Grounding: Necessary or Contingent?

Recent interest in the nature of grounding is due in part to the idea that purely modal notions are too coarse‐grained to capture what we have in mind when we say that one thing is grounded in another. Grounding not being purely modal in character, however, is compatible with it having modal consequences. Is grounding a necessary relation? In this article I argue that the answer is ‘yes’ in the sense that propositions corresponding to full grounds modally entail propositions corresponding to what they ground. The argument proceeds upon two substantive principles: the first is that there is a broadly epistemic constraint on grounding, while the second links this constraint with Fine's Aristotelian notion of essence. Many think grounding is necessary in something like the sense specified above, but just why it's necessary is an issue that hasn't been carefully addressed. If my argument is successful, we now know why grounding is necessary.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.