# Informing Artificial Intelligence Generative Techniques using Cognitive Theories of Human Creativity

## TL;DR

This paper explores integrating cognitive theories of human creativity into AI generative models, demonstrating how concepts like honing theory and intrinsic motivation can enhance computational art, and vice versa.

## Contribution

It presents a novel synthesis of deep learning and cognitive theories, showing how human creativity models can improve AI-generated art and inform psychological understanding.

## Key findings

- Implementation of honing theory and intrinsic motivation in generative art
- Impact of cognitive concepts on the quality of AI-generated outputs
- Cross-fertilization ideas between AI creativity and psychology

## Abstract

The common view that our creativity is what makes us uniquely human suggests that incorporating research on human creativity into generative deep learning techniques might be a fruitful avenue for making their outputs more compelling and human-like. Using an original synthesis of Deep Dream-based convolutional neural networks and cognitive based computational art rendering systems, we show how honing theory, intrinsic motivation, and the notion of a 'seed incident' can be implemented computationally, and demonstrate their impact on the resulting generative art. Conversely, we discuss how explorations in deep learn-ing convolutional neural net generative systems can inform our understanding of human creativity. We conclude with ideas for further cross-fertilization between AI based computational creativity and psychology of creativity.

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Source: https://tomesphere.com/paper/1812.05556