Munich Sentence Database - MuSe

This is an online interface presenting sentence completion norms described in the following preprint:

Sterner, E. F., Stadler, M., & Knolle, F. (2025, October 26). Munich Sentence (MuSe) Database – Completion norms and audio recordings for 619 German sentences. https://osf.io/preprints/psyarxiv/evr24_v1

Prediction is a core feature of language, which is widely studied across research domains. The Munich Sentence (MuSe) database enhances reproducibility by providing sentence completion norms for 619 German sentences, including cloze probabilities and entropy estimates from up to 232 participants. Sentence completions were collected in two online studies in which participants completed sentence beginnings with a single-word response after either hearing (auditory sample, N = 133) or reading (visual sample, N = 98) the sentence beginning. All responses were manually preprocessed to correct typos and spelling mistakes and to label grammatical errors, proper nouns, and singular and plural variants of the same response. In addition to the sentence norms, we provide trial-level data with participant-level demographic information and subclinical autistic and schizotypal trait measures. Together with open access R-Scripts or our webtool, this allows for tailoring the cleaning and norming steps to integrate individual difference measures. For a subset of 479 sentence beginnings, the database also includes professional audio recordings of sentence beginnings which can be flexibly combined with 531 recordings of unique sentence-final words and implemented in auditory language paradigms.

All material inclucing preprocessing and analysis scripts are freely accessible via the Open Science Framework (https://osf.io/ktnze/overview).

If you are using any of the material please cite:

Sterner, E. F., Stadler, M., & Knolle, F. (2025, October 26). Munich Sentence (MuSe) Database – Completion norms and audio recordings for 619 German sentences. https://osf.io/preprints/psyarxiv/evr24_v1

The interface was designed by Chuyang Wang (chuyang.wang(at)tum.de).