The Massive Auditory Lexical Decision (MALD) database

Tucker, Benjamin V.; Brenner, Daniel; Danielson, Denise; Kelley, Matthew C.; Nenadić, Filip; Sims, Michelle

doi:10.3758/s13428-018-1056-1

Cited by 90 publications

(109 citation statements)

References 65 publications

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“…Pseudoword data was obtained from the MALD database (Tucker et al, 2018). This database provides auditory lexical decision responses to 26,793 words and 9,592 pseudowords, collected from 231 monolingual English listeners, aged 17-29.…”

Section: The Mald Datamentioning

confidence: 99%

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning

Chuang¹,

Voller²,

Shafaei-Bajestan³

et al. 2020

Preprint

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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., 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. (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.

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Section: The Mald Datamentioning

confidence: 99%

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning

Chuang¹,

Voller²,

Shafaei-Bajestan³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Another study that implemented LDL to generate the estimated semantic vectors of non-words is Chuang et al (submitted). By examining the acoustic durations and the response times of about 10 thousand auditory non-words in the Massive Auditory Lexical Decision (MALD) database (Tucker et al, 2018), they showed that the semantics of non-words influences the processing of auditory non-words to a substantial extent. Taken together, both the present study and Chuang et al (submitted) 12 See also the work by Hendrix presented at the 11 th International Conference on the Mental Lexicon in which FastText (Bojanowski, Grave, Joulin, & Mikolov, 2016) is used to generate semantic vectors for non-words and semantic neighborhood density is found to reliably predict reaction times in a lexical decision task.…”

Section: Discussionmentioning

confidence: 99%

On the semantics of non-words and their lexical category

Cassani¹,

Chuang²,

Baayen³

2019

Preprint

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THE SEMANTICS OF NON-WORDS 2 Abstract Using computational simulations, this work demonstrates that it is possible to learn a systematic relation between words' sound and their meanings. The sound-meaning relation was learned from a corpus of phonologically transcribed child-directed speech by using the Linear Discriminative Learning (LDL) framework (Baayen, Chuang, Shafaei-Bajestan, & Blevins, 2019), which implements linear mappings between words' form vectors and semantic vectors. Presented with the form vectors of 16 non-words, taken from a study on word learning (Fitneva, Christiansen, & Monaghan, 2009), the network generated the estimated semantic vectors of the non-words. As half of these non-words were created to phonologically resemble English nouns and the other half were phonologically similar to English verbs, we assessed whether the estimated semantic vectors for these non-words reflect this word category difference. In seven different simulations, Linear Discriminant Analysis (LDA) successfully discriminated between noun-like non-words and verb-like non-words, based on their semantic relation to the words in the lexicon. Furthermore, how well LDA categorized a non-word correlated well with a phonological typicality measure (i.e., the degree of its form being noun-like or verb-like) and with children's performance in an entity/action discrimination task. On the one hand, the results suggest that children can infer the implicit meaning of a word directly from its sound. On the other hand, this study shows that non-words do land in semantic space, such that children can capitalize on their semantic relations with other elements in the lexicon to decide whether a non-word is more likely to denote an entity or an action.

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“…This procedure might occasionally result in unreasonably long word length estimations (e.g., because of a long dramatic pause between words). In such cases, we used a word truncation algorithm based on mean word lengths in conversational speech 65 . A more detailed account of how this script works is available in the Supplementary Materials.…”

Section: Movie Annotationsmentioning

confidence: 99%

A ‘Naturalistic Neuroimaging Database’ for understanding the brain using ecological stimuli

Aliko

Huang

Gheorghiu

et al. 2020

Preprint

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Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of 'resting-state' data for which the functions of networks cannot be verifiably labelled. We make a ' Naturalistic Neuroimaging Database ' (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world. judgement about these stimuli, with a corresponding button response (e.g., a '2AFC' indicating whether a sound is 'ba' or 'pa').The result of relying on unnatural stimuli and tasks is that our neurobiological understanding derived from task-fMRI may not be representative of how the brain processes information. This is perhaps why fMRI test-retest reliability is low 6,7 . Indeed, more ecologically valid stimuli like movies have higher reliability than resting-or task-fMRI. This is not only because these enhance activity, decrease head movement and improve participant compliance 8,9 . Rather, natural stimuli have higher test-retest reliability mostly because they are more representative of operations the brain normally performs and provide more constraints on processing 10-15 . Resting-fMRIThere has arguably been a significant increase in our understanding of the network organization of the human brain because of the public availability of large resting-fMRI datasets, analysed with dynamic and other functional connectivity methods 16,17 . These include the INDI '1000 Functional Connectomes Project' 18 , 'Human Connectome Project' (HCP) 19 and UK Biobank 20 . Collectively, these datasets have more than 6,500 participants sitting in a scanner 'resting'. Resulting resting-state networks are said to represent the 'intrinsic' network architecture of the brain, i.e., networks that are present even in the absence of exogenous tasks. These networks are often claimed to be modular and to constrain the task-based architecture of the brain 21 .As with task-fMRI, one might ask how representative resting-state networks are given that participants are anything but at rest. They are switching between fixating on a cross-hair, trying to stay awake, visualising, trying not to think and thinking through inner speech 21,22 . Some of these are not particular...

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The Massive Auditory Lexical Decision (MALD) database

Cited by 90 publications

References 65 publications

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning

On the semantics of non-words and their lexical category

A ‘Naturalistic Neuroimaging Database’ for understanding the brain using ecological stimuli

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