Creativity

Explaining Imagination ◽

10.1093/oso/9780198815068.003.0012 ◽

2020 ◽

pp. 262-296

Author(s):

Peter Langland-Hassan

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Prior Experience ◽

Thought Processes ◽

Psychological States ◽

Creative Cognition ◽

Personal Character ◽

Creative Products ◽

Artificial Neural

Comparatively easy questions we might ask about creativity are distinguished from the hard question of explaining transformative creativity. Many have focused on the easy questions and, in so doing, have offered no reason to think that the imagining relied upon in creative cognition cannot be reduced to more basic folk psychological states. The relevance of associative thought processes to songwriting is then explored as a means for understanding the nature of transformative creativity. Productive artificial neural networks—known as generative antagonistic networks (GANs)—are recent examples of how a system’s ability to generate creative products can be both finely tuned by prior experience and grounded in strategies that are inarticulable to the system itself. Further, the kinds of processes exploited by GANs need not be seen as incorporating anything akin to sui generis imaginative states. The chapter concludes with reflection on the added relevance of personal character to explanations of creativity.

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The Algorithms of Mindfulness

Science Technology & Human Values ◽

10.1177/01622439211025632 ◽

2021 ◽

pp. 016224392110256

Author(s):

Johannes Bruder

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Information Overload ◽

Learning Systems ◽

Emotional Resilience ◽

Creative Cognition ◽

North Americans ◽

Artificial Neural ◽

Algorithmic Techniques

This paper analyzes notions and models of optimized cognition emerging at the intersections of psychology, neuroscience, and computing. What I somewhat polemically call the algorithms of mindfulness describes an ideal that determines algorithmic techniques of the self, geared at emotional resilience and creative cognition. A reframing of rest, exemplified in corporate mindfulness programs and the design of experimental artificial neural networks sits at the heart of this process. Mindfulness trainings provide cues as to this reframing, for they detail each in their own way how intermittent periods of rest are to be recruited to augment our cognitive capacities and combat the effects of stress and information overload. They typically rely on and co-opt neuroscience knowledge about what the brains of North Americans and Europeans do when we rest. Current designs for artificial neural networks draw on the same neuroscience research and incorporate coarse principles of cognition in brains to make machine learning systems more resilient and creative. These algorithmic techniques are primarily conceived to prevent psychopathologies where stress is considered the driving force of success. Against this backdrop, I ask how machine learning systems could be employed to unsettle the concept of pathological cognition itself.

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