Rosenberg argues that intentional generalizations in the human sciences cannot be law-like because they are not amenable to significant empirical refinement. This irrefinability is said to result from the principle that supposedly controls in intentional explanation also serving as the standard for successful interpretation. The only credible evidence bearing on such a principle would then need conform to it. I argue that psychological generalizations are refinable and can be nomic. I show how empirical refinement of psychological generalizations is possible by (...) considering concrete cases. A sufficiently detailed view of the role of psychological generalizations in interpretation allows us to find in psychological investigations instances of bootstrap testing. (shrink)

The dictum ‘“ought” implies “can”’ has a status in moral philosophy in some respects like that of ‘a good player needs good co-ordination’ in talk about ball-games. Clearly, you say something important but not conclusive about proficiency in playing a ball-game when you say that it requires good co-ordination: similarly, you say something important but not conclusive about obligation when you say that it implies a certain possibility or power or ability. Each dictum is a reminder: the one about such (...) courses of physical instruction, the other about such exhortations to duty, as are worth persevering with. It would be hopeless to keep on teaching a boy the moves and tricks of rugby football if he could never co-ordinate well enough to get his eye in, so to speak. Correspondingly, it would be meaningless to recommend that someone ought to do something the specification of which involved a contradiction, and pointless to suggest that he ought to do something which, for quite general reasons, was not, and was certain to remain not, within his power. So each dictum expresses a bluff, no-nonsense wisdom which we should count on before involving ourselves in certain more detailed commitments. But probably this is as far as the comparison between the two sayings can well be taken. (shrink)

The application of machine learning algorithms to healthcare data can enhance patient care while also reducing healthcare worker cognitive load. These algorithms can be used to detect anomalous physiological readings, potentially leading to expedited emergency response or new knowledge about the development of a health condition. However, while there has been much research conducted in assessing the performance of anomaly detection algorithms on well-known public datasets, there is less conceptual comparison across unsupervised and supervised performance on physiological data. Moreover, while (...) heart rate data are both ubiquitous and noninvasive, there has been little research specifically for anomaly detection of this type of data. Considering that heart rate data are indicative of both potential health complications and an individual’s physical activity, this is a rich source of largely overlooked data. To this end, we employed and evaluated five machine learning algorithms, two of which are unsupervised and the remaining three supervised, in their ability to detect anomalies in heart rate data. These algorithms were then evaluated on real heart rate data. Findings supported the effectiveness of local outlier factor and random forests algorithms in the task of heart rate anomaly detection, as each model generalized well from their training on simulated heart rate data to real world heart rate data. Furthermore, results support that simulated data can help configure algorithms to a degree of performance when real labeled data are not available and that this type of learning might be especially helpful in initial deployment of a system without prior data. (shrink)