The Auditory English Lexicon Project: A multi-talker, multi-region psycholinguistic database of 10,170 spoken words and nonwords

Goh, Winston D.; Yap, Melvin J.; Chee, Qian Wen

doi:10.3758/s13428-020-01352-0

Cited by 32 publications

(56 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lexi-CAL provides options for users to calculate surface, orthographic, phonological, and phonographic metrics, using inbuilt algorithms. These algorithms were also used to compute the metrics in the AELP [5]. The range of metrics that LexiCAL can compute have been shown to be theoretically important predictors of both visual and spoken word recognition [5,19,20].…”

Section: Lexicalmentioning

confidence: 99%

“…These algorithms were also used to compute the metrics in the AELP [5]. The range of metrics that LexiCAL can compute have been shown to be theoretically important predictors of both visual and spoken word recognition [5,19,20]. Importantly, LexiCAL offers users the flexibility of performing calculations for any user-specified input, with reference to any user-specified corpus and phonetic system.…”

Section: Lexicalmentioning

confidence: 99%

“…In psycholinguistic research, large databases of words, such as the English Lexicon Project (ELP [1]), the CELEX lexical database [2], the Hoosier Mental Lexicon (HML [3]), the MRC psycholinguistic database [4], and the Auditory English Lexicon Project (AELP [5]), have been essential resources for researchers who require data for a multitude of lexical characteristics. These databases typically consist of a comprehensive set of words and their corresponding lexical properties (e.g., word length, number of syllables, pronunciation, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LexiCAL: A calculator for lexical variables

et al. 2021

Self Cite

View full text Add to dashboard Cite

While a number of tools have been developed for researchers to compute the lexical characteristics of words, extant resources are limited in their useability and functionality. Specifically, some tools require users to have some prior knowledge of some aspects of the applications, and not all tools allow users to specify their own corpora. Additionally, current tools are also limited in terms of the range of metrics that they can compute. To address these methodological gaps, this article introduces LexiCAL, a fast, simple, and intuitive calculator for lexical variables. Specifically, LexiCAL is a standalone executable that provides options for users to calculate a range of theoretically influential surface, orthographic, phonological, and phonographic metrics for any alphabetic language, using any user-specified input, corpus file, and phonetic system. LexiCAL also comes with a set of well-documented Python scripts for each metric, that can be reproduced and/or modified for other research purposes.

show abstract

Section: Lexicalmentioning

confidence: 99%

Section: Lexicalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LexiCAL: A calculator for lexical variables

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Second, we examined whether or not the effects of the first analysis would generalize to other large databases. Specifically, we investigated visual and auditory lexical decision times from five databases: ELP, British Lexicon Project (i.e., BLP; Keuleers et al, 2012) and English Crowdsourcing Project (i.e., ECP; Mandera et al, 2019) for visual lexical decision; and MALD and Auditory English Lexicon Project (i.e., AELP; Goh et al, 2020) for auditory lexical decision.…”

Section: Affective Valence Of Words Differentially Affects Visual and Auditory Word Recognitionmentioning

confidence: 99%

“…Mean auditory lexical decision time were retrieved from MALD (Tucker et al, 2019). For Analysis 2 and 3, mean visual lexical decision times were retrieved from ELP (Balota et al, 2007), ECP (Mandera et al, 2019) and BLP (Keuleers et al, 2012); mean auditory lexical decision times were retrieved from MALD (Tucker et al, 2019) and AELP (Goh et al, 2020).…”

Section: Dependent Variablesmentioning

confidence: 99%

Affective Valence of Words Differentially Affects Visual and Auditory Word Recognition

Gao¹,

Shinkareva²,

Peelen³

2021

Preprint

View full text Add to dashboard Cite

Recognizing written or spoken words involves a sequence of processing stages, transforming sensory features into lexical-semantic representations. While the later processing stages are common across modalities, the initial stages are modality-specific. In the visual modality, previous studies have shown that words with positive valence are recognized faster than neutral words. Here, we examined whether the effects of valence on word recognition are specific to the visual modality or are common across visual and auditory modalities. To address this question, we analyzed multiple large databases of visual and auditory lexical decision tasks, relating the valence of words to lexical decision times while controlling for a large number of variables, including arousal and frequency. We found that valence differentially influenced visual and auditory word recognition. Valence had an asymmetric effect on visual lexical decision times, primarily speeding up recognition of positive words. By contrast, valence had a symmetric effect on auditory lexical decision times, with both negative and positive words speeding up word recognition relative to neutral words. The modality-specificity of valence effects were consistent across databases and were observed when the same set of words were compared across modalities. We interpret these findings as indicating that valence influences word recognition partly at the sensory-perceptual stage. We relate these effects to the effects of positive (reward) and negative (punishment) reinforcers on perception.

show abstract