More Like this

1. The Influence of Prior Semantic Knowledge in Noisy Channel Interpretation

   https://doi.org/10.1177/17470218251383526  

   Chen, Sihan; Washington, Lia; Gibson, Edward (September 2025, Quarterly Journal of Experimental Psychology)

   How do comprehenders interpret semantically implausible sentences? Previous studies proposed a noisy-channel framework of sentence comprehension, where communication between a speaker and a comprehender happens in a noisy channel. The comprehender rationally adopts an interpretation of a sentence based on how likely the interpretation is (the semantic prior) and how likely is the interpretation corrupted into the perceived sentence because of noise (the likelihood). The theory predicted that comprehenders would be more likely to adopt a literal interpretation of an implausible sentence if their prior of implausible sentences were higher. To test this hypothesis, Gibson et al. manipulated the proportion of implausible test sentences in two sets of experiments, where participants read a number of sentences and answer a comprehension question following each sentence. Although their results supported the hypothesis, the experiment could be confounded (a) by participants’ adaptation effect (due to different experiment lengths) and (b) by different participants having different strategies to do the task (due to the between-subject design). In our study, we manipulated the semantic prior and controlled for these potential confounds. We found participants exposed to more implausible sentences were indeed more likely to interpret implausible sentences literally. Our results hence offer additional support for the noisy-channel framework. 

   more » « less
2. Noisy-channel language comprehension in aphasia: A Bayesian mixture modeling approach

   https://doi.org/10.3758/s13423-025-02639-z  

   Ryskin, Rachel; Gibson, Edward; Kiran, Swathi (August 2025, Psychonomic Bulletin & Review)

   Abstract Individuals with “agrammatic” receptive aphasia have long been known to rely on semantic plausibility rather than syntactic cues when interpreting sentences. In contrast to early interpretations of this pattern as indicative of a deficit in syntactic knowledge, a recent proposal views agrammatic comprehension as a case of “noisy-channel” language processing with an increased expectation of noise in the input relative to healthy adults. Here, we investigate the nature of the noise model in aphasia and whether it is adapted to the statistics of the environment. We first replicate findings that a) healthy adults (N= 40) make inferences about the intended meaning of a sentence by weighing the prior probability of an intended sentence against the likelihood of a noise corruption and b) their estimate of the probability of noise increases when there are more errors in the input (manipulated via exposure sentences). We then extend prior findings that adults with chronic post-stroke aphasia (N= 28) and healthy age-matched adults (N= 19) similarly engage in noisy-channel inference during comprehension. We use a hierarchical latent mixture modeling approach to account for the fact that rates of guessing are likely to differ between healthy controls and individuals with aphasia and capture individual differences in the tendency to make inferences. We show that individuals with aphasia are more likely than healthy controls to draw noisy-channel inferences when interpreting semantically implausible sentences, even when group differences in the tendency to guess are accounted for. While healthy adults rapidly adapt their inference rates to an increase in noise in their input, whether individuals with aphasia do the same remains equivocal. Further investigation of comprehension through a noisy-channel lens holds promise for a parsimonious understanding of language processing in aphasia and may suggest potential avenues for treatment. 

   more » « less
3. Understanding Cross-lingual Pragmatic Misunderstandings in Email Communication

   https://doi.org/10.1145/3512976  

   Lim, Hajin; Cosley, Dan; Fussell, Susan R. (January 2022, Proceedings of the ACM on humancomputer interaction)

   Communication tools such as email facilitate communication and collaboration between speakers of different languages, who use two primary strategies—English as a common language and machine translation (MT) tools—to help them overcome language barriers. However, each of these communication strategies creates its own challenges for cross-lingual communication. In this paper, we compare how people’s interpretations of an email sender’s social intention, and their evaluation of the email and the senders, differ when using a common language versus MT in email communication. We conducted an online experiment in which monolingual native English speakers read and rated request emails written by native English speakers, emails written by bilingual Chinese speakers in English, and emails written in Chinese then machine-translated into English. We found that participants interpreted the social intentions of the email sender less accurately for machine-translated emails than for emails written by non-native speakers in English. Participants also rated the senders and emails less positively overall for machine-translated emails compared to emails written by non-native speakers in English. Based on these findings, we suggest design possibilities that could better aid multilingual communication. 

   more » « less
4. ERP sensitivity to subcategorization violations in L2 learners

   https://doi.org/10.1177/02676583231199426  

   Liao, Chia-Hsuan; Lau, Ellen (October 2023, Second Language Research)

   Event concepts of common verbs (e.g. eat, sleep) can be broadly shared across languages, but a given language’s rules for subcategorization are largely arbitrary and vary substantially across languages. When subcategorization information does not match between first language (L1) and second language (L2), how does this mismatch impact L2 speakers in real time? We hypothesized that subcategorization knowledge in L1 is particularly difficult for L2 speakers to override online. Event-related potential (ERP) responses were recorded from English sentences that include verbs that were ambitransitive in Mandarin but intransitive in English (*  My sister listened the music). While L1 English speakers showed a prominent P600 effect to subcategorization violations, L2 English speakers whose L1 was Mandarin showed some sensitivity in offline responses but not in ERPs. This suggests that computing verb–argument relations, although seemingly one of the basic components of sentence comprehension, in fact requires accessing lexical syntax which may be vulnerable to L1 interference in L2. However, our exploratory analysis showed that more native-like behavioral accuracy was associated with a more native-like P600 effect, suggesting that, with enough experience, L2 speakers can ultimately overcome this interference. 

   more » « less
5. Native speakers and learners of Mandarin predict upcoming arguments in dative constructions based on categorical and gradient verb constraints

   https://doi.org/10.1017/S1366728924000749  

   Zhu, Yanxin Alice; Grüter, Theres (December 2024, Bilingualism: Language and Cognition)

   Abstract This study investigated the predictive use of dative verb constraints in Mandarin among home-country-raised native speakers and classroom learners (including both sequential L2 learners and heritage speakers). In a visual world eye-tracking experiment, participants made anticipatory looks to the upcoming argument (recipient versus theme) following categorical restrictions of non-alternating verbs and gradient bias of alternating verbs before the acoustic onset of the disambiguating noun. Crucially, no delay or reduction in the prediction effects was observed among L2 learners and heritage speakers in comparison with home-country-raised native speakers. Mandarin proficiency and dominant language (English versus other) did not modulate prediction effects among classroom learners. These findings provide direct support for the assumption of error-driven learning accounts of the dative alternation, that is, language users actively predict upcoming arguments based on verb information during real-time sentence processing. 

   more » « less