Abstraction in time: Finding hierarchical linguistic structure in a model of relational processing


Abstract mental representation is fundamental for human cognition. Forming such representations in time, especially from dynamic and noisy perceptual input, is a challenge for any processing modality, but perhaps none so acutely as for language processing. We show that LISA (Hummel & Holyaok, 1997) and DORA (Doumas, Hummel, & Sandhofer, 2008), models built to process and to learn structured (i.e., symbolic) representations of conceptual properties and relations from unstructured inputs, show oscillatory activation during processing that is highly similar to the cortical activity elicited by the linguistic stimuli from Ding et al. (2016). We argue, as Ding et al. (2016), that this activation reflects formation of hierarchical linguistic representation, and furthermore, that the kind of computational mechanisms in LISA/DORA (e.g., temporal binding by systematic asynchrony of firing) may underlie formation of abstract linguistic representations in the human brain.

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