Alternatives and Inferences in the Communication of Meaning

Rohde, Hannah; Kurumada, Chigusa

doi:10.1016/bs.plm.2018.08.012

Cited by 5 publications

(4 citation statements)

References 141 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that (2) is not specifically a model of language production; it is simply Bayes' Rule, a theorem that establishes that the conditional probability on the left is proportional to the combination of the prior and likelihood on the right (for other applications in linguistics, see Kehler and Rohde, 2013;Rohde and Kurumada, 2018;Franke and Jäger, 2016). If predictive language processing depends in part on comprehenders' ability to reverse engineer what utterance a speaker is likely to be producing, casting the interpretation problem as in (2) highlights the two factors which determine the probability that a speaker is talking about a particular object.…”

Section: Modelling Conceptual Probability and Speaker Informativitymentioning

confidence: 99%

Color interpretation is guided by informativity expectations, not by world knowledge about colors

Rohde¹,

Rubio-Fernández²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

When people hear words for objects with prototypical colors (e.g., ‘banana’), they look at objects of the same color (e.g., lemon). However, the experimental record also shows that when people speak, they tend to omit prototypical colors, using color adjectives when it is informative (e.g., when referring to clothes). These findings yield an interesting prediction, which we tested here: People may look at yellow objects when hearing ‘banana’, but they should look away from bananas when hearing ‘yellow’. The results of an online sentence-completion task (N=100) and an online eye-tracking task (N=41) confirmed that when presented with truncated color descriptions (e.g., ‘Click on the yellow...’), people anticipate clothing items rather than stereotypical fruits. A corpus analysis ruled out the possibility that this association between color and clothing arises from simple co-occurrence statistics. We conclude that comprehenders make linguistic predictions based not only on what they know about the world (e.g., which objects are yellow) but also on what speakers tend to say about the world (i.e., their informativity expectations).

show abstract

Section: Modelling Conceptual Probability and Speaker Informativitymentioning

confidence: 99%

Color interpretation is guided by informativity expectations, not by world knowledge about colors

Rohde¹,

Rubio-Fernández²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…More generally, relatively little is known about the extent to which listeners take into account alternative causes when interpreting linguistic input. Some studies have found similar effects on other aspects of language understanding-in particular, alternative causes presented in explicit instructions (e.g., Arnold et al, 2007;Dix et al, 2018;Grodner & Sedivy, 2011;Kurumada, Brown, Bibyk, & Tanenhaus, 2018, as summarized in Rohde & Kurumada, 2018). For example, listeners tend to anticipate unfamiliar objects as referents after a speech disfluency ("Click on [pause] thee uh red .…”

mentioning

confidence: 95%

Talker-specific pronunciation or speech error? Discounting (or not) atypical pronunciations during speech perception.

Liu¹,

Jaeger²

2019

Journal of Experimental Psychology: Human Perception and Perfor

View full text Add to dashboard Cite

Perceptual recalibration allows listeners to adapt to talker-specific pronunciations, such as atypical realizations of specific sounds. Such recalibration can facilitate robust speech recognition. However, indiscriminate recalibration following any atypically pronounced words also risks interpreting pronunciations as characteristic of a talker that are in reality because of incidental, short-lived factors (such as a speech error). We investigate whether the mechanisms underlying perceptual recalibration involve inferences about the causes for unexpected pronunciations. In 5 experiments, we ask whether perceptual recalibration is blocked if the atypical pronunciations of an unfamiliar talker can also be attributed to other incidental causes. We investigated 3 type of incidental causes for atypical pronunciations: the talker is intoxicated, the talker speaks unusually fast, or the atypical pronunciations occur only in the context of tongue twisters. In all 5 experiments, we find robust evidence for perceptual recalibration, but little evidence that the presence of incidental causes block perceptual recalibration. We discuss these results in light of other recent findings that incidental causes can block perceptual recalibration.

show abstract

“…Societal clinical reporting guidelines can synchronize the expected information content to be communicated between physicians for given types of investigations [103]. This allows the reader of a report to derive interpretations beyond what can be inferred from words alone by leveraging diverse pragmatic knowledge [104]. Thus, although various language complexities are common in medical reports (e.g., lexical and referential ambiguity, ellipsis, and punctuation ambiguity), they can often be mentally corrected from within this overarching expectation model [105].…”

Section: Comparing the Problem Circumstances Of General Versus Medica...mentioning

confidence: 99%

Design considerations for a hierarchical semantic compositional framework for medical natural language understanding

2023

View full text Add to dashboard Cite

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers on a hierarchical semantic compositional model (HSCM), which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects: semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework.

show abstract

Alternatives and Inferences in the Communication of Meaning

Cited by 5 publications

References 141 publications

Color interpretation is guided by informativity expectations, not by world knowledge about colors

Color interpretation is guided by informativity expectations, not by world knowledge about colors

Talker-specific pronunciation or speech error? Discounting (or not) atypical pronunciations during speech perception.

Design considerations for a hierarchical semantic compositional framework for medical natural language understanding

Contact Info

Product

Resources

About