The impact of visually simulated self-motion on predicting object motion-A registered report protocol

Area of Science:

• Visual Perception
• Motion Perception
• Human-Computer Interaction

Background:

• Accurate prediction of object motion is essential for successful environmental interaction.
• Self-motion perception can influence object velocity estimation due to incomplete retinal flow parsing.
• Biases and increased variability in velocity estimation can impair motion extrapolation accuracy.

Purpose of the Study:

• To investigate how simulated self-motion affects velocity estimation and motion extrapolation.
• To determine if incomplete flow parsing during self-motion introduces biases and noise in object motion prediction.
• To examine the correlation between accuracy and precision in velocity estimation and motion extrapolation tasks.

Main Methods:

• Two tasks were conducted in a virtual reality (VR) environment: a motion prediction task and a speed discrimination task.
• Participants estimated when a moving object would reach a target while experiencing simulated visual self-motion (same or opposite direction).
• A two-interval forced choice task assessed speed judgments of a moving ball under static and simulated self-motion conditions.

Main Results:

• Simulated self-motion is expected to bias velocity estimates, leading to overestimation of speeds for objects moving opposite to self-motion.
• Increased variability in velocity judgments is anticipated during simulated self-motion.
• Biases and variability in velocity estimation are expected to translate to reduced accuracy and precision in motion extrapolation.

Conclusions:

• Incomplete flow parsing during self-motion significantly impacts the accuracy and precision of object motion prediction.
• Understanding these effects is crucial for designing more effective virtual reality systems and human-computer interfaces.
• Future research should explore individual differences in susceptibility to these perceptual biases.