Louis Schiekiera

Decades of picture-word interference (PWI) research have produced a rich literature on how distractor words modulate picture naming, yet the trial-level data underlying these findings remain fragmented across laboratories, repositories, and bespoke file formats, limiting large-scale reuse and cross-study comparison. Here we present a harmonized, openly accessible dataset comprising 688,976 trials from 86 experiments across 42 studies, involving 3,353 participants naming pictures in English or German. Data were assembled from five sources: an in-house laboratory archive, raw data from a prior meta-analysis, systematic literature searches (OpenAlex, PubMed), direct author contributions, and the Open Science Framework. All datasets were cleaned and harmonized into a common 28-variable schema covering trial identifiers, response times, a harmonized response-accuracy code, experimental design characteristics, target–distractor relatedness indicators, and basic psycholinguistic properties of target and distractor words. Technical validation shows the expected reaction-time distributions across studies. The full data-collection and cleaning pipeline is openly released so that the resource is extensible: future PWI studies, additional languages, and further harmonized variables can be added by other researchers without rebuilding the infrastructure from scratch. The dataset enables mega-analyses, computational modeling of naming latencies, replication work, and methodological investigations that require statistical power beyond what individual studies can provide.

We investigate the extent to which an LLM’s hidden-state geometry can be recovered from its behavior in psycholinguistic experiments. Across eight instruction-tuned transformer models, we run two experimental paradigms—similarity-based forced choice and free association—over a shared 5,000-word vocabulary, collecting 17.5M+ trials to build behavior-based similarity matrices. Using representational similarity analysis, we compare behavioral geometries to layerwise hidden-state similarity and benchmark against FastText, BERT, and cross-model consensus. We find that forced-choice behavior aligns substantially more with hidden-state geometry than free association. In a held-out-words regression, behavioral similarity (especially forced choice) predicts unseen hidden-state similarities beyond lexical baselines and cross-model consensus, indicating that behavior-only measurements retain recoverable information about internal semantic geometry. Finally, we discuss implications for the ability of behavioral tasks to uncover hidden cognitive states.

Review studies suggest that results that are statistically significant or consistent with hypotheses are preferred in the publication process and in reception. The mechanisms underlying this bias remain unclear, and prior research has focused on between-subjects rather than within-subjects designs. We conducted a within-subjects study, grounded in dual-process decision-making theories, to examine these dynamics. Across four online experiments, 303 clinical-psychology researchers evaluated 16 fictitious abstracts varying in statistical significance and hypothesis consistency. Participants provided fast, intuitive judgments about each abstract’s likelihood of being submitted, read, or cited, rated their feeling of rightness (FOR), and gave deliberated evaluations. We analyzed the data using multilevel and mediation models. Researchers rated statistically nonsignificant abstracts as less likely to be submitted, read, or cited compared with significant ones. No such bias was found for hypothesis-inconsistent results. Intuitive judgments were rarely revised, and FOR did not predict response changes. Overall, researchers favored statistically significant results, with deliberation and FOR playing minimal roles.

The overrepresentation of positive results in psychology is often attributed in part to publication bias. However, the impact of research group output on the prevalence of positive results has not yet been investigated. The present study examines whether German clinical psychology research groups with high versus low publication outputs differ in the prevalence of positive outcomes in their publications. Scientific productivity was defined as the ratio of quantitative-empirical publications to the number of academic staff per chair. We analyzed publications authored by clinical psychology researchers at German universities from 2013 to 2022, sourced from PubMed and OpenAlex. After excluding meta-analyses, reviews, and non-empirical studies, 2,280 empirical studies from 99 research groups were identified. We then randomly sampled and coded 300 papers, evenly split between the highest and lowest output quartiles, and examined the first hypothesis. There was no statistically significant difference between the highest and the lowest output quartiles, with both reporting approximately 90% positive results. Higher group paper counts were not associated with more positive results. Exploratory abstract-level analyses showed no significant differences in positive result rates between all four output quartiles. Our results suggest a general excess of positive results in clinical psychology. Contrary to our hypothesis, German clinical psychology research groups with high and low publication outputs do not differ in the prevalence of positive outcomes in their publications.