Fig 1.
Screenshots of the Path Only (A) and Hoops Only (B) conditions from Experiment 1 and the Dense Trees (C) and Sparse Trees (D) conditions from Experiment 2.
Experiment 1 also included a Hoops and Path condition (not pictured) that combined the two key features of the Path Only and Hoops Only condition.
Fig 2.
Analyses of performance measures in Experiment 1: Mean speed (A), path deviation (B), number of collisions (C), and proportion of hoops completed (D).
Each measure is plotted as a function of block number, with the different colors representing different conditions. Error bars represent 95% CIs.
Fig 3.
(A) Bird’s eye view of virtual environment with the path highlighted by the dashed yellow line. The solid yellow lines indicate the curved sections of the path that were used in the analysis of signed path deviation. Curves are numbered 1 through 9. (B) Signed path deviation for each numbered section of the path in the Path Only, Hoop Only, and Hoops + Path conditions. Positive values correspond to an outside bias and negative values correspond to an inside bias.
Fig 4.
(A) Proportion of frames spent looking at the path, nearby trees, background, hoop center, and hoop edge in the Path Only, Hoops Only, and Hoops + Path conditions of Experiment 1. (B) Density plot of distance from fixation point to center of path. (C) Proportion of frames spent looking through the center of the hoop as a function of the amount of time until passing through the hoop. In B and C, the thin colored curves represent individual subject data and the thicker black curve represents the mean across subjects.
Fig 5.
Proportion of frames spent looking at the path, nearby trees, and background in the Dense Trees and Sparse Trees conditions of Experiment 2.
Fig 6.
(A) Illustration of how the trajectory to the most immediate hoop (Hoop N) may depend on position of subsequent hoop (Hoop N+1). (B) Predictor variables (angular position and relative orientation), outcome variable (approach angle), and (C) covariates (heading direction relative to N at N-1, angular position of N relative to N-1) used in linear model. (D) Illustration of outcome variable (approach angle) measured at different positions leading up to Hoop N.
Fig 7.
Partial R2 as a function of the percentage of segment with angular position (A) and relative orientation (B) as the predictor variable.
Colored curves show partial R2 based on individual subject data.