“…N. Shepard, Hovland, & Jenkins, 1961): The learning proceeds more rapidly when the consequential set of objects forms a region in the representational space that is connected rather than disconnected (R. N. Shepard & Kannappan, 1991). The learning also proceeds more rapidly when the consequential set is compact in terms of the Euclidean metric if the dimensions are integral, but more rapidly when the consequential set is based on shared features (or conjunctions of features) if the dimensions are separable (R. N. Shepard & Tenenbaum, 1991). (For related simulations, see Nosofsky, Gluck, Palmeri, McKinley, & Glauthier, in press;Nosofsky, Kruschke, & MeKinley, 1992; and for a similar Bayesian approach in which, however, the underlying hypotheses are taken to be Gaussian distributions rather than the sharply bounded regions posited here, see Anderson, 1991.…”