Visually induced illusions of self-motion are often referred to as vection. This article developed and tested a model of responding to visually induced vection. We first constructed a mathematical model based on well-documented characteristics of vection and human behavioral responses to this illusion. We then conducted 10,000 virtual trial simulations using this Oscillating Potential Vection Model (OPVM). OPVM was used to generate simulated vection onset, duration, and magnitude responses for each of these trials. Finally, we compared the properties of OPVM’s simulated vection responses with real responses obtained in seven different laboratory-based vection experiments. The OPVM output was found to compare favorably with the empirically obtained vection data.

We examined whether vection strength could be modulated by altering the exposure duration to optic flow. Experiment 1 sourced 150 different video clips from various Japanese animation works which simulated self-motion. Despite large differences in the content of these video clips, we found a significant positive correlation between their play durations and their ratings of vection magnitude. Experiment 2 examined this relationship further using more tightly controlled visual motion stimuli. Vection was induced by presenting the motion of the same expanding grating stimulus for 8, 16, 32, or 64 seconds. While vection onset latencies remained constant across these four conditions, vection magnitude/strength was found to increase systematically with the exposure duration. As predicted by a recent computational model of vection, we conclude that subjective vection strength does depend on the exposure duration to optic flow.

Object tracking has been generally discussed in relation to attention, but it is quite possible that nonattentive low-level motion components are involved. To elucidate this issue, we examined temporal aspects of object tracking by using stimuli comprised of just a single attribute and those comprised of multiple attributes. High-level motion processes supposedly can process cross-attribute motion, while nonattentive low-level motion processes cannot handle such motion. In Experiment 1, we measured the upper temporal limits for within- and cross-attribute object tracking, using stimuli defined by several different attributes (luminance, motion, binocular disparity, flicker, and contrast). It was found that the temporal limits with within-attribute stimuli (4-5 Hz) were much higher than those with cross-attribute stimuli (2-3 Hz). These results suggest that mechanisms involved in within- and cross-attribute object tracking are partially different. We conducted two additional experiments to clarify the nature of this difference. In Experiment 2, we measured the temporal limits for classical apparent motion perception using the same stimulus combinations as for Experiment 1. The temporal limits with within- and cross-attribute stimuli were both between 4 and 5 Hz. These values corresponded to those of within-attribute object tracking but were faster than those of cross-attribute object tracking. In Experiment 3, we measured the temporal limit for voluntary shifts of attention that did not involve motion. Temporal limits quite similar to those for cross-attribute object tracking (2-3 Hz) were obtained. These results suggest that nonattentive motion mechanisms are involved in within-attribute object tracking, whereas attention-based mechanisms mediate cross-attribute object tracking.

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