Coastal river deltas are complex and dynamic ecosystems where vegetation plays an essential role in influencing, as well as being influenced by, physical processes, creating ecogeomorphic feedbacks between vegetation canopy characteristics and topography. However, this feedback is poorly understood. This knowledge gap is due to difficulties in detecting and quantifying the interactions that define the feedback. Emerging technology and data analysis techniques like transfer entropy have made it possible to overcome former difficulties associated with sampling constraints and delineate bidirectional feedback within many vegetation classes at the delta scale. Here the transfer entropy analysis was consistent with widespread understanding of marsh zonation, yet produced additional insight into which vegetation classes specifically had a dominant impact on topographic change. Ecogeomorphic feedback was resolvable only within native vegetation classes (
We studied the learnability of English filler-gap dependencies and the “island” constraints on them by assessing the generalizations made by autoregressive (incremental) language models that use deep learning to predict the next word given preceding context. Using factorial tests inspired by experimental psycholinguistics, we found that models acquire not only the basic contingency between fillers and gaps, but also the unboundedness and hierarchical constraints implicated in the dependency. We evaluated a model’s acquisition of island constraints by demonstrating that its expectation for a filler-gap contingency is attenuated within an island environment. Our results provide empirical evidence against the argument from the poverty of the stimulus for this particular structure.
more » « less- Award ID(s):
- 2121074
- NSF-PAR ID:
- 10488147
- Publisher / Repository:
- MIT Press
- Date Published:
- Journal Name:
- Linguistic Inquiry
- ISSN:
- 0024-3892
- Page Range / eLocation ID:
- 1 to 44
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1162/ling_a_00491
