Graded strength of comparative illusions is explained by Bayesian inference

TL;DR

This paper models the comparative illusion in language processing using Bayesian inference, demonstrating that the strength of the illusion can be quantitatively predicted by a noisy-channel model that accounts for human judgments and specific linguistic effects.

Contribution

It introduces a novel synthesis of statistical language models with behavioral data to predict the gradation of comparative illusions, extending prior work with a comprehensive quantitative model.

Findings

The model accurately predicts the strength of the comparative illusion.

It explains effects related to pronominal vs. full noun phrase subjects.

Supports noisy-channel theory as a unified explanation for language processing phenomena.

Abstract

Like visual processing, language processing is susceptible to illusions in which people systematically misperceive stimuli. In one such case--the comparative illusion (CI), e.g., More students have been to Russia than I have--comprehenders tend to judge the sentence as acceptable despite its underlying nonsensical comparison. Prior research has argued that this phenomenon can be explained as Bayesian inference over a noisy channel: the posterior probability of an interpretation of a sentence is proportional to both the prior probability of that interpretation and the likelihood of corruption into the observed (CI) sentence. Initial behavioral work has supported this claim by evaluating a narrow set of alternative interpretations of CI sentences and showing that comprehenders favor interpretations that are more likely to have been corrupted into the illusory sentence. In this study, we replicate and go substantially beyond this earlier work by directly predicting the strength of illusion with a quantitative model of the posterior probability of plausible interpretations, which we derive through a novel synthesis of statistical language models with human behavioral data. Our model explains not only the fine gradations in the strength of CI effects, but also a previously unexplained effect caused by pronominal vs. full noun phrase than-clause subjects. These findings support a noisy-channel theory of sentence comprehension by demonstrating that the theory makes novel predictions about the comparative illusion that bear out empirically. This outcome joins related evidence of noisy channel processing in both illusory and non-illusory contexts to support noisy channel inference as a unified computational-level theory of diverse language processing phenomena.