themselves, because other animals need food too. To regulate their temperatures, they build all sorts of burrows and nests. They all have urges to reproduce (or their ancestors would not have evolved), so they need to seek mates and raise their young. So each species evolved machinery that enables its newborn offspring to do many things without any prior experience. This suggests that they start out with some built-in ‘If– >Do’ reaction-rules like these.
If a thing touches your skin, Do brush it away.
If that doesn’t work, Do move your body away.
If a light is too bright, Do turn your face away.
However, only a few of our If– >Do rules can be so simple as these ones are, because most of our human behaviors depend on the mental contexts that we are in. For example, a rule like “If you see food, then Do eat it” would force you to eat all the food that you see, whether or not you are hungry or need it. So those Ifs should also include some goals, as in, “If you are hungry, and you see food….” Otherwise, you’d be forced to sit on each chair that you see—or get stuck at every electrical switch, turning lights on and off repeatedly.
How does this relate to emotions and feelings? If you rapidly move your hand toward a fly, then that fly will quickly retreat, and it’s tempting for us to ‘empathize’ by attributing feelings like fear to that fly. However, we know enough about insect brains to be sure that they can’t support the kinds of complex cascades that we recognize as emotional.
In any case, this kind of ‘stimulus-response’ or ‘situated-action” model became quite popular in the early years of Psychology. Some researchers even maintained that it could explain all human behavior. However, there are problems with this.
One problem is that most rules will have exceptions to them. For example, If you drop an object, it may not fall down, if something else should intercept it. Your wristwatch will usually tell you the time, but not in the case that your watch has stopped. We could deal with some such problems by including exceptions in the Ifs of our rules—but sometimes those exceptions will have their own exceptions to them as well.
What happens when your situation matches the Ifs of several different rules? Then you’ll need some way to choose among them. One policy might arrange those rules in some order of priority. Another way would be to use the rule that has worked for you most recently. Yet another way would be to choose rules probabilistically.
However, when we face more difficult problems, simple If-Do rules won’t usually work, because we will need to look further ahead to imagine the futures each action might bring. So shortly, we’ll talk about more powerful, three-part rules that can help to predict the effects of each action.
If we have adequate sets of such If–>Do– >Then rules, then we can guess “What would happen if” before we carry an action out. Then, by doing this repeatedly, we can imagine more elaborate plans. We’ll return to this shortly, but first we’ll discuss how a system could learn simple If–>Do rules.
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All animals are born with ‘instincts’ like ‘get away from a quickly approaching object.’ Such built-in reactions tend to serve well so long as those animals stay in environments like those in which their instincts evolved. But when those worlds change, those creatures may need to be able to learn new ways to react. For example, when Joan perceives that oncoming car, she partly reacts instinctively, but she also depends on what she has learned about that particular kind of danger or threat. But how and what did she actually learn? We’ll come back to this toward the end of this book, because human learning is extremely complex, and here we’ll merely mention some ideas about how learning might work in some animals. During the 20th century, many well-known psychologists adopted this portrayal of how animals learn new If–>Do rules:
When an animal faces a new situation, it tries a random sequence of actions. Then, if one of these is followed by some ‘reward,’ then that reaction gets ‘reinforced.’ This makes that reaction more likely to happen when that animal faces the same situation.
This theory of ‘learning by reinforcement’ can be made to explain a good deal of what many kinds of animals do. Indeed, that theory was largely based on experiments with mice and rats, pigeons, dogs and cats, and snails. However, it does not help much to explain how people learn to solve difficult problems that require more complex series of actions. Indeed, deciding what to learn from these may be harder than actually solving those problems, and words like random, reward, and reinforce do not help us answer this two crucial questions:
How were the successful reactions produced? To solve a hard problem, one usually needs an intricate sequence of actions in which each step depends on what others have done. A lucky guess might produce one such step, but random choices would take far too long to find an effective sequence of them. We’ll discuss this below in Searching And Planning.
Which aspects of recent events to remember? For an If to work well, it must include only the relevant features, because one can be misled by irrelevant ones. (If you learned a new way to tie a knot, your Ifs should not mention the day of the week.) For as we’ll see in §8 Resourcefulness, if your description is too specific, then it will rarely match new situations—but if your description is too abstract, then it will match too many of them—and in either case, you won’t learn enough.
For example, suppose that you want a robot to recognize the visual image of any human hand. This is hard because we never see the same image twice—even of the very same hand—because each finger may change its position and shape, we’ll see it from different points of view, and each part will catch different amounts of light. This means that we’ll need trillions of If–>Do rules, unless we can find some special tricks that single out just the most relevant features—or if, as we’ll see in §6-2, we can
