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Edward Gibson

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Computation and Language math.PR Neurons and Cognition physics.data-an

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preprint2026arXiv

Readers make targeted regressions to plausible errors in reanalysis of "noisy-channel garden-path" sentences

A key question in psycholinguistics is how inferences about the meaning of linguistic input unfold incrementally a comprehender's mind. In this work, we study reading dynamics for ``noisy-channel garden-path'' sentences, which temporarily appear well-formed but feature late-appearing violations of expectation that can be resolved not by inferring an alternative syntactic structure, but by inferring the presence of an error. We find evidence for targeted regressions -- eye movements towards regions that are promising loci of possible errors in light of later-arriving information, showing patterns consistent with the posterior inferences of a model of noisy-channel processing with reanalysis. We discuss the implications of these findings for theories of noisy-channel language comprehension and information-theoretic explanations of reading dynamics.

preprint2016arXiv

An attempted replication of Hackl, Koster-Hale, Varvoutis (2012)

Hackl, Koster-Hale & Varvoutis (2012; Journal of Semantics, 29, 145-206; HKV) provide data that suggested that in a null context, antecedent-contained-deletion (ACD) relative clause structures modifying a quantified object noun phrase are easier to process than those modifying a definite object NP. HKV argue that this pattern of results supports a quantifier-raising (QR) analysis of both ACD structures and quantified NPs in object position: under the account that they advocate, both ACD resolution and quantified NPs in object position require movement of the object NP to a higher syntactic position. The processing advantage for quantified object NPs in ACD is hypothesized to derive from the fact that - at the point where ACD resolution must take place - the quantified NP has already undergone QR whereas this is not the case for definite NPs. Here, we report attempted replications of their self-paced reading Experiments 1 and 2. We do not replicate the critical interactions in any of the words immediately following the disambiguating verb in either experiment. Putting these observations together with the observation that it was only post-hoc analysis decisions that were responsible for HKV's observed effects in the first place (Gibson et al., submitted), we conclude that the experiments reported by HKV should not be viewed as providing evidence for the ACD quantifier raising processing effect.

preprint2015arXiv

Response to Liu, Xu, and Liang (2015) and Ferrer-i-Cancho and Gómez-Rodríguez (2015) on Dependency Length Minimization

We address recent criticisms (Liu et al., 2015; Ferrer-i-Cancho and Gómez-Rodríguez, 2015) of our work on empirical evidence of dependency length minimization across languages (Futrell et al., 2015). First, we acknowledge error in failing to acknowledge Liu (2008)'s previous work on corpora of 20 languages with similar aims. A correction will appear in PNAS. Nevertheless, we argue that our work provides novel, strong evidence for dependency length minimization as a universal quantitative property of languages, beyond this previous work, because it provides baselines which focus on word order preferences. Second, we argue that our choices of baselines were appropriate because they control for alternative theories.

preprint2013arXiv

Information content versus word length in natural language: A reply to Ferrer-i-Cancho and Moscoso del Prado Martin [arXiv:1209.1751]

Recently, Ferrer i Cancho and Moscoso del Prado Martin [arXiv:1209.1751] argued that an observed linear relationship between word length and average surprisal (Piantadosi, Tily, & Gibson, 2011) is not evidence for communicative efficiency in human language. We discuss several shortcomings of their approach and critique: their model critically rests on inaccurate assumptions, is incapable of explaining key surprisal patterns in language, and is incompatible with recent behavioral results. More generally, we argue that statistical models must not critically rely on assumptions that are incompatible with the real system under study.