Journal
SCIENTIFIC REPORTS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-01733-1
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Funding
- University of Tsukuba Basic Research Support Program Type A
- JSPS KAKENHI [19K11460, 18KK0286]
- Grants-in-Aid for Scientific Research [18KK0286, 19K11460] Funding Source: KAKEN
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This study investigated how stimulus and behavioral histories of past trials influence the control of predictive pursuit of target motion with randomized velocities. Results show that predictive pursuit responses were observed in both predictable and unpredictable conditions. The goodness of fit of the LME model improved when historical effects were fitted together rather than separately, suggesting that predictive pursuit systems use weighted averaging of past information to track randomized target motion.
The smooth pursuit system has the ability to perform predictive feedforward control of eye movements. This study attempted to examine how stimulus and behavioral histories of past trials affect the control of predictive pursuit of target motion with randomized velocities. We used sequential ramp stimuli where the rightward velocity was fixed at 16 deg/s while the leftward velocity was either fixed (predictable) at one of seven velocities (4, 8, 12, 16, 20, 24, or 28 deg/s) or randomized (unpredictable). As a result, predictive pursuit responses were observed not only in the predictable condition but also in the unpredictable condition. Linear mixed-effects (LME) models showed that both stimulus and behavioral histories of the previous two or three trials influenced the predictive pursuit responses in the unpredictable condition. Intriguingly, the goodness of fit of the LME model was improved when both historical effects were fitted simultaneously rather than when each type of historical data was fitted alone. Our results suggest that predictive pursuit systems allow us to track randomized target motion using weighted averaging of the information of target velocity (stimulus) and motor output (behavior) in past time sequences.
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