Figure 3: Relationship between trait measures and saccade metrics and visualisations of estimates of learning through performance improvements over conditions (see text for details). Eye movements here are for the slow condition only. (a) to (d) Show weak negative correlation between SPQ/SATQ and saccade rates under both gravity (G) and antigravity (AG) conditions; those for gravity have higher values. (e) to (h) Show weak correlations between SPQ/SATQ and saccade sizes/amplitudes. (i) and (j) saccade rates and sizes show little difference between gravity conditions. (k) and (l) RMSE learning i.e. performance improvement during task shows no difference between gravity and antigravity for the horizontal (x-direction), but more improvement during the task for the vertical direction (y-direction) under the antigravity condition with a median of 0.19° compared with -0.02°.
Principal component analysis
Given the number of variables measured, we could not answer all questions of interest using standard correlations and hypothesis-based statistical tests without an explosion of familywise error. Like others (Nenadić et al., 2021, Meso et al., 2020) we took a data-driven approach and identified variables of interest for a correlation-based PCA. We selected a set of 21 measures from over 200 possible measures in the multivariate experiment including the AGE, the three main clusters of the SPQ, the five sub-traits of the SATQ, and eye tracking with both saccade and RMSE measures for the G and AG condition. Table 1 contains the PCA results with the 21 variables listed and their loadings for the first seven components. These seven components in the table (in descending order of their strength) are selected based on parallels analysis restricting explained data variance to 75%. We use the data loadings to guide our qualitative description of each of the orthogonal components. The first component is dominated by eye movement measures with little contribution from AGE and the inventories. It likely captures individual differences in eye movements which make some participants better at eye-tracking tasks than others. The second component captures general overlap between the SPQ and the SATQ, unrelated to eye movements. The third component is dominated by thepositive cluster of the SPQ and the odd, face and rigiditytraits of the SATQ. In addition, eye movement for open loop constant speed tracking (160ms) and anticipatory antigravity tracking responses and learning. The fourth captured negative SPQ and social interaction and rigidity of the SATQ . For eye movements, saccade rates/amplitudes and open loop and anticipatory tracking under the gravity condition. The fifth capturedsocial interactions and odd clusters of the SATQ and for eye movements, saccade rates and learning under the gravity condition. Both the fourth and fifth components had a contribution from AGE, but this was not considered reliable because of our narrow spread of participant ages. The sixth and seventh components were difficult to characterise, with the former being predominantly driven by eye movements and the latter by traits.