Catching flies

[From John Anderson (950510.1630)]

Rick Marken (950509.2120)]

Bruce Abbott (950509.1240 EST) --

What do you DEFINE as PCT research? That is, to qualify as PCT
research, what elements must be present? I'd like to be able to
examine, say, a piece of published research and using your criteria,
classify it as PCT or non-PCT.

Excellent idea!! Here's my take on _real_ PCT research:

      [...5 criteria for published PCT research...]

Good luck on finding some good examples of PCT research out there in
the conventional literature.

Bruce and Rick, here's a paper from the 28 April 1995 issue of Science
you might consider:

        MK McBeath, DM Shaffer, MK Kaiser (1995) "How baseball outfielders
        determine where to run to catch fly balls" Science 268:569-573.

This paper seems to has a PCT flavor to it, and the topic was mentioned
recently (4/10) on CSG-L. Here's the abstract and a brief summary:

'Current theory proposes that baseball outfielders catch fly balls by
selecting a running path to achieve optical acceleration cancellation of
the ball. Yet people appear to lack the ability to discriminate
accelerations accurately. This study supports the idea that outfielders
convert the temporal problem to a spatial one by selecting a running path
that maintains a linear optical trajectory (LOT) for the ball. The LOT
model is a strategy of maintaining "control" over the relative direction
of optical ball movement in a manner that is similar to simple predator
tracking behavior.'

A previously-proposed model for how fly balls are caught is the optical
acceleration cancellation (OAC) model. In it, players run toward or away
from the ball along a line in the plane of its trajectory in such a way
that the ball rises at a constant rate. This is the model referred to by
Bill Powers (950410.0900 MST), responding to Bill Leach (950409.2231 EDT):

To your example of the ballplayer not chasing a ball that will go well
over his position, I can add another twist (that I've mentioned before).
An outfielder catching a fly ball headed straight toward him, it is
said, moves so as to keep the apparent rate of rise of the ball constant
and slow. If this perception is controlled, the ball will arrive within
catching range. If you look at the behavior of the ball player, it will
seem that the sight of the ball causes the player to run in anticipation
of the catch, but in fact something is under control all the way (or at
least intermittently as the player casts glances over his shoulder while
running toward the fence).

If the fielder runs too far in, the ball rises too fast; if he runs too
far out, it rises too slow. In the OAC view, trying to do this on a line
out of the trajectory plane of the ball, ie when the ball is not hit
directly at you, will be more difficult because then you have to take into
account both vertical _and_ horizontal motion parameters. But baseball
players agree that catching a fly ball hit directly at you is harder than
catching one that's not. Enter the linear optical trajectory (LOT) model.

In the LOT model, the fielder changes his position so that the ball's
trajectory traces out a straight line relative to home plate and the
background scenery. This requires the player to continuously move
directly under the ball, thus guaranteeing that he'll be in a position to
catch it.

Quoting the paper:

'The OAC model predicts that fielders select a running path that is
straight with constant speed, resulting in a curved optical ball
trajectory. The LOT model predicts that fielders select a running path
that curves out and has an upside-down-U-shaped speed function, resulting
in a linear optical ball trajectory.'

The authors did two kinds of experiments. In the first, they let
somewhat-experienced outfielders catch fly balls hit in a number of
directions with different initial velocities, and recorded their movements
on video tape on a tower above and behind the fielders, to see the shape
of the path they ran. In the other, they mounted video cameras on the
fielders' shoulders, and taped the ball's path relative to the fielder, to
see the shape of the ball's trajectory. To make a long story short, in
most of the cases where the balls were caught (31 in each expt), the
predictions of the LOT model were supported but those of the OAC model
were not. In the few cases where the ball was hit straight at the
fielder, the OAC model _was_ supported, but the authors consider this
situation as an "accidental view that may require an alternative

No mention of PCT in the paper, but "control" and "perception" are
mentioned several times. Might be worth a look from a PCT point of view.