The Laws of Thought summary

The first half of the twentieth century saw the emergence of psychology, a new and unusual science in which the subject matter could be neither seen nor touched

Early psychologists worked around this problem by observing people’s behavior, and then inferring backward to arrive at the thoughts and feelings behind that behavior. One early experiment by Wilhelm Wundt involved measuring the time between a participant hearing a sound and pressing a button, on which basis he made inferences about the nature of the mind. 

In 1833, English mathematician and inventor Charles Babbage started working on his Analytical Engine, a machine that could carry out any arithmetic calculation using instructions provided by punch cards. This would have been the earliest form of what we now call a computer. But Babbage died before he could complete it

Some hundred years later, Alan Turing, a fellow at King’s College Cambridge, picked up the baton, with his conception called the Turing machine. The machine’s design involved a moving head and an infinitely long piece of tape. The head could read or write either a 0 or a 1 on the tape, and could move through a number of finite states.

The 1950s saw the explosion of cognitive science. A string of key papers propelled the discipline forward, as researchers grappled with the task of explaining the mind scientifically. One approach that emerged was that of rules and symbols – formal systems based on logic. 

American scholars Allen Newell and Herbert Simon introduced the physical symbol hypothesis, whereby a collection of symbol structures could be produced that describes objects and their behaviors in the physical world. 

One of the hardest blows to the rules and symbols approach to describing the mind came from the highlands of Western New Guinea. In 1968 California native Eleanor Rosch travelled there to study the local Dani people. 

The Dani only had two words for colours: “mola” and “mili”. She enrolled some of the community’s members in a color memory experiment that had previously been conducted by Roger Brown on Harvard undergraduates.

The perceptron. Sounds like science fiction, right? Well at the time, it was. This was a machine designed by researcher Frank Rosenblatt – an artificial visual system that could classify a range of images based on the visual input received by an artificial retina – much like what our brains do with the light patterns received on our own retinas.

The perceptron’s design involved three layers. First there were sensory units that would activate in response to light patterns. These would be connected to association units, which would sum up the signals received by the sensory units and activate if they passed some predetermined threshold. Finally, the association units would be connected to response units, which would do the same thing, summing and thresholding, and ultimately give a classification of the image presented to the artificial retina. The perceptron could recognize a shape as a triangle, for example, or state if a shape appeared on the left part of an image.

We all use probability in our daily lives. We gauge how likely it is for it to rain tomorrow, or what the chances are of getting a job promotion. As more information reaches us, like a menacing cloud or an encouraging email, we adjust our probabilities accordingly. 

How do we capture this mathematically? This problem was solved in the eighteenth century, by the Reverend Thomas Bayes and the Marquis Pierre-Simon Laplace. Both men solved the problem independently, but we now refer to this kind of reasoning as Bayesian inference.