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.