Phonological ambiguity modulates resolution of semantic ambiguity during reading: An fMRI study of Hebrew.

Bitan, Tali; Kaftory, Asaf; Meiri-Leib, Adi; Eviatar, Zohar; Peleg, Orna

doi:10.1037/neu0000357

Cited by 16 publications

(11 citation statements)

References 106 publications

(264 reference statements)

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“…Moreover, given that the acronyms we used were orthographically legal, these results highlight the role phonology plays not only in the recognition of acronyms, but also in the recognition of regular words. Such results replicate and extend previous studies in Hebrew that investigated the role phonology plays in the extraction of meaning from print by comparing the disambiguation process of two types of Hebrew homographs: homophonic homographs in which both meanings have the same sounds, and heterophonic homographs in which the two meanings have different sounds (e.g., Bitan, Kaftori, Meiri‐Leib, Eviatar, & Peleg, ; Peleg & Eviatar, , ; Peleg, Markus, & Eviatar, ). These studies have repeatedly shown that heterophonic homographs (which are phonologically ambiguous) are processed differently than homophonic homographs (which are phonologically unambiguous).…”

Section: Discussionsupporting

confidence: 86%

Phonological effects in visual word recognition: evidence from the processing of two types of Hebrew acronyms

Peleg

Norman

Bergerbest

2018

Journal Research in Reading

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HighlightsWhat is already known about this topic • The most convincing evidence for early phonological effects in visual word recognition comes from studies using the masked priming paradigm with nonwords as primes. Orna Peleg is an associate professor in The Program of Cognitive Studies of Language and its Uses, and Sagol School of Neuroscience at Tel-Aviv University. Her work focuses on the cognitive and neural processes underlying language comprehension and production. Tal Norman is a PhD student in The Program of Cognitive Studies of Language and its Uses at Tel Aviv University. Her research interests include visual word recognition, bilingualism and embodied cognition.Dafna Bergerbest is a Lecturer in Cognitive Psychology in the School of Behavioral Sciences at the Academic College of Tel Aviv-Yaffo. Her work focuses on the cognitive processes underlying comprehension of ambiguous language, conscious and unconscious processes in perception and memory and the effects of inhibitory control processes on memory and language.

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Section: Discussionsupporting

confidence: 86%

Phonological effects in visual word recognition: evidence from the processing of two types of Hebrew acronyms

Peleg

Norman

Bergerbest

2018

Journal Research in Reading

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“…For instance, developmental studies indicate that children's reading development can be predicted by their phonological abilities [87,99]. Evidence of the influence of phonology on adults' reading has also been reported [89,[100][101][102][103][104][105]. As pointed out by Perrone-Bertolotti et al [106], silent reading often involves an imagery speech component: we hear our own "inner voice" while reading.…”

Section: The Indirect Route From Orthography Via Phonology Tomentioning

confidence: 95%

The Discriminative Lexicon: A Unified Computational Model for the Lexicon and Lexical Processing in Comprehension and Production Grounded Not in (De)Composition but in Linear Discriminative Learning

et al. 2019

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The discriminative lexicon is introduced as a mathematical and computational model of the mental lexicon. This novel theory is inspired by word and paradigm morphology but operationalizes the concept of proportional analogy using the mathematics of linear algebra. It embraces the discriminative perspective on language, rejecting the idea that words’ meanings are compositional in the sense of Frege and Russell and arguing instead that the relation between form and meaning is fundamentally discriminative. The discriminative lexicon also incorporates the insight from machine learning that end-to-end modeling is much more effective than working with a cascade of models targeting individual subtasks. The computational engine at the heart of the discriminative lexicon is linear discriminative learning: simple linear networks are used for mapping form onto meaning and meaning onto form, without requiring the hierarchies of post-Bloomfieldian ‘hidden’ constructs such as phonemes, morphemes, and stems. We show that this novel model meets the criteria of accuracy (it properly recognizes words and produces words correctly), productivity (the model is remarkably successful in understanding and producing novel complex words), and predictivity (it correctly predicts a wide array of experimental phenomena in lexical processing). The discriminative lexicon does not make use of static representations that are stored in memory and that have to be accessed in comprehension and production. It replaces static representations by states of the cognitive system that arise dynamically as a consequence of external or internal stimuli. The discriminative lexicon brings together visual and auditory comprehension as well as speech production into an integrated dynamic system of coupled linear networks.

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“…Nevertheless, for the present data, no less than 73% of the pseudowords have all their FBS features present. It is only for 2.3% of the pseudowords that more than five FBS features are missing in C For visual word recognition, Baayen et al (2019) found that a dual-route setup, with a direct route straight from orthographic vectors to semantic vectors, and an indirect route going from orthographic vectors to triphone vectors and from the triphone vectors to the semantic vectors, afforded greater precision (see Wong and Chen, 1999;Perrone-Bertolotti et al, 2012;Newman et al, 2012;Jared, Ashby, Agauas, & Levy, 2016;Bitan, Kaftory, Meiri-Leib, Eviatar, & Peleg, 2017;Jared & O'Donnell, 2017;Amenta et al, 2017, for detailed discussion of dual routes in reading). This result led us to inquire whether a second, indirect route, would also enhance model performance for auditory comprehension.…”

Section: From Pseudowords' Transcriptions To Pseudowords' Formsmentioning

confidence: 99%

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning

et al. 2020

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Pseudowords have long served as key tools in psycholinguistic investigations of the lexicon. A common assumption underlying the use of pseudowords is that they are devoid of meaning: Comparing words and pseudowords may then shed light on how meaningful linguistic elements are processed differently from meaningless sound strings. However, pseudowords may in fact carry meaning. On the basis of a computational model of lexical processing, linear discriminative learning (LDL Baayen et al., Complexity, 2019, 1-39, 2019), we compute numeric vectors representing the semantics of pseudowords. We demonstrate that quantitative measures gauging the semantic neighborhoods of pseudowords predict reaction times in the Massive Auditory Lexical Decision (MALD) database (Tucker et al., 2018). We also show that the model successfully predicts the acoustic durations of pseudowords. Importantly, model predictions hinge on the hypothesis that the mechanisms underlying speech production and comprehension interact. Thus, pseudowords emerge as an outstanding tool for gauging the resonance between production and comprehension. Many pseudowords in the MALD database contain inflectional suffixes. Unlike many contemporary models, LDL captures the semantic commonalities of forms sharing inflectional exponents without using the linguistic construct of morphemes. We discuss methodological and theoretical implications for models of lexical processing and morphological theory. The results of this study, complementing those on real words reported in Baayen et al. (Complexity, 2019, 1-39, 2019), thus provide further evidence for the usefulness of LDL both as a cognitive model of the mental lexicon, and as a tool for generating new quantitative measures that are predictive for human lexical processing. Keywords Auditory pseudowords • Auditory comprehension • Speech production • Linear discriminative learning • Morphology • Computational modeling 1 We note that the term "pseudoword" used here is equivalent to and referred to as "nonword" in many published studies.

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Phonological ambiguity modulates resolution of semantic ambiguity during reading: An fMRI study of Hebrew.

Cited by 16 publications

References 106 publications

Phonological effects in visual word recognition: evidence from the processing of two types of Hebrew acronyms

Phonological effects in visual word recognition: evidence from the processing of two types of Hebrew acronyms

The Discriminative Lexicon: A Unified Computational Model for the Lexicon and Lexical Processing in Comprehension and Production Grounded Not in (De)Composition but in Linear Discriminative Learning

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning

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