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Artificial intelligence & neurophysiology How Our Brain Analyzes Poetry

| Author / Editor: Marilena Hoff* / Dr. Ilka Ottleben

Artificial intelligence and 80,000 ancient Chinese poems show that constraint poetic structures aid poetry appreciation

Artificial intelligence and 80,000 ancient Chinese poems show that constraint poetic structures aid poetry appreciation
Artificial intelligence and 80,000 ancient Chinese poems show that constraint poetic structures aid poetry appreciation
(Source: New York University Shanghai )

Frankfurt/Germany — Poetry, an aesthetic object, is deeply rooted in all human cultures. But despite its universality, the neural mechanisms of poetry appreciation are seldom studied. Now, by combining artificial intelligence and neurophysiology, an international team of researchers from the Max Planck Institute for Empirical Aesthetics, the Max Planck Institute for Psycholinguistics, Google, New York University Shanghai, and East China Normal University have investigated how the highly structured format of poetry aids poetry appreciation.

Poetry — a matter of structure and deviation

Poetic language typically deviates from ordinary language, and unique, often unusual combinations of words are chosen to maximize poetic effects. Such deviation is also found in poetic structure with its formalizations of lines and rhymes.

But why is poetry so strictly structured as compared with other literary genres or communicative language? And how does this structuredness aid in conveying meanings and arousing aesthetic experience? This international research team theorized that the constrained structure of poetry serves as a mental template that allows readers and listeners to group creative poetic language into coherent meanings.

Artificial Jueju poems from eighty thousand sources

In order to test their hypothesis, the team focused on a genre of ancient Chinese poetry called Jueju, which has a highly constrained style. They generated artificial poems using a recurrent neural network so they could present novel Jueju poems to their participants, while controlling the poetic content.

Nearly eighty thousand ancient poems written over the course of five Chinese dynasties were fed into the neural network model, which then learned to create artificial poems based on the Jueju form. The researchers synthesized each poem into a speech stream, removing the pauses, intonation, and other prosodic cues that a human speaker would produce, so that listeners had to rely on their knowledge of poetic structures in order to parse the stream.

Poetry in brain

Native Chinese speakers then listened to the artificial speech streams in an MEG scanner, while the researchers aimed to detect neural signatures in the participants’ brains that corresponded to the poetic structure. And indeed, the scientists discovered a brain rhythm of around 0.67 Hertz corresponding to the line structure of Jueju.

Even though the modern Chinese listeners were hearing each “pseudo ancient” poem for the first time and could not fully understand every phrase in the poems, they recognized the highly constrained structure and then actively grouped the poetic speech stream into lines according to their prior knowledge of Jueju. When the participants listened to the same poem for the second time, their brains had learned the structure, which allowed them to predict the forthcoming lines.

This study suggests that a constrained formal and conceptual structure provides a poetic temporal frame for listeners to group semantic units as intended by the poets and even to anticipate them. It indicates that not just poetic language, but the interplay of predictable forms and unpredictable contents are essential to the aesthetic experience of poems.

Originalpublication: Xiangbin Teng, Min Ma, Jinbiao Yang, Stefan Blohm, Qing Cai, Xing Tian: Constrained Structure of Ancient Chinese Poetry Facilitates Speech Content Grouping; Current Biology, Volume 30, Issue 7, P1299-1305.e7, April 06, 2020; Published:March 05, 2020 DOI: https://doi.org/10.1016/j.cub.2020.01.059 dpo

* M. Hoff: Max-Planck-Institut für empirische Ästhetik, 60322 Frankfurt am Main/Germany

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