**Preprocessing.** Same as Experiment 1, see [https://osf.io/f5q7v/][1]. To allow for different and potentially more reliable definitions of saccade offset, we will detect post-saccadic oscillations (PSO), e.g., by using the Nyström-Holmqvist algorithm (Nyström & Holmqvist, 2010), to able to use PSO onset as saccade offset.
**Analyses.** Analyses will largely be similar to those conducted for Experiment 1 ([https://osf.io/f5q7v/][1]). In addition, we will compute presentation offsets relative to saccade offsets and use this continuous predictor to compute individual time-resolved masking functions (by fitting psychophysical functions or logistic generalized additive models to response data) for observers and scene categories. Scene categories will be defined by location codes included in the SYNS database (Adams et al., 2016) or on a feature-level, for instance, by the scenes' spatial frequency and orientation content.
**References**
Adams, W. J., Elder, J. H., Graf, E. W., Leyland, J., Lugtigheid, A. J., & Muryy, A. (2016). The southampton-york natural scenes (syns) dataset: Statistics of surface attitude. Scientific reports, 6, 35805.
Nyström, M., & Holmqvist, K. (2010). An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data. Behavior research methods, 42(1), 188-204.
[1]: https://osf.io/f5q7v/
