Significant category clustering in free recall

Frender and Doubilet suggest that Bousfield's ratio of repetitions (RR) is the best measure of clustering in free recall presently available. Conditioning only on the number of words recalled, they determine the mean ofRR in the absence of clustering. In this note the null variance ofRR is presented. This permits development of conservative significance tests based on the Cramér inequality.     

The Destiny of Creation: Theological Ethical Reflections on Laudato Si'

In this essay, I ask what the precise relation is between Laudato si's theology and its claims about our individual and corporate responsibility for the environment and the plight of the poor. To do so, I first clarify the relationship between the theological claims and its account of moral norms, situating the text within the history of western ethical theory. I then turn to reconstruct the submerged theology of the encyclical, focusing on Pope Francis's accounts of the techno‐economic paradigm and the possibility of an “integral ecology” paradigm. I end by assessing the text in terms of the coherence and plausibility of its argument as an ethical and theological statement.     

Constraints and Preferences in Inductive Learning: An Experimental Study of Human and Machine Performance

The paper examines constraints and preferences employed by people in learning decision rules from preclassified examples. Results from four experiments with human subjects were analyzed and compared with artificial intelligence (AI) inductive learning programs. The results showed the people's rule inductions tended to emphasize category validity (probability of some property, given a category) more than cue validity (probability that an entity is a member of a category given that it has some property) to a greater extent than did the AI programs. Although the relative proportions of different rule types (e.g., conjunctive vs. disjunctive) changed across experiments, a single process model provided a good account of the data from each study. These observations are used to argue for describing constraints in terms of processes embodied in models rather than in terms of products or outputs. Thus AI induction programs become candidate psychological process models and results from inductive learning experiments can suggest new algorithms. More generally, the results show that human inductive generalizations tend toward greater specificity than would be expected if conceptual simplicity were the key constraint on inductions. This bias toward specificity may be due to the fact that this criterion both maximizes inferences that may be drawn from category membership and protects rule induction systems from developing over-generalizations.