Fishermen have exploited affordances since the beginning of time. A fishing
lure attempts to emphasize those aspects of a fish’s desired food, presenting
the strongest stimulus possible: if the fish is hungry, the stimulus of
the lure will trigger feeding. As seen in Fig. 3.6, fishing lures often look to a
human almost nothing like the bait they imitate.
What makes Gibson so interesting to roboticists is that an affordance is directly
perceivable. Direct perception means that the sensing process doesn’t
require memory, inference, or interpretation. This means minimal computation,
which usually translates to very rapid execution times (near instantaneous)
on a computer or robot.
But can an agent actually perceive anything meaningful without some
memory, inference, or interpretation? Well, certainly baby arctic terns don’t
need memory or inference to get food from a parent. And they’re definitely
not interpreting red in the sense of: “oh, there’s a red blob. It’s a small oval,
which is the right shape for Mom, but that other one is a square, so it must
be a graduate ethology student trying to trick me.” For baby arctic terns, it’s
simply: red = food, bigger red = better.
Does this work for humans? Consider walking down the hall and somebody
throws something at you. You will most likely duck. You also probably
ducked without recognizing the object, although later you may determine it
was only a foam ball. The response happens too fast for any reasoning: “Oh
look, something is moving towards me. It must be a ball. Balls are usually
hard. I should duck.” Instead, you probably used a phenomena so basic that