Syllabication For Agreement

Dimitropoulou, M., Duabeitia, J. A., Blitsas, P., Carreiras, M. (2009). A standardized set of 260 images for neo-Chinese Greek: standards for name agreement, age of acquisition and visual complexity. Behavior Research Methods, 41, 584-589. doi:10.3758/BRM.41.2.584 Compared to English, Latin is an example of a strongly arrowed language. The consequences of an agreement are therefore: Alario, F.-X., Ferrand, L. (1999). A set of 400 standardized images for French: standards for name agreement, image convention, familiarity, visual complexity, image variability and age of capture. Behavior Research Methods, Instruments, Computers, 31, 531-552. doi:10.3758/BF03200732 Description above of the Wikipedia article “Accord,” licensed CC-BY-SA, full list of contributors on Wikipedia. After Cortese and Schock (2013) and Yap and Balota (2009), the predictors were grouped together. They were then entered into three different stages in the regression models.

Stage 1 included 16 dichotomous variables encoding for the characteristics of the original phonemes in TA: vocal, alveolar, postal, pharyngé, palatal, dental, trill, glottal, uvular, pharyngeized, labiodental, approximately, plosiv, velar, bilabial and nasal. This was done to avoid possible bias in relation to the triggering of the voice key and the joint differences between the different initial phonemes (Cortese – Choc, 2013). Stage 2 included all of the newly obtained variables for TA, with the exception of one (four out of five): word length in phonemes, subjective frequency, H statistics for name tuning, familiarity and imagery. Step 3 included each of the five key variables separately. We did this to examine the specific contribution of each key variable beyond that of other variables. So we made five regression models, one for each key variable included in Stage 3. The influence of the above variables on treatment in Arabic has been the subject of a minor or no study. That`s why, in TA, we performed a reading-aloud task with the stimuli of the database and through multiple regression analyses. As with studies in other languages, we found that the best predictors for reading in Arabic were the length and frequency of words (z.B Balota et al., 2004; Yap – Balota, 2009).