imaging subsample (N=116) of the Basel-Berlin Risk Study. Our analyses include group-level (average) task-related activations and individual differences analyses that link task activation to various indices of risk taking, such as psychometrically derived risk preference factors (Frey et al., 2017). We focus our analyses on "risk matrix" regions (Knutson & Huettel, 2015), which designate the nucleus accumbens, anterior cingulate cortex and anterior insular cortex as central neural regions involved with risk-related processing

Age differences in decision-making under uncertainty

Humans globally are reaping the benefits of longer lives. Yet, longer life spans also require engaging with consequential but often uncertain decisions well into old age. Previous research has yielded mixed findings with regards to life span differences in how individuals make decisions under uncertainty. One factor contributing to the heterogeneity of findings is the the diversity of paradigms that cover different aspects of uncertainty and tap into different cognitive and affective mechanisms. In this study, 175 participants (53.14% females, mean age = 44.9 years, SD = 19.0, age range = 16 to 81) completed functional neuroimaging versions of two prominent paradigms in this area, the Balloon Analogue Risk Task and the Delay Discounting Task. Guided by neurobiological accounts of age-related changes in decision-making under uncertainty, we examined age effects on neural activation differences in decision-relevant brain structures, and compared these across multiple contrasts for the two paradigms using specification curve analysis

Risk taking across the life span

Data and R analysis scripts for manuscript: Mamerow, L., Frey, R., and Mata, R. (2016). Risk Taking Across the Life Span: A Comparison of Self-report and Behavioral Measures of Risk Taking

Lifespan trajectories of risk preference, impulsivity, and self-control: A dataset containing self-report, informant-report, behavioral, hormone and functional neuroimaging measures from a cross-sectional human sample

This paper describes data collected from a cross-sectional convenience sample of 200 healthy human volunteers between 16 and 81 years of age. We assembled an extensive battery of measures of risk preference, impulsivity, and self-control, as well as a range of demographic and cognitive measures, Crucially, we adopted different measure categories, including self-reports, informant reports, behavioral measures, and biological measures (hormones, brain function) to capture individual differences, and adopted a within-participant design. Data collection took place over multiple sessions. First, participants completed a laboratory session at the university during which we collected computer-assisted self-report measures (i.e., standardized questionnaires) as well as behavioral measures using computerized tasks. Second, participants independently completed a home session that included the completion of self-report measures, and the collection of saliva samples. In parallel, we acquired informant reports from up to three individuals nominated by the study participants. Third, participants completed a final session at the local hospital during which we collected structural and functional neuroimaging data, as well as further self-report measures. The data was collected to address questions concerning the developmental trajectories of risk preference and related constructs while assessing the impact of the assessment method; however, we invite fellow researchers to benefit from and further explore the data for research on decision-making under risk and uncertainty in general, and to apply novel analytical approaches (e.g., machine-learning applications to the neuroimaging data). Combining a large set of measures with a within-participant design affords a wealth of opportunities for further insights and a more robust evidence base supporting current theorizing on (age-related) differences in risk preference, impulsivity, and self-control

Brain tract structure predicts relapse to stimulant drug use

Diffusion tractography allows identification and measurement of structural tracts in the human brain previously associated with motivated behavior in animal models. Recent findings indicate that the coherence of a tract connecting the midbrain to Nucleus Accumbens (NAcc) is associated with a diagnosis of stimulant use disorder (SUD), but not relapse. Here, we used diffusion tractography in a sample of patients treated for SUD (n=60) to determine whether qualities of tracts projecting from medial prefrontal, anterior insular, and amygdalar cortices to NAcc might instead foreshadow relapse. Reduced coherence of a tract projecting from the right anterior insula to the NAcc was associated with subsequent relapse to stimulant use, but not previous diagnosis. These findings highlight a new structural target for predicting relapse to stimulant use, and further suggest that distinct connections to the NAcc may modulate relapse versus diagnosis

Meta-Analyses of the Temporal Stability and Convergent Validity of Risk Preference Measures

Understanding whether risk preference represents a stable, coherent trait is central to efforts aimed at explaining, predicting, and preventing risk-related behaviours. We help characterise the nature of the construct by adopting an individual participant data meta-analytic approach to summarise the temporal stability of over 350 risk preference measures (33 panels, 57 samples, >575,000 respondents). Our findings reveal significant heterogeneity across and within measure categories (propensity, frequency, behaviour), domains (e.g., investment, occupational, alcohol consumption), and sample characteristics (e.g., age). Specifically, while self-reported propensity and frequency measures of risk preference show a higher degree of stability relative to behavioural measures, these patterns are moderated by domain and age. Crucially, an analysis of convergent validity reveals a low agreement across measures, questioning the idea that they capture the same underlying phenomena. Our results raise concerns about the coherence and measurement of the risk preference construct

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu