We recently reported strong, replicable (i.e., replicated) evidence for lexically mediated compensation for coarticulation (LCfC; Luthra et al., 2021), whereby lexical knowledge influences a prelexical process. Critically, evidence for LCfC provides robust support for interactive models of cognition that include top-down feedback and is inconsistent with autonomous models that allow only feedforward processing. McQueen, Jesse, and Mitterer (2023) offer five counter-arguments against our interpretation; we respond to each of those arguments here and conclude that top-down feedback provides the most parsimonious explanation of extant data.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11228965 | PMC |
| http://dx.doi.org/10.1111/cogs.13449 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Visual attention matters during word recognition: A Bayesian modeling approach.
Psychon Bull Rev
January 2025
Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LPNC, 38000, Grenoble, France.
It is striking that visual attention, the process by which attentional resources are allocated in the visual field so as to locally enhance visual perception, is a pervasive component of models of eye movements in reading, but is seldom considered in models of isolated word recognition. We describe BRAID, a new Bayesian word-Recognition model with Attention, Interference and Dynamics. As most of its predecessors, BRAID incorporates three sensory, perceptual, and orthographic knowledge layers together with a lexical membership submodel.
View Article and Find Full Text PDFAcquisition of Speech Prosody in a Non-native Tone Language by Children With and Without Autism Spectrum Disorder.
J Autism Dev Disord
January 2025
The First Hospital of Jinan University, Guangzhou, China.
Purpose: Children with autism spectrum disorder (ASD) often show abnormal speech prosody. Tonal languages can pose more difficulties as speakers need to use acoustic cues to make lexical contrasts while encoding the focal function, but the acquisition of speech prosody of non-native languages, especially tonal languages has rarely been investigated.
Methods: This study aims to fill in the aforementioned gap by studying prosodic focus-marking in Mandarin by native Cantonese-speaking children with ASD (n = 25), in comparison with their typically developing (TD) peers (n = 20) and native Mandarin-speaking children (n = 20).
Simulating Early Phonetic and Word Learning Without Linguistic Categories.
Dev Sci
March 2025
Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Études Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France.
Before they even talk, infants become sensitive to the speech sounds of their native language and recognize the auditory form of an increasing number of words. Traditionally, these early perceptual changes are attributed to an emerging knowledge of linguistic categories such as phonemes or words. However, there is growing skepticism surrounding this interpretation due to limited evidence of category knowledge in infants.
View Article and Find Full Text PDFMoving beyond word frequency based on tally counting: AI-generated familiarity estimates of words and phrases are an interesting additional index of language knowledge.
Behav Res Methods
December 2024
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense, 30, 28040, Madrid, Spain.
This study investigates the potential of large language models (LLMs) to estimate the familiarity of words and multi-word expressions (MWEs). We validated LLM estimates for isolated words using existing human familiarity ratings and found strong correlations. LLM familiarity estimates performed even better in predicting lexical decision and naming performance in megastudies than the best available word frequency measures.
View Article and Find Full Text PDFThe Italian Crowdsourcing Project: Visual word recognition times for 130,495 Italian words.
Behav Res Methods
December 2024
Department of Psychology, University of Milano-Bicocca, P.zza dell'Ateneo Nuovo, 1, 20126, Milano, Italy.
Despite being largely spoken and studied by language and cognitive scientists, Italian lacks large resources of language processing data. The Italian Crowdsourcing Project (ICP) is a dataset of word recognition times and accuracy including responses to 130,465 words, which makes it the largest dataset of its kind item-wise. The data were collected in an online word knowledge task in which over 156,000 native speakers of Italian took part.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!