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Altered processing of fear and safety learning has been linked to individual differences of anxiety symptoms and anxiety-related personality traits, such as neuroticism, intolerance of uncertainty, and negative emotionality. However, the literature is limited by individual studies focusing on different aspects of fear conditioning and extinction, using varying outcome measures, and assessing different anxiety-related traits, mostly in isolation. This heterogeneity poses a challenge to cumulative knowledge building on g the overall relationship between anxiety-related traits and fear learning processes. To bridge this gap, a comprehensive systematic review and meta-analysis (Registered Report) was performed, examining how anxiety-related traits relate to fear acquisition and extinction. Following a thorough screening of titles, abstracts, and full texts, over 90 studies were included in the final analysis. Additionally, an item-level content analysis was conducted on the questionnaires used to assess anxiety-related traits in the included studies (n = 28) to also evaluate the conceptual overlap among these constructs. The presentation will share preliminary findings from both analyses, discuss their implications for the field of research and highlight directions for future research in this field.

Quantifying the strength of the relationships between brain rhythms and cognition remains a core interest in cognitive neuroscience. Yet, despite a long-standing emphasis on reporting and interpreting effect sizes, reflected in journal guidelines and publication manuals, the field is still overly focused on statistical significance. In this large-scale proof-of-concept study, we aim to provide a comprehensive meta-analytic assessment of the landscape of effect sizes and reporting standards, using the established effect of attentional modulations of alpha power. We identified 4601 records, out of which 200 passed the abstract and full-text screening stage. Out of those 200 studies, only 46% provided sufficient information to either extract or compute a measure of effect size for alpha power lateralization. The primary reasons for missing effect sizes were the use of cluster-based permutation tests (52%), incomplete reporting of statistical parameters (35%), and the lack of appropriate tests against zero (13%). A descriptive analysis of the dataset reveals that most studies investigated anticipatory, endogenous shifts of attention in the visual domain, whereas other timescales of attention (e.g., retro-active shifts of attention), exogenous cuing, or other modalities (e.g., auditory/tactile) are less frequently targeted. A preliminary meta-analysis of 92 studies revealed a medium-sized effect. The data also show clear signs of significant funnel plot asymmetry. Ongoing analyses aim at assessing how effect sizes differ for a range of potential moderators, such as sensory modality, cue validity or target eccentricity. Implications for reporting standards in the field will be discussed.

Individual participant data (IPD) meta-analyses are increasingly regarded as a gold standard for evidence synthesis, offering substantial advantages over traditional aggregate-data approaches. However, these benefits come at the cost of considerable practical and methodological complexity. In this talk, I will use an ongoing IPD meta-analysis as a case example to illustrate key challenges and decision points in conducting IPD meta-analyses.

The presentation will focus on the full workflow, from initial data acquisition to statistical modeling. First, I will discuss challenges related to data collection, including study identification, data requests, and common barriers to data sharing, such as legal and ethical constraints. Second, I will address issues of data harmonization, including the alignment of heterogeneous measures, handling of missing data, and ensuring comparability across studies. Third, I will outline key analytical decisions, with a particular focus on the comparison between one-stage and two-stage modeling approaches and their implications for inference.

The talk aims to provide a transparent account of the practical realities of IPD meta-analyses, highlighting common pitfalls and strategies to address them. By doing so, it seeks to contribute to ongoing discussions on best practices and to support researchers considering IPD approaches in their own work.