Steven Pinker -- How the Mind Works - Hampshire High Italian ...

108 J HOW THE MIND WORKSyoung, and so on. Any fact stored in the connections for one animal (parrotsare warm-blooded) automatically transfers to similar animals (budgiesare warm-blooded), because the network does not care that theconnections belong to an animal at all. The connections merely saywhich visible properties predict which invisible properties, skippingideas about species of animals altogether.Conceptually speaking, a pattern associator captures the idea that iftwo objects are similar in some ways, they are probably similar in otherways. Mechanically speaking, similar objects are represented by some ofthe very same units, so any piece of information connected to the unitsfor one object will ipso facto be connected to many of the units for theother. Moreover, classes of different degrees of inclusiveness are superimposedin the same network, because any subset of the units implicitlydefines a class. The fewer the units, the larger the class. Say there areinput units for "moves," "breathes," "hairy," "barks," "bites," and "lifts-legat-hydrants."The connections emanating out of all six trigger facts aboutdogs. The connections emanating out of the first three trigger facts aboutmammals. The connections emanating out of the first two trigger factsabout animals. With suitable weights, the knowledge programmed in forone animal can be shared with both its immediate and its distant familymembers.A fifth trick of neural networks is that they learn from examples,where learning consists of changes in the connection weights. Themodel-builder (or evolution) does not have to hand-set the thousands ofweights needed to get the outputs right. Suppose a "teacher" feeds a patternassociator with an input and also with the correct output. A learningmechanism compares the network's actual output—which at first will bepretty random—with the correct one, and adjusts the weights to minimizethe difference between the two. If the network leaves an outputnode off that the teacher says ought to be on, we want to make it morelikely that the current funnel of active inputs will turn it on in the future.So the weights on the active inputs to the recalcitrant output unit areincreased slightly. In addition, the output node's own threshold is loweredslightly, to make it more trigger-happy across the board. If the networkturns an output node on and the teacher says it should be off, theopposite happens: the weights of the currently active input lines aretaken down a notch (possibly driving the weight past zero to a negativevalue), and the target node's threshold is raised. This all makes thehyperactive output node more likely to turn off in response to those