Biology subjects

Hertwig, R.

Publications and source records attributed to Hertwig, R..

8 recordsLinked to original sources

bioRxiv2026

Genomics of Educational Attainment Across 80 Years of Social and Political Transformation in Germany

Abrupt social and political transformations in twentieth-century Germany may have shifted how polygenic indices relate to educational outcomes across birth cohorts. Following a prespecified analysis plan, we harmonized genomic, educational, and regional data from four German studies, comprising 13,049 individuals born between 1918 and 1995. Polygenic index associations with educational attainment were robust and strengthened modestly across more recent birth cohorts, with no detectable difference between East and West Germany. Associations with intergenerational educational mobility showed no evidence of such strengthening but were stronger in the East than in the West. Polygenic indices were less predictive of educational attainment and educational mobility for women than for men, with no evidence of regional variation. We discuss the methodological limitations of polygenic indices for examining gene-environment interactions, and emphasize that evaluating whether these differences represent progress towards equal educational opportunity requires a normative framework that social science genetics alone cannot supply.

genomics↗

bioRxiv2025

DfE-DB: A systematic database of 3.8 million human decisions across multiple experience-based tasks

Learning from experience is central to human decision making, but progress is limited by heterogeneous task designs and fragmented data formats that hinder systematic comparison across studies. We introduce the Decisions From Experience Database (DfE-DB), a structured, open-access resource comprising 3.8 million trial-level decisions from 11,921 participants across 168 studies and 13 paradigms. By harmonizing raw behavioral data and classifying studies along 13 key design features, the DfE-DB enables quantitative comparisons previously obscured by heterogeneous task and data structures. Using the database, we show that choice tendencies--toward higher risk, expected value, or experienced mean--vary substantially across paradigms and are strongly shaped by core design features such as feed-back type, outcome structure, stationarity, and sampling. These features explain substantial cross-study variability and reveal underexplored paradigm variants. By providing a unified and extensible data infrastructure, the DfE-DB facilitates reproducible research on testing the generality of behavioral phenomena and computational models, fostering a more integrated science of decisions from experience.

neuroscience↗

bioRxiv2025

Explicit memory representations in decisions from experience

Reinforcement learning (RL) models explain how people adapt behavior through incremental value updates but assume that individual experiences are not explicitly stored or retrieved. Across two experiments (N = 282 and N = 1,818), we tested whether people rely on such explicit memory representations during experience-based choice. Participants sampled outcomes from two lotteries and, in an "ignore" condition, were instructed to disregard specific outcomes before deciding. Ignoring these outcomes substantially altered preferences, suggesting that choices were guided by explicit episodic representations rather than cumulative reinforcement. Frequency judgments revealed generally accurate memory for experienced outcomes, but reduced precision for continuous compared with discrete outcome distributions. These findings challenge purely incremental RL accounts and support theories proposing that human choice integrates flexible episodic memory with reinforcement mechanisms, bridging models of learning, memory, and decision-making. Statement of RelevanceHow people learn from experience shapes decisions in many everyday situations, from choosing a stock to invest in to selecting a restaurant or deciding which route to take home. Our experiments show that people do not rely solely on incremental value updates but instead exploit memory representations of past outcomes to guide choices. When asked to ignore certain outcomes, participants adjusted their preferences in line with these representations. These findings reveal that human decision-making also engages flexible use of memory for past experiences, highlighting the importance of memory-based mechanisms in adaptive, real-world choice behaviour.

neuroscience↗

bioRxiv2025

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference

Polygenic indexes (PGIs) -- DNA-based predictors of individual phenotypes -- have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGIs predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repositorys scope, and introduce novel resources that reduce confounding bias and improve interpretability.

genomics↗

bioRxiv2024

Neural variability compresses with increasing belief precision during Bayesian inference

To make optimal decisions, intelligent agents must learn latent environmental states from discrete observations. Bayesian frameworks argue that integration of evidence over time allows us to refine our state belief by reducing uncertainty about alternate possibilities. How is this increasing belief precision during learning reflected in the brain? We propose that moment-to-moment neural variability should scale with the degree of reduction of uncertainty during learning. In a sample of 47 healthy adults, we found that BOLD signal variability (SDBOLD, as measured with functional MRI) indeed compressed with successive exposure to decision-related evidence. Crucially, more accurate participants expressed greater SDBOLD compression primarily in Default Mode Network regions, possibly reflecting the increasing precision of their latent state belief during more efficient learning. Further, computational modeling of behavior suggested that more accurate subjects held a more unbiased (flatter) prior belief over possible states that allowed for larger uncertainty reduction during learning, which was directly reflected in SDBOLD changes. Our results provide first evidence that moment-to-moment neural variability compresses with increasing belief precision during effective learning, proposing a flexible mechanism for how we come to learn the probabilistic nature of the world around us.

neuroscience↗

bioRxiv2022

EEG-representational geometries and psychometric distortions in approximate numerical judgment

When judging the average value of sample stimuli (e.g., numbers) people tend to either over- or underweight extreme sample values, depending on task context. In a context of overweighting, recent work has shown that extreme sample values were overly represented also in neural signals, in terms of an anti-compressed geometry of number samples in multivariate electroencephalography (EEG) patterns. Here, we asked whether neural representational geometries may also reflect underweighting of extreme values (i.e., compression) which has been observed behaviorally in a great variety of tasks. We used a simple experimental manipulation (instructions to average a single-stream or to compare dual-streams of samples) to induce compression or anti-compression in behavior when participants judged rapid number sequences. Model-based representational similarity analysis (RSA) replicated the previous finding of neural anti-compression in the dual-stream task, but failed to provide evidence for neural compression in the single-stream task, despite the evidence for compression in behavior. Instead, the results suggested enhanced neural processing of extreme values in either task, regardless of whether extremes were over- or underweighted in subsequent behavioral choice. We further observed more general differences in the neural representation of the sample information between the two tasks. The results suggest enhanced processing of extreme values as the brains default. Such a default raises new questions about the origin of common psychometric distortions, such as diminishing sensitivity for larger values.

neuroscience↗

bioRxiv2021

Cohort Profile: Genetic data in the German Socio-Economic Panel Innovation Sample (Gene-SOEP)

The German Socio-Economic Panel (SOEP) serves a global research community by providing representative annual longitudinal data of private households in Germany. The sample provides a detailed life course perspective based on a rich collection of information about living conditions, socio-economic status, family relationships, personality, values, preferences, and health. We collected genetic data from 2,598 individuals in the SOEP Innovation Sample, yielding the first genotyped sample that is representative of the entire German population (Gene-SOEP). The Gene-SOEP sample is a longitudinal study that includes 107 full-sibling pairs, 501 parent-offspring pairs, and 152 parent-offspring trios that are overlapping with the parent-offspring pairs. We constructed a repository of 66 polygenic indices in the Gene-SOEP sample based on results from well-powered genome-wide association studies. The Gene-SOEP data provides a valuable resource to study individual differences, inequalities, life-course development, health, and interactions between genetic predispositions and environment.

genetics↗

bioRxiv2021

Control over sampling boosts numerical evidence processing in human decisions from experience

When acquiring information about choice alternatives, decision makers may have varying levels of control over which and how much information they sample before making a choice. How does control over sampling affect the quality of experience-based decisions? Here, combining variants of a numerical sampling task with neural recordings, we show that control over when to stop sampling can enhance (i) behavioral choice accuracy, (ii) the build-up of parietal decision signals, and (iii) the encoding of numerical sample information in multivariate electroencephalogram (EEG) patterns. None of these effects were observed when participants could only control which alternatives to sample, but not when to stop sampling. Furthermore, levels of control had no effect on early sensory signals or on the extent to which sample information leaked from memory. The results indicate that freedom to stop sampling can amplify decisional evidence processing from the outset of information acquisition and lead to more accurate choices.

neuroscience↗