Leaps and Bounds: An Introduction to the Field of Computational Creativity

Veale, Tony; Pérez, Rafael Pérez y

doi:10.1007/s00354-020-00116-w

Cited by 10 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For its part, though several computational models of creativity exist, they have been developed in relative isolation from empirical research, and surprisingly few are well-suited to modeling the cognitive theories of NCR in a way that can be easily compared to human performance. Since a clearer understanding of human creativity could lead to more creative artificial systems, further integration and collaboration between computational modeling and NCR stands to benefit both fields greatly (Chateau-Laurent & Alexandre, 2021; Dipaola et al, 2018; Veale & Pérez y Pérez, 2020; Wiggins & Bhattacharya, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…This approach would bring considerable clarity to our understanding of creative cognition, allowing researchers to rigorously compare different theories and make inferences about underlying processes. Such integration between NCR and computational modeling would, in turn, aid the development of artificial creative systems (Chateau-Laurent & Alexandre, 2021; Wiggins & Bhattacharya, 2014) since a more algorithmic understanding of human creative cognition could inform models of autonomous creativity (Dipaola et al, 2018; Veale & Pérez y Pérez, 2020).…”

Section: Future Steps For Computational Models Of Creative Cognitionmentioning

confidence: 99%

“…Existing models of creative cognition arguably fall into both camps, and both are useful to NCR. While descriptive models are crucial for a precise understanding of human creative cognition, normative models can demonstrate how a particular creative task could be optimally performed, inspiring the development of descriptive models and guiding empirical research efforts (Veale & Pérez y Pérez, 2020).…”

Section: Varieties Of Computational Modelmentioning

confidence: 99%

See 2 more Smart Citations

Toward greater computational modeling in neurocognitive creativity research.

Lloyd-Cox,

Pickering,

Beaty

et al. 2023

Psychology of Aesthetics, Creativity, and the Arts

View full text Add to dashboard Cite

Creative cognition is the driving force behind all cultural and scientific progress. In recent years, the field of neurocognitive creativity research (NCR) has made considerable progress in revealing the neural and psychological correlates of creative cognition. However, a detailed understanding of how cognitive processes produce creative ideas, and how these processes interact differently across tasks and individuals, remains elusive. In this article, we argue that the increased adoption of computational modeling can help greatly in achieving this goal. While the verbal theories guiding NCR have evolved from broader accounts into more specific descriptions of neurocognitive processes, they remain more open to interpretation and harder to falsify than formal models. Translating theories into computational models can make them more concrete, accessible, and easier to compare, and helps researchers to develop causal hypotheses for how variation in cognitive factors leads to variation in creative outcomes. Currently, however, computational modeling of creativity is conducted almost entirely separately from NCR, and few attempts have been made to embody the cognitive theories of NCR in models that can simulate performance on common lab-based tasks. In this article, we discuss theories of creative cognition and how they might benefit from the wider adoption of formal modeling. We also examine recent computational models of creativity and how these might be improved and better integrated with NCR. Finally, we describe a pathway toward a mechanistic understanding of creative cognition through the integration of computational modeling, psychological theory, and empirical research, outlining an example model based on dual-process accounts. Public Significance StatementThis review argues that creativity research would benefit greatly from the wider adoption of computational modeling. We discuss how translating verbal theories of creative cognition into formal computational models can make them more rigorous, accessible, and communicable, and can highlight questions for future research. We examine previous models of creativity and explain how these can be improved to benefit our understanding of human creative cognition and the development of artificial creative systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Future Steps For Computational Models Of Creative Cognitionmentioning

confidence: 99%

Section: Varieties Of Computational Modelmentioning

confidence: 99%

See 1 more Smart Citation

Toward greater computational modeling in neurocognitive creativity research.

Lloyd-Cox,

Pickering,

Beaty

et al. 2023

Psychology of Aesthetics, Creativity, and the Arts

View full text Add to dashboard Cite

show abstract

“…This PhD thesis is also related to Computational Creativity (CC), a multidisciplinary field of research that is concerned with replicating, understanding and enhancing human creativity through computational tools (Veale and Pérez y Pérez, 2020). 1 In language, this has to deal with artifacts such as poetry, literature, and, especially for us, verbal humor.…”

Section: Introductionmentioning

confidence: 99%

Towards Generation and Recognition of Humorous Texts in Portuguese

Lima Inácio,

Gonçalo Oliveira

2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Student Research W

View full text Add to dashboard Cite

Dealing with humor is an important step to develop Natural Language Processing tools capable of handling sophisticated semantic and pragmatic knowledge. In this context, this PhD thesis focuses on the automatic generation and recognition of verbal punning humor in Portuguese, which is still an underdeveloped language when compared to English. One of the main goals of this research is to conciliate Natural Language Generation computational models with existing theories of humor from the Humanities while avoiding mere generation by including contextual information into the generation process. Another point that is of utmost importance is the inclusion of the listener as an active part in the process of understanding and creating humor; we hope to achieve this by using concepts from Recommender Systems in our methods. Ultimately, we want to not only advance the current state-of-the-art in humor generation and recognition, but also to help the general Portuguese-speaking research community with methods, tools and resources that may aid in the development of further techniques for this language. We also expect our systems to provide insightful ideas about how humor is created and perceived by both humans and machines.

show abstract

“…One essence of creative design activity is the reliance on tools, as history has witnessed the advancement of human design and creativity in lockstep with the evolution of their tools (Veale & Pérez y Pérez, 2020). The vision that machines will actively aid or transform the architectural design process has been around since the 1950s.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the noisy landscape: Architectural conceptual design space interpretation using disentangled representation learning

Chen

Stouffs

2022

Computer aided Civil Eng

View full text Add to dashboard Cite

Time and resource restrictions limit the architect's design scope. Computational design methods can offer support to overcome these limitations. Design exploration has been a long‐established task in computational‐aided generative design. However, conventional objective‐ and performance‐based systems have restrictions pertaining to the exploration scope. Without a quasi‐global cognition of the conceptual design space, the exploration scope is bound to be limited. This paper is a proposal for an epistemic shift toward the interpretation of conceptual design space per se. This topic receives limited attention in the current literature due to the scarcity of interpretation tools. Using a customized large‐scale architectural image database with high‐level visual diversity and latent data space coverage, this paper serves as a first attempt to investigate the possibilities of leveraging disentangled representation learning to structurally interpret architectural conceptual design space in both supervised and unsupervised manner. Various schemes of supervised disentanglement are tested, with analytical comparisons indicating discrepant structural traits of different latent spaces. The unsupervised interpretation scheme shows the preliminary capability of automatic feature disentanglement. Our long‐term objective is to offer designers a broader spectrum of creative design through innovative design systems.

show abstract

Leaps and Bounds: An Introduction to the Field of Computational Creativity

Cited by 10 publications

References 40 publications

Toward greater computational modeling in neurocognitive creativity research.

Toward greater computational modeling in neurocognitive creativity research.

Towards Generation and Recognition of Humorous Texts in Portuguese

Deciphering the noisy landscape: Architectural conceptual design space interpretation using disentangled representation learning

Contact Info

Product

Resources

About