Does antecedent complexity affect ellipsis processing? An empirical investigation

Paape, Dario; Nicenboim, Bruno; Vasishth, Shravan

doi:10.5334/gjgl.290

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…In brms, the latter can be done using the function set_prior(). One possible standard setup for diffuse priors which is sometimes used in reading studies (e.g., Paape, Nicenboim, & Vasishth, 2017;Vasishth, Mertzen, Jäger, & Gelman, 2018), but which we argue is an example of a "bad" prior, is as follows: For the intercept we use a normal distribution with mean 0 and standard deviation 10. Note that this is on the log scale as we assume a lognormal distribution of reading times.…”

Section: Prior Predictive Checksmentioning

confidence: 99%

Toward a principled Bayesian workflow in cognitive science.

Schad

Betancourt²,

Vasishth

2021

Psychological Methods

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192

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Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilistic programming languages such as Stan, and easily accessible front-end packages such as brms. The utility of Bayesian methods, however, ultimately depends on the relevance of the Bayesian model, in particular whether or not it accurately captures the structure of the data and the data analyst's domain expertise. Even with powerful software, the analyst is responsible for verifying the utility of their model. To demonstrate this point, we introduce a principled Bayesian workflow (Betancourt, 2018) to cognitive science. Using a concrete working example, we describe basic questions one should ask about the model: prior predictive checks, computational faithfulness, model sensitivity, and posterior predictive checks. The running example for demonstrating the workflow is data on reading times with a linguistic manipulation of object versus subject relative clause sentences. This principled Bayesian workflow also demonstrates how to use domain knowledge to inform prior distributions. It provides guidelines and checks for valid data analysis, avoiding overfitting complex models to noise, and capturing relevant data structure in a probabilistic model. Given the increasing use of Bayesian methods, we aim to discuss how these methods can be properly employed to obtain robust answers to scientific questions. All data and code accompanying this article are available from https://osf.io/b2vx9/.

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Section: Prior Predictive Checksmentioning

confidence: 99%

Toward a principled Bayesian workflow in cognitive science.

Schad

Betancourt²,

Vasishth

2021

Psychological Methods

Self Cite

178

192

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“…Following Paape et al (2017), we consider there to be evidence for a non-zero effect if 0 is not included in the 95% CrI, and there to be weak evidence for such an effect if the 95% CrI includes 0 but the probability of the parameter being either positive or negative is above 95% (e.g., the model is 95% confident that the parameter is positive). Table 2 reports the population-level effects of each parameter in the model.…”

Section: Modelmentioning

confidence: 99%

Vowel duration and the voicing effect across English dialects

Tanner

Sonderegger

Stuart‐Smith

2019

TWPL

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The 'voicing effect' -the durational difference in vowels preceding voiced and voiceless consonants -is a well-documented phenomenon in English, where it plays a key role in the production and perception of the English final voicing contrast. Despite this supposed importance, little is known as to how robust this effect is in spontaneous connected speech, which is itself subject to a range of linguistic factors. Similarly, little attention has focused on variability in the voicing effect across dialects of English, bar analysis of specific varieties. Our findings show that the voicing of the following consonant exhibits a weaker-than-expected effect in spontaneous speech, interacting with manner, vowel height, speech rate, and word frequency. English dialects appear to demonstrate a continuum of potential voicing effect sizes, where varieties with dialect-specific phonological rules exhibit the most extreme values. The results suggest that the voicing effect in English is both substantially weaker than previously assumed in spontaneous connected speech, and subject to a wide range of dialectal variability.

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“…The extensive literature on this phenomenon has focused on systemic questions like the modeling of the ellipsis site, the relation between the ellipsis site and its antecedent (or postcedent) and the licensing conditions of VP ellipsis (see e.g., Merchant, 2018 ; Reich, 2018 , for recent overviews). Analogously, the psycholinguistic literature mainly addressed procedural aspects of the relation between antecedent and target such as complexity effects (see e.g., Frazier et al, 2000 ; Frazier and Clifton, 2001 ; Apel et al, 2007 ; Martin and McElree, 2008 ; Paape et al, 2017 ). However, to the best of our knowledge, the question of when and why speakers actually make use of VP ellipsis given that the corresponding full form is also available to them has not yet been investigated in the literature.…”

Section: Introductionmentioning

confidence: 99%

The Role of UID for the Usage of Verb Phrase Ellipsis: Psycholinguistic Evidence From Length and Context Effects

et al. 2021

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We investigate the underexplored question of when speakers make use of the omission phenomenon verb phrase ellipsis (VPE) in English given that the full form is also available to them. We base the interpretation of our results on the well-established information-theoretic Uniform Information Density (UID) hypothesis: Speakers tend to distribute processing effort uniformly across utterances and avoid regions of low information by omitting redundant material through, e.g., VPE. We investigate the length of the omittable VP and its predictability in context as sources of redundancy which lead to larger or deeper regions of low information and an increased pressure to use ellipsis. We use both naturalness rating and self-paced reading studies in order to link naturalness patterns to potential processing difficulties. For the length effects our rating and reading results support a UID account. Surprisingly, we do not find an effect of the context on the naturalness and the processing of VPE. We suggest that our manipulation might have been too weak or not effective to evidence such an effect.

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Does antecedent complexity affect ellipsis processing? An empirical investigation

Cited by 7 publications

References 37 publications

Toward a principled Bayesian workflow in cognitive science.

Toward a principled Bayesian workflow in cognitive science.

Vowel duration and the voicing effect across English dialects

The Role of UID for the Usage of Verb Phrase Ellipsis: Psycholinguistic Evidence From Length and Context Effects

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