In the article the mutual influence of folk theatre, Austrian Singspiel and Viennese opera in the genres of comic opera, operetta and drama performances involving music, singing and dancing is studied. The powerful influence of Italian and French opera schools, as well as the Italian Commedia Dell'arte led to the flourishing of music and theatre art in Austria: opera buffa (A. Salieri, Ch. W. Glück, J. Haydn, W. A. Mozart), fairy-tale comedies of F. Raimund and satirical dramas of Nestroy. Their (...) game-like nature finds its expression in musical turns, satirical songs and duets. Viennese dance operetta has absorbed the traditions of Viennese popular comedy and comic opera. Recitative, vocal music and folk dances were the key elements of operettas (J. Strauss junior, F. von Suppe, K. Millöcker, K. Zeller, F. Lehar). The music and the game in Austria are the results of theatralization of life: the life turns into something like art. Austrian culture, which is addressed directly to the human being and short of complex philosophical musings and speculative schemes, has genetically built-in playful, musical and theatrical basis, as well as German. Strongly-pronounced playful and musical source as an important national component of the culture, its code, determine its highly fictitious character (staginess) and integrity (both genre and intermediately), thereby maintain its special status in the European culture. (shrink)

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting (...) brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN–human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in (...) the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN–human correspondences in vision and other domains of cognition. We explore all these issues in this response. (shrink)

The Anatoly Bakushinsky’s Seminarium (1917–1926) at the Tsvetkov gallery in Moscow became one of the first experimental and most influential venues to develop approaches to the perception of art in Soviet Russia. In it, Bakushinsky, an art critic and the head of the Physical-Psychological Department of the State Academy of Artistic Sciences (GAKhN), incorporated the practice of formal art history into a methodology based on materialism, psychology, and experimental aesthetics widely practiced at the GAKhN. Today, this combination of approaches (...) is perceived as an isolated historical example of an outmoded eclecticism. I argue, however, that Bakushinsky’s methodology should be studied in the context of the complex institutional structure of the Academy that was designed to ensure multidisciplinary work in art studies and its almost immediate implementation into art practice. This institutional and material nexus of the GAKhN’s history opens up a new direction in studying the intellectual legacy of Soviet humanities of the 1920s, which is otherwise often reduced to biographical narratives. (shrink)