Maren Klingelhöfer-Jens

Recent adversity (RA) increases the risk of later mental health problems, but the mechanisms underlying this association remain largely unclear. Altered associative learning and specifically threat learning processes may constitute a potential pathway. Cross-sectional studies link RA to reduced threat-safety discrimination, a process crucial for adaptive functioning; however, longitudinal evidence allowing for more causal conclusions is lacking. In this prospective longitudinal study, 69 healthy participants completed fear acquisition, 24h-delayed extinction training and a reinstatement-test at two measurement time points six months apart (T0 and T1). We captured exposure to RA between T0 and T1 as well as autonomic and neural responses (SCRs and BOLD-fMRI), fear ratings, and salivary and hair cortisol at both assessments. During acquisition training, at the beginning of extinction (i.e., fear recall) and reinstatement-test at T1, RA-exposed individuals showed less autonomic threat-safety discrimination than unexposed individuals, mainly driven by blunted threat signal responding. These group differences predicted depression levels at the 1 and 1.5 year follow-ups and were mirrored by distinct activation in key fear-related brain regions, including striatal regions, thalamus, vmPFC, insula, and amygdala, whereas self-reported fear was unrelated to RA. In conclusion, across multiple outcomes, RA shapes threat learning and retrieval under conditions of (potential) imminent threat, with reduced autonomic threat discrimination prospectively predicting elevated depressive symptoms at 1- and 1.5-year follow-up. Overall, these findings implicate altered threat learning and retrieval processes as a potential mechanistic pathway through which RA becomes biologically embedded, potentially elevating psychopathological risk.

Biological psychology links mental phenomena and physiological processes, advancing our understanding of the human condition and informing clinical applications. Unlocking this potential demands rigorous and transparent methodology. Artificial Intelligence (AI), encompassing generative (e.g., large language) models and machine-learning classifiers, promises to advance open science in this field through automated literature synthesis, experimental design assistance, and enhanced code documentation. Yet the same tools risk undermining transparency through algorithmic opacity, systematic biases, and non-reproducible outputs. In this paper, we critically examine how AI can support or hinder open science practices throughout the biopsychological research cycle. We discuss current AI applications at each research stage and explore their potential benefits (e.g., accelerated systematic reviews, stimulus generation, metadata formatting) alongside relevant concerns (e.g., premature study exclusion, confabulated citations, amplified publication bias). To provide practical guidance, we offer recommendations on how AI may be used to promote the adoption and implementation of open science practices at each step of the biopsychological research cycle. Alongside potential pitfalls and a future vision, these guidelines equip biopsychological researchers with actionable support for integrating AI-based methods into their work in line with open science principles.

Replicability is a cornerstone of scientific progress. Yet, replications are often undervalued, and are sometimes seen as redundant, unimportant, or lacking novelty. This impedes their broader adoption in research and beyond. In response, the credibility revolution calls for slower, more deliberate science and greater responsiveness to fallibility. In this perspective piece, we argue that (a) replications are essential for validating scientific claims, (b) replications need to be made more visible, recognized, and integrated into research and educational practices, and (c) we can change the way we view and judge replication results. We propose a framework where replication studies can be systematically tracked and normalized through the Replication Hub as part of the Framework for Open and Reproducible Research Training (FORRT) initiative, with the goal of enhancing the visibility, integration, and cumulative impact of replication research across disciplines.

Replicability is a cornerstone of scientific progress. Yet, replications are often undervalued, and are sometimes seen as redundant, unimportant, or lacking novelty. This impedes their broader adoption in research and beyond. In response, the credibility revolution calls for slower, more deliberate science and greater responsiveness to fallibility. In this perspective piece, we argue that (a) replications are essential for validating scientific claims, (b) replications need to be made more visible, recognized, and integrated into research and educational practices, and (c) we can change the way we view and judge replication results. We propose a framework where replication studies can be systematically tracked and normalized through the Replication Hub as part of the Framework for Open and Reproducible Research Training (FORRT) initiative, with the goal of enhancing the visibility, integration, and cumulative impact of replication research across disciplines.

Replicability is a cornerstone of scientific progress. Yet, replications are often undervalued, and are sometimes seen as redundant, unimportant, or lacking novelty. This impedes their broader adoption in research and beyond. In response, the credibility revolution calls for slower, more deliberate science and greater responsiveness to fallibility. In this perspective piece, we argue that (a) replications are essential for validating scientific claims, (b) replications need to be made more visible, recognized, and integrated into research and educational practices, and (c) we can change the way we view and judge replication results. We propose a framework where replication studies can be systematically tracked and normalized through the Replication Hub as part of the Framework for Open and Reproducible Research Training (FORRT) initiative, with the goal of enhancing the visibility, integration, and cumulative impact of replication research across disciplines.

Replicability is a cornerstone of scientific progress. Yet, replications are often undervalued, and are sometimes seen as redundant, unimportant, or lacking novelty. This impedes their broader adoption in research and beyond. In response, the credibility revolution calls for slower, more deliberate science and greater responsiveness to fallibility. In this perspective piece, we argue that (a) replications are essential for validating scientific claims, (b) replications need to be made more visible, recognized, and integrated into research and educational practices, and (c) we can change the way we view and judge replication results. We propose a framework where replication studies can be systematically tracked and normalized through the Replication Hub as part of the Framework for Open and Reproducible Research Training (FORRT) initiative, with the goal of enhancing the visibility, integration, and cumulative impact of replication research across disciplines.

Replicability is a cornerstone of scientific progress. Yet, replications are often undervalued, and are sometimes seen as redundant, unimportant, or lacking novelty. This impedes their broader adoption in research and beyond. In response, the credibility revolution calls for slower, more deliberate science and greater responsiveness to fallibility. In this perspective piece, we argue that (a) replications are essential for validating scientific claims, (b) replications need to be made more visible, recognized, and integrated into research and educational practices, and (c) we can change the way we view and judge replication results. We propose a framework where replication studies can be systematically tracked and normalized through the Replication Hub as part of the Framework for Open and Reproducible Research Training (FORRT) initiative, with the goal of enhancing the visibility, integration, and cumulative impact of replication research across disciplines.

The Spielberger State-Trait Anxiety Inventory (STAI) is the most cited measure of state and trait anxiety, and is routinely employed in a variety of research and clinical contexts. Here, we investigate the temporal stability as well as the convergent and discriminant validity of the German version of the STAI trait scale (STAI-T) across multiple time points in two independent samples (105 and 120 Caucasians). We observed temporal stabilities of .42-.67 for intervals between 20 and 41 months and from .81-.87 for intervals of five to 12 months, with decreasing stability as the time interval increased. Temporal stability estimates of the STAI-T were similar to those of related constructs. Additionally, examining the relationships within a nomological network support the recent conclusion that the STAI-T also shares substantial variance with questionnaires measuring negative emotionality such as depression, and hence does not measure anxiety specifically - despite its name. These results provide further psychometric information on what the STAI-T actually measures and to what extent STAI-T scores are expected to be stable across longer time intervals. This is of relevance for researchers aiming, for example, to use the STAI-T scale for predicting symptom trajectories and evaluating the effectiveness of therapeutic interventions.

The Spielberger State-Trait Anxiety Inventory (STAI) is the most cited measure of state and trait anxiety, and is routinely employed in a variety of research and clinical contexts. Here, we investigate the temporal stability as well as the convergent and discriminant validity of the German version of the STAI-T scale across multiple time points in two independent samples (105 and 120 Caucasians). We observed temporal stabilities of .42 - .67 for intervals between 20 and 41 months and from .81 - .87 for intervals of five to 12 months, with decreasing stability as the time interval increased. Temporal stability estimates of the STAI-T were similar to those of related constructs. Additionally, examining the relationships within a nomological network support the recent conclusion that the STAI-T also shares substantial variance with questionnaires measuring negative emotionality such as depression, and hence does not measure anxiety specifically – despite its name. These results provide further psychometric information on what the STAI-T actually measures and to what extent STAI-T scores are expected to be stable across longer time intervals. This is of relevance for researchers aiming, for example, to use the STAI-T scale for predicting symptom trajectories and evaluating the effectiveness of therapeutic interventions.

Childhood adversity is a strong predictor of developing psychopathological conditions. Multiple theories on the mechanisms underlying this association have been suggested which, however, differ in the operationalization of ‘exposure.’ Altered (threat) learning mechanisms represent central mechanisms by which environmental inputs shape emotional and cognitive processes and ultimately behavior. 1402 healthy participants underwent a fear conditioning paradigm (acquisition training, generalization), while acquiring skin conductance responses (SCRs) and ratings (arousal, valence, and contingency). Childhood adversity was operationalized as (1) dichotomization, and following (2) the specificity model, (3) the cumulative risk model, and (4) the dimensional model. Individuals exposed to childhood adversity showed blunted physiological reactivity in SCRs, but not ratings, and reduced CS+/CS- discrimination during both phases, mainly driven by attenuated CS+ responding. The latter was evident across different operationalizations of ‘exposure’ following the different theories. None of the theories tested showed clear explanatory superiority. Notably, a remarkably different pattern of increased responding to the CS- is reported in the literature for anxiety patients, suggesting that individuals exposed to childhood adversity may represent a specific sub-sample. We highlight that theories linking childhood adversity to (vulnerability to) psychopathology need refinement.