In the last chapter we considered the evidence for a female superiority in empathizing. In the next chapter we encounter the evidence for a male superiority in systemizing. But first let’s take a short pause on our journey to examine what systemizing is.
Systemizing is the drive to understand a system and to build one. By a system I do not just mean a machine (like a tool, or a musical instrument, or the insides of your watch). Nor do I even just mean things that you can build (like a house, a town, or a legal code). I mean by a system anything which is governed by rules specifying input-operation-output relationships. This definition takes in systems beyond machines, such as math, physics, chemistry, astronomy, logic, music, military strategy, the climate, sailing, horticulture, and computer programming. It also includes systems like libraries, economics, companies, taxonomies, board games, or sports. The system might be tiny (like an individual cell), or larger (like a whole animal), or larger still (like a social group or a political system).
Systemizing involves first the analysis of the features in a system that can vary, followed by close, detailed observation of the effects that occur when each feature is varied (“systematically”). Repeating such observations leads one to discover the input-operation-output rules governing the behavior of the system.
Here’s a simple example: “If I push the red button, the projector advances to the next slide.” Here, the red button is the input, pushing it is the operation, and the next slide popping up is the output.
Sometimes the operation is not performed by an animate agent (you or anyone else) but is an impersonal event. Here’s a simple example: “At 10 A.M., the sun casts a shadow on my bedroom wall at this particular point.” Here, the sun is the input, and its position is the operation. The shadow from the sun’s previous position is one output, and the shadow from the sun’s present position is a new output.
Systemizing therefore needs an exact eye for detail, since it makes a world of difference if you confuse one input or operation for another. If the operation is a mouse-click on a computer screen, or if the input is a digit in a mathematical formula, one tiny change at this stage can lead to a completely different output—it can lead the system to behave completely differently. The pay-off of good systemizing is not only being able to understand the system but also being able to predict what it will do next.
The key thing about systemizing is that the system your brain is trying to understand is finite, deterministic, and lawful. Once you have identified the rules and regularities of the system, then you can predict its workings absolutely. This holds true even for more complex systems, where there are many more parameters, or where the rules are much more elaborate. But the rules are in principle specifiable.
You might feel, as a reader, that I am using such a broad notion of “system” that it includes almost everything. This is a reasonable worry. In fact, systemizing (and empathizing) are processes in the mind, and as such they can indeed be applied to almost any aspect of the environment. In practice, empathizing is most easily applied to agents (i.e., entities that are capable of self-propulsion, even virtual ones, such as cartoon characters),1 while systemizing is most easily applied to lawful aspects of the environment. And there are many lawful aspects of the environment to discover, using this process.
We can draft a classification of the six major kinds of system that exist, which the brain can analyze and/or build. (Here I am, systemizing systems.)
We often think of systems in the world of technology as “man-made.” (My guess is that most of these were indeed invented by men, and as this book will go on to explore, this may be no coincidence.) Technical systems may be complex, such as computers, vehicles, tools, and other machines. They also include the complex systems of the kind that academics would study in branches of physics, electronic and mechanical engineering, computer science, and material science. But a technical system can be as basic as a roof, a sail, a plane wing, or a compass. For example:
These include the complex systems in nature of the kind that academics study in ecology, geography, chemistry, physics, astronomy, medicine, meteorology, biology, or geology. But systemizing nature is not just carried out by academics. We all systemize nature. Just think how we analyze an animal or a plant, an ecosystem, or the climate. And again, these systems can include quite ordinary things like soil, rivers, rocks, an insect, or a leaf. For example:
Complex examples of abstract systems include things such as math, logic, grammar, music, computer programs, taxation, mortgages, pensions, stocks and shares, or maps. Some abstract systems are really quite ordinary, such as the rules for reading text, or the account book of a business, or a train timetable. For example:
These are groups of people or, more precisely, the rules describing these groups. Complex social systems include those studied by academics in politics, business, law, theology, the military, economics, history, and social science. Simpler social systems include a committee, a political group, a group of friends, an institution, or charts such as a soccer league table, a pop-music chart, or a list of players in the sports team. For example:
Some of these systems are vast, such as encyclopedias, museums, or second-hand-record shops and book shops; some of them are more limited, such as sets of coins or stamps in an album. But they all need to be organized according to some criteria or taxonomy, and there can be many different ways to cut the cake, as it were. This is because members of a category can be grouped in different ways. For example:
Again, some of these systems are complex, such as the finger movements required to play a Beethoven sonata on the piano. Others are simpler, such as the ability to throw a dart at the bullseye, or the golf swing. The golf swing lasts just two seconds, but what goes on during those two seconds (the operation) can make the difference between the ball (the input) ending up in the hole (output 1) or in the lake (output 2). For example:
So we have (at least) six different kinds of systems. You can see that, despite their surface differences, there are some deep, underlying similarities. In each case, the systemizer explores how a particular input produces a particular output following a particular operation. This provides us with more or less useful if-then rules. You use a narrower canoe, it goes faster. You prune your roses in March, they grow stronger next season. You fly above a cloud, you experience less turbulence. You swing the golf club higher, the ball travels along a steeper trajectory. You focus on the jaws of the crocodiles, the reptile classification changes. You divide some numbers by others, they leave no remainder. The outcome is noted and stored as a possible underlying rule or regularity governing the system. The rules are nothing more than input-operation-output relations.
Behaviorist psychologists of the early twentieth century called this kind of learning “association” learning, which is a partial description of systemizing. Typically in association learning (in other words, classical or operant conditioning) we extract the rule because there is sufficient reward or punishment. For example, a child learns that touching a hot radiator leads to pain, or a motorist discovers that a particular parking meter takes his money and credits him with twice the expected amount of time. In these examples the motivation for learning is an external reward (x) or punishment (y).
Systemizing is different from classical or operant conditioning, in that the motivation is not external but intrinsic—to understand the system itself. The buzz is not derived from some tangible reward (such as a food pellet when you press a lever, or a salary when you do a job). Rather, the buzz is in discovering the causes of things, not because you want to collect causal information for the sake of it, but because discovering causes gives you control over the world.
And a second big difference between association learning and systemizing is that the former is within the capability of most organisms with a nervous system, from a worm to an American president, whereas the latter may be a uniquely human or higher primate capability. This needs to be investigated in a range of species, but one conclusion is that causal cognition is rarely, if ever, seen outside of humans.1
Philosophers worry about whether such correlation-based observations could ever distinguish between “common cause” (where two things appear to be causally related, but in reality they are both caused by a third, common factor) and “causation” proper. My guess is that this is a nicety that in practice the brain ignores, because even mistaking a common cause for causation gives you valuable leverage over events in the world. It allows you to begin designing systems or intervening in nature, to get control in the world.
So the big pay-off of systemizing is control. If you want to harness energy with a water wheel or a windmill, you had better understand how water or wind pressure causes your technical system to move. If you can figure out what controls what, you can build any machine to do anything for you: a spear that flies straight, or a rocket that can get to the moon. The principles—systemizing—are the same, but the list of if-then rules gets longer as the system becomes more complex.
Systemizing is an inductive process. You watch what happens each time you click that mouse, and after a series of reliably predictable results, you form your rule. Systemizing is also an empirical process. You need a keen eye and an orderly mind. An exact mind. Without them, essential variables or parameters, and the pattern of their effects, will be missed, or the rules will not have been carefully checked and tested. If one exception occurs which violates the rule, the systemizer notes it, rechecks the rule, and refines or revises it. If he or she has identified the rule governing the system correctly, the system works. The test is repeatability. Of course, this only works with events which repeat or are repeatable, and where the output can change.2
In the next chapter we look at the evidence relevant to the claim that there is a male superiority in systemizing.