Spring School on Language, Music, and Cognition: Organizing Events in Time

The interdisciplinary spring school “Language, music, and cognition: Organizing events in time” was held from February 26 to March 2, 2018 at the Institute of Musicology of the University of Cologne. Language, speech, and music as events in time were explored from different perspectives including evolutionary biology, social cognition, developmental psychology, cognitive neuroscience of speech, language, and communication, as well as computational and biological approaches to language and music. There were 10 lectures, 4 workshops, and 1 student poster session. Overall, the spring school investigated language and music as neurocognitive systems and focused on a mechanistic approach exploring the neural substrates underlying musical, linguistic, social, and emotional processes and behaviors. In particular, researchers approached questions concerning cognitive processes, computational procedures, and neural mechanisms underlying the temporal organization of language and music, mainly from two perspectives: one was concerned with syntax or structural representations of language and music as neurocognitive systems (i.e., an intrapersonal perspective), while the other emphasized social interaction and emotions in their communicative function (i.e., an interpersonal perspective). The spring school not only acted as a platform for knowledge transfer and exchange but also generated a number of important research questions as challenges for future investigations

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Implicit learning of recursive context-free grammars

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning

Embodied Musical Interaction

Music is a natural partner to human-computer interaction, offering tasks and use cases for novel forms of interaction. The richness of the relationship between a performer and their instrument in expressive musical performance can provide valuable insight to human-computer interaction (HCI) researchers interested in applying these forms of deep interaction to other fields. Despite the longstanding connection between music and HCI, it is not an automatic one, and its history arguably points to as many differences as it does overlaps. Music research and HCI research both encompass broad issues, and utilize a wide range of methods. In this chapter I discuss how the concept of embodied interaction can be one way to think about music interaction. I propose how the three “paradigms” of HCI and three design accounts from the interaction design literature can serve as a lens through which to consider types of music HCI. I use this conceptual framework to discuss three different musical projects—Haptic Wave, Form Follows Sound, and BioMuse

Can Computers Create Art?

This essay discusses whether computers, using Artificial Intelligence (AI), could create art. First, the history of technologies that automated aspects of art is surveyed, including photography and animation. In each case, there were initial fears and denial of the technology, followed by a blossoming of new creative and professional opportunities for artists. The current hype and reality of Artificial Intelligence (AI) tools for art making is then discussed, together with predictions about how AI tools will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork. It is theorized that art is something created by social agents, and so computers cannot be credited with authorship of art in our current understanding. A few ways that this could change are also hypothesized.Comment: to appear in Arts, special issue on Machine as Artist (21st Century

The biology and evolution of music: A comparative perspective

Studies of the biology of music (as of language) are highly interdisciplinary and demand the integration of diverse strands of evidence. In this paper, I present a comparative perspective on the biology and evolution of music, stressing the value of comparisons both with human language, and with those animal communication systems traditionally termed &quot;song&quot;. A comparison of the &quot;design features&quot; of music with those of language reveals substantial overlap, along with some important differences. Most of these differences appear to stem from semantic, rather than structural, factors, suggesting a shared formal core of music and language. I next review various animal communication systems that appear related to human music, either by analogy (bird and whale &quot;song&quot;) or potential homology (great ape bimanual drumming). A crucial comparative distinction is between learned, complex signals (like language, music and birdsong) and unlearned signals (like laughter, ape calls, or bird calls). While human vocalizations clearly build upon an acoustic and emotional foundation shared with other primates and mammals, vocal learning has evolved independently in our species since our divergence with chimpanzees. The convergent evolution of vocal learning in other species offers a powerful window into psychological and neural constraints influencing the evolution of complex signaling systems (including both song and speech), while ape drumming presents a fascinating potential homology with human instrumental music. I next discuss the archeological data relevant to music evolution, concluding on the basis of prehistoric bone flutes that instrumental music is at least 40,000 years old, and perhaps much older. I end with a brief review of adaptive functions proposed for music, concluding that no one selective force (e.g., sexual selection) is adequate to explaining all aspects of human music. I suggest that questions about the past function of music are unlikely to be answered definitively and are thus a poor choice as a research focus for biomusicology. In contrast, a comparative approach to music promises rich dividends for our future understanding of the biology and evolution of music. (c) 2005 Elsevier B.V. All rights reserved.</p