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Audio Visual Association Test in Non Synesthetic Subjects: Technological Tailoring of the Methods

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13811))

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Abstract

Synesthesia is a relatively rare condition for which a specific stimulus activates one or more senses different from the ones usually designed for its evaluation; this can happen in any combination of senses or cognitive paths. In this work, we focus on audio-visual synesthesia, one of the most common. Many investigators have studied the connection between music and colours in synesthetic and non-synesthetic subjects in the past years, exploring the possibility of an emotional gate between the perception of these stimuli. In this study, an experiment was designed to verify whether this connection can be extended to music and images, considering not only colour but also shape and their spatial distribution. In the study, participants’ musical and visual abilities will also be tested to explore any correlation with their music-images association.

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Notes

  1. 1.

    https://p5js.org/.

  2. 2.

    https://nodejs.org/.

  3. 3.

    https://firebase.google.com/.

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Author information

Authors and Affiliations

  1. Department of Engineering, Campus Bio-Medico University of Rome, Rome, Italy

    Costanza Cenerini, Luca Vollero & Giorgio Pennazza

  2. Department of Science and Technology for Humans and the Environment, Campus Bio-Medico University of Rome, Rome, Italy

    Marco Santonico

  3. Department of Medicine and Surgery, Campus Bio-Medico University of Rome, Rome, Italy

    Flavio Keller

Authors
  1. Costanza Cenerini
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  2. Luca Vollero
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  3. Giorgio Pennazza
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  4. Marco Santonico
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  5. Flavio Keller
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Corresponding author

Correspondence to Costanza Cenerini .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Cenerini, C., Vollero, L., Pennazza, G., Santonico, M., Keller, F. (2023). Audio Visual Association Test in Non Synesthetic Subjects: Technological Tailoring of the Methods. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_32

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  • DOI: https://doi.org/10.1007/978-3-031-25891-6_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25890-9

  • Online ISBN: 978-3-031-25891-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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