**Preprocessing.** Task performance will be analyzed offline. Trials will be included for analysis, if:
- fixation control is passed.
- the saccade is successfully detected and the eye crossed the boundary around the initial target location (i.e., target area).
- the saccade is not falsely detected online.
- there are no blinks during saccade flight.
- the stimulus movement (as indicated by trial timestamps) is concluded before saccade offset.
- there were no dropped frames during the movement of the stimulus
- a saccade is successfully detected offline.
- the participant made one saccade (rather than two or more) in order to reach the target location.
Trials are repeated during the experiment in case fixations were not passed or saccades were not executed properly.
In addition, participants may be excluded from the analysis in case their understanding of the task was wrong or if too few trials could be collected, e.g., due to insufficient calibration, or an inability to make accurate saccades. At least half of the planned trials will have to be completed and valid to justify the inclusion of the participant's data.
**Analyses.**
1. Analysis of mean scene identification performance in all experimental conditions (movement duration x color). Dependent variables will be analyzed using repeated-measures ANOVAs, as well as using general linear mixed-effects models (Bates et al., 2007; Moscatelli et al., 2012). Individual paired t-tests with the appropriate corrections for repeated testing may be used to compare individual conditions.
2. Saccades will be detected offline using the Engbert-Kliegl algorithm (Engbert & Kliegl, 2003; Engbert & Mergenthaler, 2006). We will analyze the effects of saccade parameters (e.g., saccade amplitude, peak velocity, speed during presentation) on correct scene identifications.
3. We will perform a categorization based on scene content, as provided in the SYNS dataset and analyze identification performance within and across categories.
4. We will simulate the neural responses of V1 cells to visual features, such as components of spatial frequency and orientation for each color/grayscale channels (e.g., Samonds et al., 2018), using Gabor filters on the image data (relative to measured gaze position) to perform reverse-correlation analyses (e.g., Wyart et al., 2012; Li et al., 2016). Using this technique, we will be able investigate what image features are used to perform the judgment. Alternatively, we will analyze global image features to perform reverse regression.
**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.
Bates, D., Sarkar, D., Bates, M. D., & Matrix, L. (2007). The lme4 package. R package version, 2(1), 74.
Engbert, R., & Kliegl, R. (2003). Microsaccades uncover the orientation of covert attention. *Vision research, 43*(9), 1035-1045.
Engbert, R., & Mergenthaler, K. (2006). Microsaccades are triggered by low retinal image slip. *Proceedings of the National Academy of Sciences, 103*(18), 7192-7197.
Li, H. H., Barbot, A., & Carrasco, M. (2016). Saccade preparation reshapes sensory tuning. *Current Biology, 26*(12), 1564-1570.
Moscatelli, A., Mezzetti, M., & Lacquaniti, F. (2012). Modeling psychophysical data at the population-level: the generalized linear mixed model. *Journal of vision, 12*(11), 26-26.
Samonds, J. M., Geisler, W. S., & Priebe, N. J. (2018). Natural image and receptive field statistics predict saccade sizes. *Nature Neuroscience, 21*(11), 1591.
Wyart, V., Nobre, A. C., & Summerfield, C. (2012). Dissociable prior influences of signal probability and relevance on visual contrast sensitivity. *Proceedings of the National Academy of Sciences, 109*(9), 3593–3598.
