Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement.

Taxonomic incommensurability denotes the difficulty in comparing scientific theories due to different uses of concepts and operationalizations. To tackle this problem in psychology, here we use language models to obtain semantic embeddings representing psychometric items, scales and construct labels in a vector space. This approach allows us to analyse different datasets (for example, the International Personality Item Pool) spanning thousands of items and hundreds of scales and constructs and show that embeddings can be used to predict empirical relations between measures, automatically detect taxonomic fallacies and suggest more parsimonious taxonomies.

A systematic review and 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 characterize the nature of the construct by adopting a systematic review and individual participant data meta-analytic approach to summarize the temporal stability of 358 risk preference measures (33 panels, 57 samples, 579,114 respondents). Our findings reveal noteworthy heterogeneity across and within measure categories (propensity, frequency and behaviour), domains (for example, investment, occupational and alcohol consumption) and sample characteristics (for example, age).

Mapping Mental Representations With Free Associations: A Tutorial Using the R Package associatoR.

People's understanding of topics and concepts such as risk, sustainability, and intelligence can be important for psychological researchers and policymakers alike. One underexplored way of accessing this information is to use free associations to map people's mental representations. In this tutorial, we describe how free association responses can be collected, processed, mapped, and compared across groups using the R package .

Large language models surpass human experts in predicting neuroscience results.

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.

A tutorial on open-source large language models for behavioral science.

Large language models (LLMs) have the potential to revolutionize behavioral science by accelerating and improving the research cycle, from conceptualization to data analysis. Unlike closed-source solutions, open-source frameworks for LLMs can enable transparency, reproducibility, and adherence to data protection standards, which gives them a crucial advantage for use in behavioral science. To help researchers harness the promise of LLMs, this tutorial offers a primer on the open-source Hugging Face ecosystem and demonstrates several applications that advance conceptual and empirical work in behavioral science, including feature extraction, fine-tuning of models for prediction, and generation of behavioral responses.

Novel embeddings improve the prediction of risk perception.

Unlabelled: We assess whether the classic psychometric paradigm of risk perception can be improved or supplanted by novel approaches relying on language embeddings. To this end, we introduce the Basel Risk Norms, a large data set covering 1004 distinct sources of risk (e.g.

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).

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.

Cohort profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G).

The German Socio-Economic Panel (SOEP) serves a global research community by providing representative annual longitudinal data of respondents living in private households in Germany. The dataset offers a valuable life course panorama, encompassing living conditions, socioeconomic status, familial connections, personality traits, values, preferences, health, and well-being. To amplify research opportunities further, we have extended the SOEP Innovation Sample (SOEP-IS) by collecting genetic data from 2,598 participants, yielding the first genotyped dataset for Germany based on a representative population sample (SOEP-G).

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.

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.

Structural differences in the semantic networks of younger and older adults.

Cognitive science invokes semantic networks to explain diverse phenomena, from memory retrieval to creativity. Research in these areas often assumes a single underlying semantic network that is shared across individuals. Yet, recent evidence suggests that content, size, and connectivity of semantic networks are experience-dependent, implying sizable individual and age-related differences.

Life-Course Trajectories of Risk-Taking Propensity: A Coordinated Analysis of Longitudinal Studies.

Objectives: How does risk preference change across the life span? We address this question by conducting a coordinated analysis to obtain the first meta-analytic estimates of adult longitudinal age differences in risk-taking propensity in different domains.

Methods: We report results from 26 longitudinal samples (12 panels; 187,733 unique respondents; 19 countries) covering general and domain-specific risk-taking propensity (financial, driving, recreational, occupational, health) across 3 or more waves.

Results: Results revealed a negative relation between age and both general and domain-specific risk-taking propensity.

On the semantic representation of risk.

What are the defining features of lay people's semantic representation of risk? We contribute to mapping the semantics of risk based on word associations to provide insight into both universal and individual differences in the representation of risk. Specifically, we introduce a mini-snowball word association paradigm and use the tools of network and sentiment analysis to characterize the semantics of risk. We find that association-based representations not only corroborate but also extend those extracted from past survey- and text-based approaches.

Temporal discounting across adulthood: A systematic review and meta-analysis.

A number of developmental theories have been proposed that make differential predictions about the links between age and temporal discounting, or the devaluation of future rewards. Most empirical studies examining adult age differences in temporal discounting have relied on economic intertemporal choice tasks, which pit choosing a smaller, sooner monetary reward against choosing a larger, later one. Although initial studies using these tasks suggested older adults discount less than younger adults, follow-up studies provided heterogeneous, and thus inconclusive, results.

Using Network Science to Understand the Aging Lexicon: Linking Individuals' Experience, Semantic Networks, and Cognitive Performance.

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance.

Does information structuring improve recall of discharge information? A cluster randomized clinical trial.

Objectives: The impact of the quality of discharge communication between physicians and their patients is critical on patients' health outcomes. Nevertheless, low recall of information given to patients at discharge from emergency departments (EDs) is a well-documented problem. Therefore, we investigated the outcomes and related benefits of two different communication strategies: Physicians were instructed to either use empathy (E) or information structuring (S) skills hypothesizing superior recall by patients in the S group.

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.

Identifying robust correlates of risk preference: A systematic approach using specification curve analysis.

People's risk preferences are thought to be central to many consequential real-life decisions, making it important to identify robust correlates of this construct. Various psychological theories have put forth a series of candidate correlates, yet the strength and robustness of their associations remain unclear because of disparate operationalizations of risk preference and analytic limitations in past research. We addressed these issues with a study involving several operationalizations of risk preference (all collected from each participant in a diverse sample of the German population; = 916), and by adopting an exhaustive modeling approach-specification curve analysis.

New Perspectives on the Aging Lexicon.

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations).

Three gaps and what they may mean for risk preference.

Risk preference is one of the most important building blocks of choice theories in the behavioural sciences. In economics, it is often conceptualized as preferences concerning the variance of monetary payoffs, whereas in psychology, risk preference is often thought to capture the propensity to engage in behaviour with the potential for loss or harm. Both concepts are associated with distinct measurement traditions: economics has traditionally relied on behavioural measures, while psychology has often relied on self-reports.

Good + Bad = ? Developmental Differences in Balancing Gains and Losses in Value-Based Decisions From Memory.

Value-based decisions often involve comparisons between benefits and costs that must be retrieved from memory. To investigate the development of value-based decisions, 9- to 10-year olds (N = 30), 11- to 12-year olds (N = 30), and young adults (N = 30) first learned to associate gain and loss magnitudes with symbols. In a subsequent decision task, participants rapidly evaluated objects that consisted of combinations of these symbols.