Andrea E. Martin

Publications

Displaying 1 - 2 of 2
  • Doumas, L. A. A., Martin, A. E., & Hummel, J. E. (2020). Relation learning in a neurocomputational architecture supports cross-domain transfer. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 932-937). Montreal, QB: Cognitive Science Society.

    Abstract

    Humans readily generalize, applying prior knowledge to novel situations and stimuli. Advances in machine learning have begun to approximate and even surpass human performance, but these systems struggle to generalize what they have learned to untrained situations. We present a model based on wellestablished neurocomputational principles that demonstrates human-level generalisation. This model is trained to play one video game (Breakout) and performs one-shot generalisation to a new game (Pong) with different characteristics. The model
    generalizes because it learns structured representations that are functionally symbolic (viz., a role-filler binding calculus) from unstructured training data. It does so without feedback, and without requiring that structured representations are specified a priori. Specifically, the model uses neural co-activation to discover which characteristics of the input are invariant and to learn relational predicates, and oscillatory regularities in network firing to bind predicates to arguments. To our knowledge,
    this is the first demonstration of human-like generalisation in a machine system that does not assume structured representa-
    tions to begin with.
  • Hashemzadeh, M., Kaufeld, G., White, M., Martin, A. E., & Fyshe, A. (2020). From language to language-ish: How brain-like is an LSTM representation of nonsensical language stimuli? In T. Cohn, Y. He, & Y. Liu (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 645-655). Association for Computational Linguistics.

    Abstract

    The representations generated by many mod-
    els of language (word embeddings, recurrent
    neural networks and transformers) correlate
    to brain activity recorded while people read.
    However, these decoding results are usually
    based on the brain’s reaction to syntactically
    and semantically sound language stimuli. In
    this study, we asked: how does an LSTM (long
    short term memory) language model, trained
    (by and large) on semantically and syntac-
    tically intact language, represent a language
    sample with degraded semantic or syntactic
    information? Does the LSTM representation
    still resemble the brain’s reaction? We found
    that, even for some kinds of nonsensical lan-
    guage, there is a statistically significant rela-
    tionship between the brain’s activity and the
    representations of an LSTM. This indicates
    that, at least in some instances, LSTMs and the
    human brain handle nonsensical data similarly.

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