Sanja Sreckovic

The paper deals with the formalistic view on music presented in Eduard Hanslick’s treatise On the Musically Beautiful, which is taken to be the foundingwork of the aesthtetics of music. In the paper I propose an interpretation of Hanslick’s treatise which differs on many points from the interpretations displayed in the works of several most influential contemporary aestheticians of music. My main thesis is that Hanslick’s treatise is misunderstood and incorrectly presented by these authors. I try to demonstrate this thesis by referring to Hanslick’s original formulations in the German edition and by showing that my interpretation renders Hanslick’s view far more coherent and his arguments successful in showing his main conclusions. Accepting this alternative interpretation should have further implications on many contemporary theories in the aesthetics of music that reckon on the failure of Hanslick’s arguments as presented by usual interpretations.

If a conclusion was reached that creatures without a language capability exhibit some form of a capability for logic, this would shed a new light on the relationship between logic, language, and thought. Recent experimental attempts to test whether some animals, as well as pre-linguistic human infants, are capable of exclusionary reasoning are taken to support exactly that conclusion. The paper discusses the analyses and conclusions of two such studies: Call’s (2004) two cups task, and Mody and Carey’s (2016) four cups task. My paper exposes hidden assumptions within these analyses, which enable the authors to settle on the explanation which assigns logical capabilities to the participants of the studies, as opposed to the explanations which do not. The paper then demonstrates that the competing explanations of the experimental results are theoretically underdeveloped, rendering them unclear in their predictions concerning the behavior of cognitive subjects, and thus difficult to distinguish by use of experiments. Additionally, it is questioned whether the explanations are rivals at all, i.e. whether they compete to explain the cognitive processes of the same level. The contribution of the paper is conceptual. Its aim is to clear up the concepts involved in these analyses, in order to avoid oversimplified or premature conclusions about the cognitive abilities of pre- and non-linguistic creatures. It is also meant to show that the theoretical space surrounding the issues involved might be much more diverse and unknown than many of these studies imply.

The dissertation discusses the relationship between two approaches to researching music: the empirical approach of experimental psychology and cognitive neuroscience, and the speculative approach of philosophical aesthetics of music. The aim of the dissertation is to determine the relationship between problems, conceptual frameworks, and domains of inquiry of the two approaches. The dissertation should answer whether the philosophical and the empirical approach deal with the same, or at least relatable aspects of music. In particular, it should answer whether the results of the empirical research contribute to the debates of philosophical aesthetics of music. Each of the three chapters of the dissertation deals with one particular problem in philosophy of music: meaning in music, value of music, and the relationship between music and the emotions. For each particular philosophical problem discussed in these chapters, it is demonstrated that the experimental results provide interesting insights for philosophy of music. It is concluded in the dissertation that philosophers can benefit from examining experimental studies, not only regarding the particular aesthetic theories, but also in regard to the methodological consideration, since the dissertation shows that this kind of interdisciplinary approach uncovers methods useful to philosophers, not commonly available to the armchair philosophical approach.

The article presents Hanslick’s aesthetic formalism as the starting point of the contemporary aesthetics of music. His book, written in the 19th century, is considered contemporary because it still proves to be influential and fruitful in the contemporary theoretical circles, especially in the modern analytic aesthetics of music, where it is widely cited and discussed. The article positions Hanslick’s book in relation to his nearest predecessors Kant and Herbart, and to the neighbouring area where the formalistic view appeared, namely in Fidler’s visual formalism in fine arts. It is shown that by being the first piece of writing to attempt to systematically and comprehensively determine the essence and the content of music, and the criteria for evaluation of musical worth, Hanslick’s book sets the foundation for a rigorous examination of the aesthetics of music, and also breaks off with the widely ingrained preconceptions of music, which were in themselves an obstacle to establishing the aesthetics of music as a scientific discipline.

The question of meaning in music has been discussed by numerous philosophers of music. On one end of the philosophical spectrum, the meaning in music is understood as “specifically musical” meaning, i.e. the meaning exhausted by the musical ideas. The other end of the spectrum is occupied by the view that the meaning in music is emotional, consisting of the ex-pression or representation of emotions by music, i.e. that the meaning in music is emotional meaning. The paper will demonstrate that the results of psychological research support a more complex view, which ac-knowledges different types of meaning. The main aim of the paper is to examine the contribution of experimental psychological research for the philosophical debates on musical meaning. Certain studies of behavioral and neurological reactions to musical stimuli have revealed interesting relationships between the processing of syntax, semantics, and emotion in mu-sic. After presenting three main philosophical views on musical meaning, the paper discusses the results and implications of three experimental psychological studies, and their relevance for the philosophical views. It is argued in the paper that the experimental results enrich the classical philosophical debate on meaning in music. The original contribution of the paper con-sists in suggesting a way to connect the philosophical debates on musical meaning with the experimental psychological research. The implications of the paper will reveal higher complexity of the issue of musical meaning than is implicitly supposed in the theoretical debates. The second important implication is the suggestion of a way in which philosophical questions concerning music can be approached with the help of experimental psychology in future research.

The paper deals with the approaches to researching music from the scientific perspective. It is argued that the scientific literature concerning music contains two different methodological approaches which significantly determine the range of possible conclusions to be reached by the research. The approach „from the outside“ investigates music by automatically applying to music the more general conclusions concerning human cognition and other capacities and behaviors. Thus, this approach omits music’s internal factors. In contrast, the approach „from within“ consists in empirically investigating reactions to music, capacities necessary for its understanding, performing, etc., and their relationships with the reactions and capacities outside the domain of music. The paper shows that, even though the scientific research of music is still in the beginning stage, the approach from within enables more complete explanations concerning music, due to taking into account a larger number of factors relevant for philosophical understanding of the phenomenon of music.

Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate what it is specifically that makes ML methodology substantially unsuitable for reaching explanations. The analysis is given broader philosophical context by connecting it with the views on the role of prediction and explanation in science, their relationship, and the value of explanation. We proceed to show, first, that ML disrupts the relationship between prediction and explanation commonly understood as a functional relationship. Then we show that the value of explanation is not exhausted in purely predictive functions, but rather has a ubiquitously recognized value for both science and everyday life. We then invoke two hypothetical scenarios with different degrees of automatization of science, which help test our intuitions on the role of explanation in science. The main question we address is whether ML will reorient or otherwise impact our standard explanatory practice. We conclude with a prognosis that ML would diversify science into purely predictively oriented research based on ML-like techniques and, on the other hand, remaining faithful to anthropocentric research focused on the search for explanation.

Because of its practical advantages, machine learning (ML) is increasingly used for decision-making in numerous sectors. This paper demonstrates that the integral characteristics of ML, such as semi-autonomy, complexity, and non-deterministic modeling have important ethical implications. In particular, these characteristics lead to a lack of insight and lack of comprehensibility, and ultimately to the loss of human control over decision-making. Errors, which are bound to occur in any decision-making process, may lead to great harm and human rights violations. It is important to have a principled way of assigning responsibility for such errors. The integral characteristics of ML, however, pose serious difficulties in defining responsibility and regulating ML decision-making. First, we elaborate on these characteristics and their epistemic and ethical implications. We then analyze possible general strategies for resolving the assignment of moral responsibility and show that, due to the specific way in which ML functions, each potential solution is problematic, whether we assign responsibility to humans, machines, or using hybrid models. Then, we shift focus on an alternative approach that bypasses moral responsibility and attempts to define legal liability independently through solutions such as informed consent and the no-fault compensation system. Both of these solutions prove unsatisfactory because they leave too much room for potential abuses of ML decision-making. We conclude that both ethical and legal solutions are fraught with serious difficulties. These difficulties prompt us to re-weigh the costs and benefits of using ML for high-stake decisions.