Humean Supervenience?

Philosophical Studies -     

The Metaphysics of Causal Models

This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases of late pre?mption and other cases that have proved problematic for causal models.

Effects of similarity and repetition on memory: registration without learning?

We investigated judgments of the frequency of test items (Y) that were highly similar to studied items (X) to test a prediction made by several memory models: that the judged frequency of Y should be proportional to the judged frequency of X. Whether stimuli were pictures or words, judged frequency of Y was bimodally distributed with 1 mode at zero, suggesting that frequency judgments involve a 2-stage process in which a zero judgment is made if there is a mismatch between retrieved information and the test item. Nonzero judgements, taken by themselves, were consistent with the prediction of proportionality. In 2 experiments, the percentage of zero judgments made to Y increased with repetition of X, but in 2 others the percentage did not change beyond frequency = 1. The percentage of "new" judgments in recognition memory followed this same pattern. Because the judged frequency of X increased even as X-Y discrimination showed no improvement, we characterize the result as "registration without learning."