Readers make targeted regressions to plausible errors in reanalysis of "noisy-channel garden-path" sentences
Psycholinguistics research reveals how human readers deploy targeted eye movements to locate plausible error sites when encountering garden-path sentences that violate late-stage expectations. The work validates computational models of noisy-channel language processing, where comprehenders infer input corruption rather than reanalyze syntax. This finding matters for LLM developers building robust parsing and error-recovery mechanisms, and for interpretability researchers studying how neural language models might implement similar inference patterns during decoding.
Modelwire context
ExplainerThe study's key finding isn't just that readers recover from garden-path errors, but that they do so by inferring input corruption rather than revising their parse tree. This shifts the cognitive model from syntax-repair to signal-recovery, a distinction that changes what we'd expect LLMs to learn.
This connects directly to the mechanistic interpretability work from earlier this month on how language models route signals through internal subspaces (the Latin-trigger backdoor study). Both papers assume models implement inference patterns that operate at a layer below surface syntax. The garden-path work provides psycholinguistic evidence that humans may use similar latent-layer inference for error handling. If LLMs are learning analogous patterns, understanding the human baseline becomes crucial for predicting failure modes in deployed systems.
If researchers run the same eye-tracking protocol on sentences where the 'error' is actually a real typo versus a genuine syntactic ambiguity, and find that readers deploy different targeting strategies, that confirms the noisy-channel model is doing real work. If the distinction collapses, the effect may just be attention redistribution without genuine inference.
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Mentionsnoisy-channel processing · garden-path sentences · psycholinguistics · language comprehension · reanalysis
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