Discussion
In this work, we studied the relationship between both ASD and
schizotypy traits, recently demonstrated to have overlapping
characteristics (Ford et al., 2018; Abu-Akel et al., 2017; Nenadić et
al., 2021), and their association with cognition and behaviour probed
through a gravity tracking task (Meso et al., 2020). Both ASD and
Schizophrenia have been found to disrupt ocular motor function (Barnes,
2008; Johnson, Lum, Rinehard & Fielding, 2016) and questions remain
about the commonality of the specific disruption. In this context, the
work involved a conceptual replication and extension of research which
introduced the gravity ball tracking task. We found that participants
were much better at tracking under the familiar gravity condition than
when the direction was inverted in the antigravity condition (Meso et
al., 2020). This tracking task was different from more widely used
predictive sinusoidal tracking with blanking along the trajectory
(Faiola, Meyhöfer & Ettinger, 2020), as participant performance could
be improved by the use of knowledge about gravity (Jörges &
López-Moliner, 2017). In the current work, we similarly demonstrated an
advantage for the downward gravity condition over others and unpack the
multitude of results from the multivariate eye movement and inventory
measures by answering three broad questions. We first interpret the
findings in the context of replicating and extending Meso et al.,
(2020), with the inclusion of the ASD inventory and controls. Second, we
unveil differences in tracking dynamics and discuss their implications.
Finally, we characterise the multidimensional relationship between eye
movements, ASD traits and Schizotypy.
Replication of the gravity advantage in tracking
Participants were asked to track a moving ball in repeated trials within
blocks of separate gravity conditions. As such, within two or three
trials from the start of the block, participants could implicitly or
explicitly expect that the vertical component in the gravity and
antigravity conditions would have the same acceleration over trials
within the block. Under the gravity condition, all participants were
good at tracking throughout the half-second duration from motion onset
we analysed. Tracking was about 20% better than under the control
condition, in which gravity acted in the rightward direction, suggesting
that implicit knowledge of the physics-based expectations of gravity
enhanced visual tracking (similarly suggested by Jörges &
López-Moliner, 2019). The antigravity condition engendered much larger
individual differences in responses and poorer tracking. Some
participants were able to anticipate the accelerating response in the
upward direction before the visual system could produce a
stimulus-driven response, and most were able to improve to a point at
which tracking performance was best and at a plateau matched across all
conditions by about 300ms from onset. In the current work, we sought to
better understand these dynamic differences across conditions. Meso et
al., (2020) used linear mixed models to control for random effects and
identify a relationship between Schizotypy trait levels and both RMSE
and saccades which involved an interaction with gravity direction
condition. Here, we have extended this previous work, aiming to better
understand how the dynamic eye response depended on violations of the
physics of gravity and trait characteristics.
Schizotypy and ASD trait relationships
We used two established and quick-to-answer inventories: one for ASD –
the SATQ (Kane et al., 2012) and the other for schizotypy - the SPQ
(Raine, 1991). Individual scores for the two inventories were found to
be highly correlated. Large recent studies have shown that the two
conditions are related and suggested that individuals with a clinical
ASD diagnosis were about 3.5 times more likely to receive a concurrent
diagnosis of schizophrenia (Zheng, Zheng & Zou, 2018). Similarly,
overlaps have been identified in traits of both ASD and Schizotypy in
healthy undiagnosed participants across multiple cultures (Abu-Akel et
al., 2017; Nenadić et al., 2021). The heterogeneity of ASD and
Schizotypy traits means that the overlap can also be separated into
quite distinct clusters; for example, a first encompassing negative
schizotypy and poor social communication (ASD), a second encompassing
positive schizotypy and attention to detail (ASD) and a third with less
specific overlap (Abu-Akel et al., 2017; Nenadić et al., 2021). Indeed,
our two inventories decomposed the trait measures into three broad
dimensions (from nine smaller ones) for Schizotypy and five dimensions
for ASD (Raine, 1991; Kane et al., 2012). We expected some of these
subtrait dimensions to be associated with the specific eye movement
metrics. When we measured the correlation between both overall single
trait levels and various eye movement measures, there were
unsurprisingly no significant relationships. Instead, this motivated the
use of a data-driven approach to parcel out the variance into the
various contributions in a similar way to what was achieved by the
linear mixed models in Meso et al., (2020), to observe more subtle
relationships.
Gravity direction performance and dynamics
The gravity condition was generally found to be better performed than
the antigravity condition. When the saccades were compared between these
conditions, there was a significant difference between rate of saccades
produced, with fewer saccades under the gravity conditions when compared
to the antigravity conditions. However, the sizes of these saccades were
not different. This may have been driven by more catch-up saccades in
the antigravity condition. We separated the tracking measure of RMSE
into four response epochs from anticipatory at onset, sensory response
threshold, open-loop response and finally a closed-loop response. This
was done to reduce the complexity of the continuous tracking responses
and capture key ocular motor signatures that isolate anticipatory
responses, in the absence of the stimulus from the earliest unelaborated
sensory responses and later responses which incorporate sensorimotor
feedback loops (Masson & Perrinet, 2012; Meso et al., 2022). We first
looked at the correlation between the RMSE in the vertical response for
both the gravity and antigravity conditions across the four time
windows. All four windows showed a significant relationship between
performance in both gravity conditions, with the relationship weakest in
the anticipatory responses occurring before the sensory response was
visible and getting progressively stronger, with a peak in the last
closed loop epoch. This finding suggests that the anticipatory response
is at least partially driven by separate mechanisms in the gravity and
antigravity condition, with downward gravity able to draw on prior
knowledge about the physics rules in a fast, pre-attentive way (Jörges
& López-Moliner, 2017). Further, in the antigravity condition, the RMSE
responses themselves were also poorest for the anticipatory condition
and subsequently less poor at the threshold of stimulus response in the
second epoch considered. Although, in both these earlier windows, RMSE
was significantly different from the gravity response. In the latter two
windows, responses were not different across the gravity and antigravity
conditions. Late in the ocular motor response, integrative processes
serving motion estimation incorporate visual feedback and therefore
error signals (Goettker, Braun, & Gegenfurtner, 2019; Meso et al.,
2022). By contrasting these later open-loop and closed-loop responses to
the earlier epochs, we therefore expected to be able to separate motion
perception deficits previously characterised (Barnes, 2008) from
specifically prediction deficits (Koychev et al., 2016). On top of that,
in contrasting the gravity and antigravity conditions in the
multivariate analysis that followed, we have prediction scenarios
involving the overfamiliar case of downward gravity and the
rule-contravening case of antigravity, which unpacked some seeming
contradicting results on tracking deficits in schizotypy (Koychev et
al., 2016; Meyhöfer et al., 2015). We also calculated a measure of
learning to quantify improvement of performance over the course of a
block and found that learning improved more in the antigravity than
gravity condition, with a larger improvement of almost two and a half
times in the accelerating vertical direction when compared with the
constant speed horizontal direction. Participants were therefore able to
get slightly better over the course of the trials, particularly for the
antigravity condition in anticipating the upwards direction. This
learning could not however extend to a level to match the established
prior knowledge about the physics of gravity. The dynamic performance
measures, saccade metrics and learning measures were therefore all of
interest in the multivariate analysis involving trait measures.
Multidimensional patterns identified
We used PCA to unpack the relationships between the different variables
in this experiment and link the trait measures to the eye movements. PCA
has previously been used to study trait measures from healthy
populations (Nenadić et al., 2021) and with eye-tracking metrics (Meso
et al., 2020). They were ideal in this scenario given the multiple
dimensions of the inventories (Raine, 1991; Kane et al., 2012)
reflecting the heterogeneity of the constructs of interest – ASD trait
and schizotypy. We used a simple correlation form of PCA and parallels
analysis to restrict our components of interest to seven (Joliffe and
Cadima, 2016). The ordered components become progressively less strong
and therefore less dominant in the explanation of the trends in the
results. The first two of these components explaining 38% of the
variance separately captured all-round eye movement performance at 20%
and non-specific overlap between all dimensions of both inventories at
about 18%. Interestingly, these dominant components may explain why
direct correlations between individual eye movement measures and the
inventories might not be expected to identify strong links. Instead,
these substantial separated parts of the variance may account for the
strong correlation between inventories, which has a non-specific profile
overlap previously proposed (Abu-Akel et al., 2017; Zheng, Zheng & Zou,
2018).
The next two components together accounting for just under 20% of the
variance were the most interesting in the current work. These separated
the negative schizotypy dimension from the positive dimension. In the
first of these, the Positive schizotypy was associated with the ASD
trait dimensions of oddness and rigidity and related with opposite sign
to the ability to read faces. This relationship is consistent with that
identified by Nenadić et al. (2021). This component was also linked to
tracking performance in the constant speed horizontal direction during
the stimulus response, i.e. a tracking eye movement that is more general
and not dependent on prediction. In the vertical direction, however, the
association was with the antigravity condition in the anticipatory
response and antigravity learning metrics. Therefore, these eye
movements suggest for the first time that this positive-oddcluster of traits may relate to general motion tracking, while also
associated with an ability to adapt to motion that contravenes
expectations of the physics of gravity. This finding extends that of
Meso et al., (2020), as well as previous work using blanking paradigms
(Koychev et al., 2016).
The second of these components links the negative dimension of
schizotypy with the ASD trait dimensions of social interaction and
rigidity, consistent with previous work (Nenadić et al., 2021; Ford et
al., 2018). This negative-social component is also strongly
associated with tracking in the gravity direction, specifically under
the gravity condition, both in the anticipatory and the later open loop
response. This result is therefore consistent with the possibility that
participants’ use of long term learned prior information about gravity
could be associated specifically with this negative-social dimension.
Indeed, it has been hypothesised that Schizophrenia is driven by poor
predictive mechanisms that should be based on learned rules (Millard,
Bearden, Karlsgodt, & Sharpe, 2022) and gravity could represent such a
prior (Jörges & López-Moliner, 2017; Meso et al., 2020).
Each of the three remaining components contributing just under 18% of
the total variance were less reliably interpretable, either because of a
potentially spurious high age-related correlation, non-specific
associations with either eye movements or equally non-specific
relationships between inventory responses. The key finding in the
current work is therefore in the characteristic eye movements associated
with the positive-odd and negative-social trait
dimensions. Interestingly, in both cases, there is an association with
predictive tracking. In the case of negative-social , this is the
application of physics rules associated with gravity. Following on from
the work of Faiola et al., (2020) and MacNeilage & Glasauer, (2018), we
can make a testable prediction that downward gravity stimuli will
specifically drive predictive responses in both the FEF and the
cerebellum. On the other hand, the positive-odd case is
associated with the learning of physics rules that enable prediction but
contravene expectations of natural physics. In such a context, we expect
that there will still be contributions from the FEF to support the
anticipatory aspects of the task, but less of a contribution from the
cerebellum in terms of physics rules of gravity (MacNeilage & Glasauer,
2018; Dakin, Peters, Giunti & Day, 2018).
Conclusion
In this work, we showed that the participant tracking of the motion of a
ball subjected to downward gravity was faster and better than tracking
under the contravention of physics rules introduced by our antigravity
condition. Performance, however, revealed large individual differences
which we analysed along with inventories of ASD traits and schizotypy,
to unveil two key findings which come with testable predictions. The
first is that the positive-odd cluster of characteristics is expected to
be specifically associated with the use of learnt rules that contravene
physics, while the second is that the negative-social cluster is
expected to be associated with the use of an established potentially
pre-attentive prior. We therefore encourage colleagues to consider
testing these arising predictions in imaging, behavioural and clinical
studies.