Jiali Song¹ (), Ginnie Wee¹, Benjamin Wolfe¹; ¹University of Toronto Mississauga

Prediction is a fundamental part of navigating our visual world. Although there is prior evidence of prediction in memory representations of dynamic natural scenes (representational momentum), there is relatively little empirical data on an explicit prediction task in this context. Our study investigates the extent to which drivers can accurately predict non-hazardous, everyday road scenes. To this end, we created a novel stimulus set of 3D videos of real road scenes, which we plan to make publicly available, recorded using a stereoscopic dashcam setup during urban and highway driving. On each trial, we showed observers a 2s preview (video or still image) of a road scene and asked them to select the image that best represents what they think the scene will look like 2s after the end of the preview in a 5AFC task. The alternatives were frames sampled from the video at 1s intervals and always included the correct (+2s) frame. We also manipulated the presence of stereoscopic depth information using a 3D display. In a sample of 48 licensed drivers who each performed 420 trials, we found that predictions were on average 0.29s farther in time than ground truth, and such bias towards the future was larger for video compared to still image previews. Prediction proportion correct was higher for video compared to still previews and for urban roads compared to highways, suggesting an important role for motion information and environmental density in prediction. Moreover, these effects were mainly driven by increased prediction precision with relatively small changes in the magnitude of future bias. Stereoscopic depth information had negligible effects on prediction performance. Our findings suggest that drivers can make predictions about road scenes, and these predictions are subject to biases similar to those affecting memory representations.

Acknowledgements: This work was supported by funding from NSERC Discovery Grant and University of Toronto XSeed Grant awarded to BW.