Selectivity, Scope, and Simplicity of Models: A lesson from fitting judgments of perceived depth

When comparing psychological models a researcher should assess their relative selectivity, scope, and simplicity. The third of these considerations can be measured by the models ' parameter counts or equation length, the second by their ability to fit random data, and the first by their differential ability to fit patterned data over random data. These conclusions are based on exploration of integration models reflecting depth judgments. Replication of Massaro's (1988a) results revealed an additive model (Bruno & Cutting, 1988), and Massaro's fuzzy-logical model of perception (FLMP) fit data equally well, but further exploration showed that the FLMP fit random data better. The FLMP's successes may reflect not its sensitivity in capturing psycholog-ical process but its scope in fitting any data and its complexity as measured by equation length. Good scientific theories are usually thought to have several properties: They are accurate, simple, broad in scope, inter-nally consistent, and have the ability to generate new research (Kuhn, 1977). When models can be used to instantiate theo-ries, they might reflect these same properties. For our purposes the two key concepts in this set are simplicity and scope