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Vygotsky Meets Backpropagation

Artificial Neural Models and the Development of Higher Forms of Thought

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Artificial Intelligence in Education (AIED 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10947))

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Abstract

In this paper we revisit Vygotsky’s developmental model of concept formation, and use it to discuss learning in artificial neural networks. We study learning in neural networks from a learning science point of view, asking whether it is possible to construct systems that have developmental patterns that align with empirical studies on concept formation. We put the state-of-the-art Inception-v3 image recognition architecture in an experimental setting that highlights differences and similarities in algorithmic and human cognitive processes.

The Vygotskian model of cognitive development reveals important limitations in currently popular neural algorithms, and puts neural AI in the context of post-behavioristic science of learning. At the same time, the Vygotskian model of development of thought suggests new architectural principles for developing AI, machine learning, and systems that support human learning. In this context we can ask what would it take for machines to learn, and what could they learn from research on learning.

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Notes

  1. 1.

    Vygotsky [29] discusses experiments done with the words mur, cev, bik, and lag. We use these in training the Inception network.

  2. 2.

    https://www.tensorflow.org/tutorials/image_recognition.

  3. 3.

    Relational information might, however, be captured using other network architectures, e.g., [3, 7, 13, 19].

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Authors and Affiliations

  1. Meaning Processing, Helsinki, Finland

    Ilkka Tuomi

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  1. Ilkka Tuomi
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Correspondence to Ilkka Tuomi .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Penstein Rosé

  2. University of Technology, Sydney, NSW, Australia

    Roberto Martínez-Maldonado

  3. University of Duisburg-Essen, Duisburg, Germany

    H. Ulrich Hoppe

  4. UCL Institute of Education, London, UK

    Rose Luckin

  5. UCL Institute of Education, London, UK

    Manolis Mavrikis

  6. UCL Institute of Education, London, UK

    Kaska Porayska-Pomsta

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  8. University of Sussex, Brighton, UK

    Benedict du Boulay

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Tuomi, I. (2018). Vygotsky Meets Backpropagation. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10947. Springer, Cham. https://doi.org/10.1007/978-3-319-93843-1_42

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  • DOI: https://doi.org/10.1007/978-3-319-93843-1_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93842-4

  • Online ISBN: 978-3-319-93843-1

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