It is argued that a link prevails between the phenomenology of externality present in classical liberal theory and the state of mind known as schizophrenia. To escape the social reality of possessive individualism, especially the conception of consequences, ends, habits, routine, the schizophrenic individual 'withdraws' or regresses into a psychic universe that contains a dimension unrelated to the consciousness and values of externality: the pursuit of wealth and things, the calculated regard of the other as an instrument for enriching the (...) self. The schizophrenic is incapable of adapting his 'ego' to the necessities of the social environment; he cannot defend himself in 'conventional' or 'normal' ways from the demands of living in a social milieu where the expectations and judgments of others impose intolerable pressures on consciousness. Instead the individual undergoing the painful process of withdrawal constructs a set of psychic defenses that from the standpoint of the external world appear to be 'strange', 'odd', 'bizarre', or 'demented'. The 'mode of Being' of the schizophrenic has nothing in common with the life-style of the acquisitive, status-conscious society. Some consideration is given to the political implications and meaning of the schizophrenic's withdrawal. (shrink)

Alzheimer's Disease is a form of dementia that affects the memory, cognition, and motor skills of patients. Extensive research has been done to develop accessible, cost-effective, and non-invasive techniques for the automatic detection of AD. Previous research has shown that speech can be used to distinguish between healthy patients and afflicted patients. In this paper, the ADReSS dataset, a dataset balanced by gender and age, was used to automatically classify AD from spontaneous speech. The performance of five classifiers, as well (...) as a convolutional neural network and long short-term memory network, was compared when trained on audio features and text features. The same audio and text features were used to train five regression models to predict the Mini-Mental State Examination score for each patient, a score that has a maximum value of 30. The top-performing classification models were the support vector machine and random forest classifiers trained on BERT embeddings, which both achieved an accuracy of 85.4% on the test set. The best-performing regression model was the gradient boosting regression model trained on BERT embeddings and CLAN features, which had a root mean squared error of 4.56 on the test set. The performance on both tasks illustrates the feasibility of using speech to classify AD and predict neuropsychological scores. (shrink)