Gulf Arabic nouns and verbs: A standardized set of 319 object pictures and 141 action pictures, with predictors of naming latencies

Abstract

Standardized pictorial stimuli and predictors of successful picture naming are not readily available for Gulf Arabic. On the basis of data obtained from Qatari Arabic, a variety of Gulf Arabic, the present study provides norms for a set of 319 object pictures and a set of 141 action pictures. Norms were collected from healthy speakers, using a picture-naming paradigm and rating tasks. Norms for naming latencies, name agreement, visual complexity, image agreement, imageability, age of acquisition, and familiarity were established. Furthermore, the database includes other intrinsic factors, such as syllable length and phoneme length. It also includes orthographic frequency values (extracted from Aralex; Boudelaa & Marslen-Wilson, 2010). These factors were then examined for their impact on picture-naming latencies in object- and action-naming tasks. The analysis showed that the primary determinants of naming latencies in both nouns and verbs are (in descending order) image agreement, name agreement, familiarity, age of acquisition, and imageability. These results indicate no evidence that noun- and verb-naming processes in Gulf Arabic are influenced in different ways by these variables. This is the first database for Gulf Arabic, and therefore the norms collected from the present study will be of paramount importance for researchers and clinicians working with speakers of this variety of Arabic. Due to the similarity of the Arabic varieties spoken in the Gulf, these different varieties are grouped together under the label "Gulf Arabic" in the literature. The normative databases and the standardized pictures from this study can be downloaded from http://qufaculty.qu.edu.qa/tariq-khwaileh/download-center/ .

Imageability, familiarity, and age of acquisition ratings for Arabic abstract nouns, abstract verbs and adjectives

“…It is reported that words with high familiarity are processed faster than their counterparts (Boukadi et al, 2016;Akinina et al, 2014;Barca, Burani, & Arduino, 2002). This variable has been found to influence lexical retrieval as reported in Levantine Arabic (Khwaileh et al, 2014), Gulf Arabic (Khwaileh et al 2018) and Tunisian Arabic (Boukadi et al, 2016). Familiarity can be measured through a five-point rating scale ("1" indicating very unfamiliar; "5" indicating very familiar).…”

“…According to Khwaileh et al (2018), word frequency can be measured through extracting frequency values from corpora, such as in Khwaileh et al (2018), or through rating tasks as done by Boukadi, Zouaidi & Wilson (2016), where participants rated the frequency of 348 words using a seven-point scale, "1" indicating the word is never encountered and "7" indicating that it is encountered several times a day. Khwaileh et al (2018) define the 'age of acquisition' as the age at which a given word is learned. They further state that words learned at an earlier age are processed faster and more accurately than later acquired ones (e.g.…”

“…Khwaileh et al (2014;2018) define 'word length' as the number of syllables or phonemes in a given word. They also state that long words are more challenging to process in language production tasks than short words (Khwaileh et al 2018).…”

“…the Levantine-Arabic database (Khwaileh, Body, & Herbert, 2014) and the Tunisian-Arabic database (Boukadi, Zouaidi, & Wilson, 2016). A third database includes concrete nouns and concrete verbs for Gulf Arabic (Khwaileh, Mustafawi, Herbert, & Howard, 2018).…”

“…Second, most previously published normative databases are based on English and Indo-European languages that are either lexically, typologically or structurally related e.g. Dutch (Shao, Roelofs, & Meyer, 2014), Portuguese (Cãmeirao & Vicente, 2010), Spanish (Alonso, Fernandez, & Díez, 2015), Russian (Akinina, Malyutina, Ivanova, Iskra, Mannova, Dragoy, 2014), French (Bonin, Peereman, Malardier, Mèot, & Chalard, 2003;Bonin, Mèot, Chalard, & Fayol, 2002), Italian (Barca, Burani, & Arduino, 2002), or on other languages such as Turkish (Raman, Raman, & Mertan, 2014).There are three published normative datasets for Arabic: the Levantine-Arabic database (Khwaileh et al,, 2014), the Gulf Arabic nouns and verbs (Khwaileh et al, 2018) and the Tunisian-Arabic database (Boukadi, Zouaidi, & Wilson, 2016).…”