Creativity explained by Computational Cognitive Neuroscience

Neural Information Processing–Letters …, 2007

Neurocognitive model inspired by the putative processes in the brain has been applied to invention of novel words. This domain is proposed as the simplest way to understand creativity using experimental and computational means. Three factors are essential for creativity in this domain: knowledge of the statistical language properties, imagination constrained by this knowledge, and filtering of results that selects most interesting novel words. These principles are implemented using a simple correlation-based algorithm for auto-associative memory that learns the statistical properties of language. Results are surprisingly similar to those created by humans. Perspectives on computational models of creativity are discussed.

Handbook of Creativity

Cr eativity is a marvel of the human mind, and an obvious goal for AI workers. Indeed, the proposal that led to the famous 1956 Dartmouth Summer School often remembered as the time of AI's birth mentioned "creativity," "invention," and "discovery" as key aims for the newly named discipline (McCarthy et al. 1955, 45, 49ff.). And, 50 years later, Herb Simon-in an email discussion between AAAI Fellows (quoted in Boden 2006, 1101)-cited a paper on creativity in answer to the challenge of whether AI is a science, as opposed to "mere" engineering. But if its status as an AI goal is obvious, its credibility as a potential AI achievement is not. Many people, including many otherwise hard-headed scientists, doubt-or even deny outright-the possibility of a computer's ever being creative. Sometimes, such people are saying that, irrespective of its performance (which might even match superlative human examples), no computer could "really" be creative: the creativity lies entirely in the programmer. That's a philosophical question that needn't detain us here (but see the final section, below). More to the point, these people typically claim that a computer simply could not generate apparently creative performance. That's a factual claim-which, in effect, dismisses AI research on creativity as a waste of time. However, it is mistaken: AI scientists working in this area aren't doomed to disappointment. It doesn't follow that they will ever, in practice, be able to engineer a new Shakespeare or a neo-Mozart (although the latter goal has been approached more nearly than most people imagine). But lesser examples of AI creativity already abound. And, crucially, they help us to understand how human creativity is possible.

Creativity and the Brain, 2007

Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed.

Psychonomic Bulletin & Review, 2004

This article outlines a framework of creativity based on functional neuroanatomy. Recent advances in the field of cognitive neuroscience have identified distinct brain circuits that are involved in specific higher brain functions. To date, these findings have not been applied to research on creativity. It is proposed that there are four basic types of creative insights, each mediated by a distinctive neural circuit. By definition, creative insights occur in consciousness. Given the view that the working memory buffer of the prefrontal cortex holds the content of consciousness, each of the four distinctive neural loops terminates there. When creativity is the result of deliberate control, as opposed to spontaneous generation, the prefrontal cortex also instigates the creative process. Both processing modes, deliberate and spontaneous, can guide neural computation in structures that contribute emotional content and in structures that provide cognitive analysis, yielding the four basic types of creativity. Supportive evidence from psychological, cognitive, and neuroscientific studies is presented and integrated in this article. The new theoretical framework systematizes the interaction between knowledge and creative thinking, and how the nature of this relationship changes as a function of domain and age. Implications for the arts and sciences are briefly discussed.

Proceedings of the 7th Computational Creativity Symposium at AISB 2021, 2021

This philosophical paper examines the Darwinian account of creativity as a model for assessing computational creativity. It will first establish a Darwinian account of creativity using Simonton’s model. It will then apply this model to popular image-producing AI, Generative Adversarial Networks, and the promising Creative Adversarial Network, both used in the computational production of ‘artworks’. The paper will argue that these networks are compatible with a Darwinian account of creativity, due to the presence of blind variation within the networks, a key component of Simonton’s model. The paper will then address some initial objections. The aim of this paper will ultimately be to assess whether the AI systems are compatible with the Darwinian model of creativity, and in the process explore Darwinian creativity as a potential standard for testing computational creativity.

2025

Creativity is one of the most sophisticated and essential capabilities of the human brain, enabling the generation of novel and valuable ideas across domains such as art, science, and technology. Grounded in neuroscience, this paper explores the intricate neural mechanisms underlying creative thinking. It focuses on the dynamic interplay of three large-scale neural networks: the Default Mode Network (DMN), responsible for spontaneous ideation and divergent thinking [1]; the Executive Control Network (ECN), critical for evaluative processes and convergent thinking [2]; and the Salience Network (SN), which mediates cognitive transitions and prioritises relevant information [3]. Additionally, the cognitive processes of bending (restructuring information) [4], breaking (deconstructing established ideas) [5], and blending (synthesising novel constructs) [6] are discussed as essential mechanisms of creative ideation. The paper also examines how collaborative environments, particularly virtual ones, amplify creative potential by integrating diverse neural activity [7]. Practical implications are offered to enhance creativity in individuals and teams, with a focus on optimising individual cognitive functioning [8], leveraging collaboration [9], and applying visual representations to connect and integrate ideas [10]. These findings bridge the gap between neuroscientific theory and realworld applications, contributing to a deeper understanding of how creativity can be nurtured and harnessed. 1. Introduction. Creativity is often heralded as one of the most defining traits of humanity, enabling individuals and societies to adapt, innovate, and solve complex challenges. From artistic

Bulletin of Science, Technology & Society, 2009

Creativity Research Journal, 2007

The authors put forward a novel theory to account for creativity and innovation, with particular reference to mathematical discovery, but they do not define their conception of creativity or outline factors that they believe are critical to their model of creative function. Does their theory hold true only for mathematical creativity? What factors make mathematical creativity separable from artistic or even other types of scientific creativity? Clarity at the level of definitions is necessary not only because delimiting claims helps make hypotheses more concrete but also because it is only then possible to clearly assess the power and fitness of the model they propose.

This paper presents a multi-dimensional perspective for the study of creativity and formulates a framework for computational creativity that enables the definition of functional relationships among scales, and captures the effects of time. Its relevance and usefulness are shown first by classifying recent studies of computational creativity and second by illustrating multi-dimensional approaches to the computational study of creativity with sample simulation scenarios. The paper closes offering modeling guidelines for the computational studies of creativity.

New Generation Computing

Computers have enhanced productivity and cost-effectiveness in all of the creative industries, and their value as tools is rarely doubted. But can machines serve as more than mere tools, and assume the role and responsibilities of a co-creative partner, or even become goal-setting, autonomous creators in their own right? These are the questions that define the discipline of computational creativity. The answers require an algorithmic understanding of how humans give meaning to form, but a transformation in the way we think about creativity is unlikely to occur in a single bound. Rather, interdisciplinary insights from diverse fields must first inform our models, and shape a narrative of creativity in which machines are both tools and creators. To set the stage for the newest work, this introduction to the special issue on computational creativity shows where the field is going, and where it has come from.