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. 2024 Dec;56(8):8761-8783.
doi: 10.3758/s13428-024-02502-4. Epub 2024 Sep 9.

HeLP: The Hebrew Lexicon project

Roni Stein  1 , Ram Frost  1   2 , Noam Siegelman  3
Affiliations

HeLP: The Hebrew Lexicon project

Roni Stein et al. Behav Res Methods. 2024 Dec.

Abstract

Lexicon projects (LPs) are large-scale data resources in different languages that present behavioral results from visual word recognition tasks. Analyses using LP data in multiple languages provide evidence regarding cross-linguistic differences as well as similarities in visual word recognition. Here we present the first LP in a Semitic language-the Hebrew Lexicon Project (HeLP). HeLP assembled lexical decision (LD) responses to 10,000 Hebrew words and nonwords, and naming responses to a subset of 5000 Hebrew words. We used the large-scale HeLP data to estimate the impact of general predictors (lexicality, frequency, word length, orthographic neighborhood density), and Hebrew-specific predictors (Semitic structure, presence of clitics, phonological entropy) of visual word recognition performance. Our results revealed the typical effects of lexicality and frequency obtained in many languages, but more complex impact of word length and neighborhood density. Considering Hebrew-specific characteristics, HeLP data revealed better recognition of words with a Semitic structure than words that do not conform to it, and a drop in performance for words comprising clitics. These effects varied, however, across LD and naming tasks. Lastly, a significant inhibitory effect of phonological ambiguity was found in both naming and LD. The implications of these findings for understanding reading in a Semitic language are discussed.

Keywords: Visual word recognition; Mega studies; Reading; Cross-linguistic differences.

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Conflict of interest statement

The authors have no relevant financial or non-financial interests to disclose.

Figures

Fig. 1
Fig. 1
Properties of words and nonwords in the LD task. A Distribution of length (in letters). B Distribution of OLD20
Fig. 2
Fig. 2
Information about included HeLP stimuli and their available lexical properties
Fig. 3
Fig. 3
Distributions of the Hebrew-specific predictors. A Number of words tagged as 0-clearly non-Semitic, 1-clearly Semitic, 2-undetermined, 3-other. B Number of words with each number of clitics. C Pronunciation entropy scores
Fig. 4
Fig. 4
A Distribution of the number of sessions per participant in the LD task. B Distribution of the number of responses per word and per nonword in the LD task. C Distribution of the number of sessions per participant in the naming task. D Distribution of the number of responses per word in the naming task
Fig. 5
Fig. 5
A Distribution of RT for words and nonwords in the LD task (across trials). B Distribution of accuracy rate for words and nonwords in the LD task. C Accuracy rates by participant in the LD task. D Distribution of RT for words in the naming task (across trials). E Distribution of accuracy rate for words in the naming task. F Accuracy rates by participant in the naming task
Fig. 6
Fig. 6
Visual depiction of significant Interactions in the RT model, LD data. A Interaction between word length and log frequency. B Interaction between OLD20 and log frequency. C Interaction between OLD20 and word length
Fig. 7
Fig. 7
Visual depiction of significant Interactions in the accuracy model, LD data. A Interaction between word length and word log frequency. B Interaction between OLD20 and word log frequency. C Interaction between OLD20 and word length
Fig. 8
Fig. 8
Visual depiction of effects of interest in the RT model, naming data. A Interaction between OLD20 and word length (the only significant interaction in this model). B Estimated mean log-transformed RT for words as a function of the number of clitics
Fig. 9
Fig. 9
Visual depiction of significant Interactions in the accuracy model, naming data. A Interaction between word length and word log frequency. B Interaction between OLD20 and word length
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References

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