The field of computational creativity, including musical metacreation, strives to develop artificial systems that are capable of demonstrating creative behavior or producing creative artefacts. But the claim of creativity is often assessed, subjectively only on the part of the researcher, and not objectively at all. This paper provides theoretical motivation for more systematic evaluation of musical metacreation and computationally creative systems, and presents an overview of current methods used to assess human and machine creativity that may be adapted for this purpose. In order to highlight the need for a varied set of evaluation tools, a distinction is drawn between three types of creative systems: those which are purely generative; those which contain internal or external feedback; and those which are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and techniques are suggested to help researchers 1) evaluate their systems' creative process and generated artefacts, and test their impact on the perceptual, cognitive, and affective states of the audience, and 2) build mechanisms for reflection into the creative system, including models of human perception and cognition, to endow creative systems with internal evaluative mechanisms to drive self-reflective processes. The first type of evaluation can be considered external to the creative system, and may be employed by the researcher to both better understand the efficacy of their system and its impact, and to incorporate feedback into the system. Here, we take the stance that understanding human creativity can lend insight to computational approaches, and knowledge of how humans perceive creative systems and their output can be incorporated into artificial agents as feedback to provide a sense of how a creation will impact the audience. The second type centers around internal evaluation, in which the system is able to reason about its own behavior and generated output. We argue that creative behavior cannot occur without feedback and reflection by the creative/metacreative system itself. More rigorous empirical testing will allow computational and metacreative systems to become more creative by definition, and can be used to demonstrate the impact and novelty of particular approaches.
We present a novel hypothetical account of entrainment in music and language, in context of the Information Dynamics of Thinking model, IDyOT. The extended model affords an alternative view of entrainment, and its companion term, pulse, from earlier accounts. The model is based on hierarchical, statistical prediction, modeling expectations of both what an event will be and when it will happen. As such, it constitutes a kind of predictive coding, with a particular novel hypothetical implementation. Here, we focus on the model's mechanism for predicting when a perceptual event will happen, given an existing sequence of past events, which may be musical or linguistic. We propose a range of tests to validate or falsify the model, at various different levels of abstraction, and argue that computational modeling in general, and this model in particular, can offer a means of providing limited but useful evidence for evolutionary hypotheses.
This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractAn increasing number of tools are being developed to help academics interact with information, but little is known about the benefits of those tools for their users. This study evaluated academics' receptiveness to information proposed by a mobile app, the SerenA Notebook: information that is based in their inferred interests but does not relate directly to a prior recognized need. The evaluated app aimed at creating the experience of serendipitous encounters: generating ideas and inspiring thoughts, and potentially triggering follow--up actions, by providing users with information related to their work and leisure interests in the form of suggestions. We studied how 20 academics interacted with messages sent by the mobile app at a rate of 3 per day over ten consecutive days.Collected data sets were analyzed using thematic analysis. We found that contextual factors (location, activity and focus) strongly influenced academics' responses to messages. Academics described some unsolicited information as interesting but irrelevant when they could not make immediate use of it. They highlighted filtering information as their major struggle rather than finding information. Some messages that were positively received acted as reminders of activities participants were meant to be doing but were postponing, or were relevant to ongoing activities at the time the information was received.
Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between
This chapter examines the field of algorithmic composition from the perspective of computational creativity. It begins by introducing the idea of computational creativity as a philosophical perspective. Next, it introduces a method for consideration of the properties of creative systems, the Creative Systems Framework (CSF; Wiggins, 2006a,b). The CSF becomes the starting point for a discussion of a system of comparison specific to algorithmic composition as an artistic and technical practice. Finally, the chapter sketches a road map for future developments in algorithmic composition and live coding, in these terms.
Two modest-sized symbolic corpora of post-tonal and post-metrical keyboard music have been constructed, one algorithmic, the other improvised. Deep learning models of each have been trained. The purpose was to obtain models with sufficient generalisation capacity that in response to separate fresh input seed material, they can generate outputs that are statistically distinctive, neither random nor recreative of the learned corpora or the seed material. This objective has been achieved, as judged by k-sample Anderson-Darling and Cramer tests. Music has been generated using the approach, and preliminary informal judgements place it roughly on a par with an example of composed music in a related form. Future work will aim to enhance the model such that it deserves to be fully evaluated in relation to expression, meaning and utility in real-time performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.