Sequential Thought Processes in PDP Models

ion for this example. We have taken such features as has television as a microfeature. In a more realistic example, we would expect television to itself be a particular pattern over a set of units that are used to represent many different varieties of television. There might be many variations on the television pattern corresponding to variations among televisions. Moreover, since televisions in bedrooms may be systematically different (perhaps smaller) than televisions in living rooms , we would expect that these correlations would be picked up and there would be a context dependency between the particular version of television and the remaining objects in the room. In such a case the units that participate in the representation of television would play the role of a slot in a schema, and the particular pattern of activation on these units would represent the characteristics of the slot filler. Figure 6 shows several examples of the processing of this network. These runs started by " clamping " one of the descriptors on (that is , by setting the value to 1 and not letting it change) and then letting the system find a goodness-of-fit maximum. In the first example , the descriptor oven was clamped on. In such a case , we expect that the system will bring those units most tightly bound to the oven unit on and turn off those units negatively correlated to oven or other units that it turns on. On the assumption that oven is a central feature of the kitchen schema the pattern the system eventually turns on is just that which might be said to correspond to the default kitchen schema. The strengths of each of the 40 units is shown along with the " goodness-of-fit" of the state after every 20 updates. The system begins with oven and ceiling on and then adds coffee-cup (weakly), then sink and refrigerator concludes that the room is small adds toaster and coffeepot and finally ends up at a maximum with ceiling, walls, window, small, telephone, clock, coffee-cup, drapes, stove, sink, refrigerator, toaster, cupboard, coffeepot and oven. other words, it finds the default or prototype kitchen. Similarly, runs of the system starting with desk, bathtub, sofa or bed clamped lead to goodness maxima corresponding to the prototype or default office PSYCHOLOGICAL PROCESSES bathroom , living room , or bedroom , respectively. It is, as previously noted , these maxima that we believe correspond roughly to instantiations of schemata for kitchens , offices , bathrooms , living rooms, and bedrooms. The system receives input in the form of having some of the descriptors clamped from the outside. It then finds the best interpretation of the input through this process of hill climbing. As it climbs, the system " fills in " the relevant descriptors of the scene in question. In the case of the network we created for this example, there are essentially five maxima-one corresponding to each of the different room types. There are 240 possible binary states in which the system could potentially settle, but, in fact , when it is started by clamping exactly one descriptor, it will only settle into one of five states. This roughly corresponds to the view that this data base contains five schemata defined over the set of 40 descriptors. There are , as we shall see numerous subschemata which involve subpatterns within the whole pattern. 00000000000000000000 g6~puter I f~~Yhange bathtub television dresser coffee-pot cupboard toaster r~frigerator: sink stove 00 drapes . .. fire-place ash-tray . a a 00000000000000000 coffee-cup , easy-chair sofa floor-lamp picture . 00000000000000 clock .. ...... ... Idesk-chair. I books , carpet ! bookshelf . . . i typewriter .. ... .....