Convergence of Age Differences in Risk Preference, Impulsivity, and Self-Control: A Multiverse Analysis.

OBJECTIVES: Numerous theories exist regarding age differences in risk preference and related constructs, yet many of them offer conflicting predictions and fail to consider convergence between measurement modalities or constructs. To pave the way for conceptual clarification and theoretical refinement, in this preregistered study we aimed to comprehensively examine age effects on risk preference, impulsivity, and self-control using different measurement modalities, and to assess their convergence. METHODS: We collected a large battery of self-report, informant report, behavioral, hormone, and neuroimaging measures from a cross-sectional sample of 148 (55% female) healthy human participants between 16 and 81 years (mean age = 46 years, standard deviation [SD] = 19). We used an extended sample of 182 participants (54% female, mean age = 46 years, SD = 19) for robustness checks concerning the results from self-reports, informant reports, and behavioral measures. For our main analysis, we performed specification curve analyses to visualize and estimate the convergence between the different modalities and constructs. RESULTS: Our multiverse analysis approach revealed convergent results for risk preference, impulsivity, and self-control from self- and informant reports, suggesting a negative effect of age. For behavioral, hormonal, and neuroimaging outcomes, age effects were mostly absent. DISCUSSION: Our findings call for conceptual clarification and improved operationalization to capture the putative mechanisms underlying age-related differences in risk preference and related constructs.

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.

Age differences in the neural basis of 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 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. In line with theoretical predictions, we find age differences in nucleus accumbens, anterior insula, and medial prefrontal cortex, but the results vary across paradigm and contrasts. Our results are in line with existing theories of age differences in decision making and their neural substrates, yet also suggest the need for a broader research agenda that considers how both individual and task characteristics determine the way humans deal with uncertainty.

Aging and Economic Preferences: Cumulative Meta-Analyses of Age Differences in Risk, Time, Social, and Effort Preferences.

OBJECTIVES: Several theories predict changes in individuals' economic preferences across the life span. To test these theories and provide a historical overview of this literature, we conducted meta-analyses on age differences in risk, time, social, and effort preferences as assessed by behavioral measures. METHODS: We conducted separate meta-analyses and cumulative meta-analyses on the association between age and risk, time, social, and effort preferences. We also conducted analyses of historical trends in sample sizes and citation patterns for each economic preference. RESULTS: The meta-analyses identified overall no significant effects of age for risk (r = -0.02, 95% CI [-0.06, 0.02], n = 39,832) and effort preferences (r = 0.24, 95% CI [-0.05, 0.52], n = 571), but significant effects of age for time (r = -0.04, 95% CI [-0.07, -0.01], n = 115,496) and social preferences (r = 0.11, 95% CI [0.01, 0.21], n = 2,997), suggesting increased patience and altruism with age, respectively. Equivalence tests, which compare these effects to practically important ones (i.e., r = |0.1|), however, suggest that all effects are of trivial significance. The analyses of temporal trends suggest that the magnitude of effects and sample sizes have not changed significantly over time, nor do they dramatically affect the extent that articles are cited. DISCUSSION: Overall, our results contrast with theories of aging that propose general age effects for risk and effort preferences, yet provide some but tenuous support for those suggesting age-related changes in time and social preferences. We discuss implications for theory development as well as future empirical work on economic preferences.

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 structural properties of a tract connecting the midbrain to nucleus accumbens (NAcc) are associated with a diagnosis of stimulant use disorder (SUD), but not relapse. In this preregistered study, 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. As predicted, reduced diffusion metrics of a tract projecting from the right anterior insula to the NAcc were associated with subsequent relapse to stimulant use, but not with previous diagnosis. These findings highlight a structural target for predicting relapse to stimulant use and further suggest that distinct connections to the NAcc may confer risk for relapse versus diagnosis.

Brain-Behavior Associations for Risk Taking Depend on the Measures Used to Capture Individual Differences.

Maladaptive risk taking can have severe individual and societal consequences; thus, individual differences are prominent targets for intervention and prevention. Although brain activation has been shown to be associated with individual differences in risk taking, the directionality of the reported brain-behavior associations is less clear. Here, we argue that one aspect contributing to the mixed results is the low convergence between risk-taking measures, especially between the behavioral tasks used to elicit neural functional markers. To address this question, we analyzed within-participant neuroimaging data for two widely used risk-taking tasks collected from the imaging subsample of the Basel-Berlin Risk Study (N = 116 young human adults). Focusing on core brain regions implicated in risk taking (nucleus accumbens, anterior insula, and anterior cingulate cortex), for the two tasks, we examined group-level activation for risky versus safe choices, as well as associations between local functional markers and various risk-related outcomes, including psychometrically derived risk preference factors. While we observed common group-level activation in the two tasks (notably increased nucleus accumbens activation), individual differences analyses support the idea that the presence and directionality of associations between brain activation and risk taking varies as a function of the risk-taking measures used to capture individual differences. Our results have methodological implications for the use of brain markers for intervention or prevention.