How aesthetic appreciation is modulated by prior knowledge of authorship. A case for human versus Artificial Intelligence

Salvatore Gaetano Chiarella, Dionigi Mattia Gagliardi, Dario Rossi, Giulia Torromino, Fabio Babiloni e Giulia Cartocci
in NODES 15-16 →
Empirical aesthetics and neuroaesthetics investigate the cognitive and neurobiological processes involved in aesthetic appreciation (Zeki, 1999; Skov et al., 2018). This new relationship between aesthetics and science has led to a renewed concept of beauty and aesthetic appreciation. According to these studies, aesthetic appreciation is not intended as a simple and objective response to objects’ properties or configurations but rather as an attribute of our experience of objects that has been actively constructed by our cognitive and brain system (Corradini et al., 2020; Chatterjee & Vartanian, 2016). Thus, aesthetic appreciation is not conceived as an absolute value but rather it changes within individuals, populations, cultures and epochs (e.g., Nadal & Chatterjee, 2019). Recent studies have showed that aesthetic appreciation can be modulated by several factors ranging from expectation to context (see Corradini, 2020).
On the other hand, human creativity, intended as an exclusive human activity (Sternberg, 1999), has been questioned by the growing implementation of machine learning and artificial neural networks in the field of Artificial Intelligence (AI). The idea that AI would ultimately mimic all human’s abilities, including creativity, is long-standing and already existed in the founders of computational science (Lovelace, 1843; Turing, 1950). We are currently living in a new era in which AI shows creative abilities per se and it is used to produce artworks. Some AI have been developed to “compose” music, “write” poems, or “paint” pictures (Mazzone & Elgammal, 2019). Moreover, AI-artworks have been acknowledged as proper artworks by the art system, exhibited in important museums and sold by international auction houses for thousands of dollars (Goenaga, 2020).