The Understanding of Scalar Implicatures in Children With Autism Spectrum Disorder: Dichotomized Responses to Violations of Informativeness.
TL;DR: Thanks to a fine-grained measure such as the ternary judgment task, this study highlighted a neglected aspect of the pragmatic profile of ASD, whose struggle with social communication seems to affect also the domain of informativeness.
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Abstract: This study investigated the understanding of underinformative sentences like “Some elephants have trunks” by children with autism spectrum disorder (ASD) The scalar term ‘some’ can be interpreted pragmatically, ‘Not all elephants have trunks’, or logically, ‘Some and possibly all elephants have trunks’ Literature indicates that adults with ASD show no real difficulty in interpreting scalar implicatures, ie, they often interpret them pragmatically, as controls do This contrasts with the traditional claim of difficulties of people with ASD in other pragmatic domains, and is more in line with the idea that pragmatic problems are not universal The aim of this study was to: a) gain insight in the ability of children with ASD to derive scalar implicatures, and b) do this by assessing not only sensitivity to underinformativeness, but also different degrees of tolerance to violations of informativeness We employed a classic statement-evaluation task, presenting optimal, logical false, and underinformative utterances In Experiment 1, children had to express their judgment on a binary option ‘I agree’ vs ‘I disagree’ In Experiment 2, a ternary middle answer option ‘I agree a bit’ was also available Sixty-six Flemish-speaking 10-year-old children were tested: 22 children with ASD, an IQ-matched group, and an age-matched group In the binary judgment task, the ASD-group gave more pragmatic answers than the other groups, which was significant in the mixed effects logistic regression analysis, although not in the non-parametric analysis In the ternary judgment task, the children with ASD showed a dichotomized attitude towards the speaker’s meaning, by tending to either fully agree or fully disagree with underinformative statements, in contrast with TD children, who preferred the middle option Remarkably, the IQ-matched group exhibited the same pattern of results as the ASD group Thanks to a fine-grained measure such as the ternary judgment task, this study highlighted a neglected aspect of the pragmatic profile of ASD, whose struggle with social communication seems to affect also the domain of informativeness We discuss the implications of the dichotomized reaction towards violations of informativeness of these two groups in terms of the potential role of ASD and of cognitive and verbal abilities
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Citations
Language in autism: domains, profiles and co-occurring conditions
Jeannette Schaeffer,Muna Abd El-Raziq,Elena Castroviejo,Stephanie Durrleman,Sandrine Ferré,Ileana C. Grama,Petra Hendriks,Mikhail Kissine,Marta Manenti,Theodoros Marinis,Natalia Meir,Rama Novogrodsky,Alexandra Perovic,Francesca Panzeri,Silvia Silleresi,Nufar Sukenik,Agustín Vicente,Racha Zebib,Philippe Prévost,Laurice Tuller +19 more
TL;DR: The authors reviewed the current knowledge state on pragmatic and structural language abilities in autism and their potential relation to extralinguistic abilities and autistic traits, focusing on questions regarding autism language profiles with varying degrees of (selective) impairment and with respect to potential comorbidity of autism and language impairment.
Scalar and Ad-Hoc Pragmatic Inferences in Children: Guess Which One Is Easier.
TL;DR: It is found that four- and five-year-olds children performed better on ad-hoc than on scalar implicatures, and morphosyntactic competence was associated with success in both kinds ofimplicatures.
31
Longitudinal associations between theory of mind and metaphor understanding during middle childhood
TL;DR: The authors found that metaphor comprehension and general inferential abilities develop side by side in a mutually supportive way, and that the tendency to mentally interpret mental metaphors is a driving factor in ToM development.
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Large language models are not zero-shot communicators
TL;DR: A simple task is designed and widely used state-of-the-art models are evaluated, finding that, despite only evaluating on utterances that require a binary inference (yes or no), most perform close to random.
Ad-hoc and scalar implicatures in children with autism spectrum disorder
TL;DR: The authors found that children with ASD have difficulty with both kinds of implicatures and their performance as a group was significantly lower than the performance of their typically developing (TD) peers.
18
References
Fitting Linear Mixed-Effects Models Using lme4
TL;DR: In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.
Logic and Conversation
Siobhan Chapman
- 01 Jan 2005
TL;DR: For instance, Grice was interested in Quine's logical approach to language, although he differed from Quine over certain specific specific questions, such as the viability of the distinction between analytic and synthetic statements.
8.9K
Mixed-effects modeling with crossed random effects for subjects and items
TL;DR: In this article, the authors provide an introduction to mixed-effects models for the analysis of repeated measurement data with subjects and items as crossed random effects, and a worked-out example of how to use recent software for mixed effects modeling is provided.
8.2K
Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models
TL;DR: This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables, and introduces ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA.
3.3K