time it takes to cross the required distance, and time is typically a factor in these sports. So, a emerged for the long distance targeted throwing task in the context of these sports, namely, hit the target in brief time. This, along with individual differences, appeared to determine which portions of the affordance space the throwers used. Now that we have empirically characterised the behaviour of expert throwers, we turned to our affordance analysis to see whether this can explain the specific combinations of chosen release parameters. Simulation Analysis We implemented the equations of projectile motion in a bespoke Labview programme (National Instruments; see Appendix 1 for details). We specified the tennis ball (mass, drag coefficient and radius) and the target (as a 4ft diameter circle set at some distance and height) and computed the outcome (hit or miss) of each simulated throw. We ran the equations within two loops, one for variation in release angle (ranging from -30° to 90°; this range emerged from the simulations as necessary to map the complete affordance structure for all target locations) and one for variation in release speed (here ranging from 0ms-1 to 45ms-1; our highest observed release speed across the two experiments reported below was 45.01ms-1). The number of increments for each loop defines the simulation resolution. We set these to 400 for each loop yielding 16,000 data points per plot. The resulting simulations were dense enough to reveal the affordance structure but not so dense as to require more than a standard PC to generate in a reasonable amount of time. For this experiment, we generated 117 simulations (3 target distances x 3 target heights x 13 release heights (1.5m-2.7m in 0.1m increments, which spans the observed range of heights). We produced heat maps in Matlab R2013a (Mathworks) to illustrate which regions of the space produced hits and how this changed with changes in target parameters. Target Affordances
time it takes to cross the required distance, and time is typically a factor in these sports. So, a emerged for the long distance targeted throwing task in the context of these sports, namely, hit the target in brief time. This, along with individual differences, appeared to determine which portions of the affordance space the throwers used. Now that we have empirically characterised the behaviour of expert throwers, we turned to our affordance analysis to see whether this can explain the specific combinations of chosen release parameters. Simulation Analysis We implemented the equations of projectile motion in a bespoke Labview programme (National Instruments; see Appendix 1 for details). We specified the tennis ball (mass, drag coefficient and radius) and the target (as a 4ft diameter circle set at some distance and height) and computed the outcome (hit or miss) of each simulated throw. We ran the equations within two loops, one for variation in release angle (ranging from -30° to 90°; this range emerged from the simulations as necessary to map the complete affordance structure for all target locations) and one for variation in release speed (here ranging from 0ms-1 to 45ms-1; our highest observed release speed across the two experiments reported below was 45.01ms-1). The number of increments for each loop defines the simulation resolution. We set these to 400 for each loop yielding 16,000 data points per plot. The resulting simulations were dense enough to reveal the affordance structure but not so dense as to require more than a standard PC to generate in a reasonable amount of time. For this experiment, we generated 117 simulations (3 target distances x 3 target heights x 13 release heights (1.5m-2.7m in 0.1m increments, which spans the observed range of heights). We produced heat maps in Matlab R2013a (Mathworks) to illustrate which regions of the space produced hits and how this changed with changes in target parameters. Target Affordances
wilson-et-al-in-press-using-task-dynamics-to-quantify-the-affordances-of-throwing-for-long-distance-and-accuracy1.docx