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.