ABSTRACT
In two speech production studies conducted in Italian, we investigated the impact of phonological neighbourhood properties such as the neighbourhood density and the mean frequency of the neighbours on speech processing. Two populations of healthy (Study 1) and neurologically impaired (Study 2) individuals were tested. We employed multi-regression methods to analyse naming latencies in Study 1 and accuracy rates in Study 2 while controlling for various psycholinguistic predictors. In Study 1, pictures with words from high-density neighbourhoods were named faster than those from low-density neighbourhoods. Additionally, words with high-frequency neighbours were named faster in Study 1 and yielded higher accuracy rates in Study 2. The results suggest facilitatory effects of both the phonological neighbourhood density and frequency neighbourhood variables. Furthermore, we observed interactions between these two phonological neighbourhood variables and name agreement and repetition. Specifically, the facilitation effect was more pronounced for pictures with lower name agreement and during the initial presentation of the pictures. These findings are discussed in the context of previous literature and within the framework of interactive models of speech production.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Data and code for analysis are available on the following repository platform: https://osf.io/4sbc6/.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/02699206.2024.2360127.
Notes
1 In the RTs analysis, to explore whether the variables were affected by multicollinearity, we calculated the variance inflation factor (VIF) for each independent variable (Name agreement, AoA, Lexical frequency, PhonN, and PhonN_MFreq). Following the suggestion of an anonymous reviewer, word length (number of phonemes) was also included. VIF values were < 1.58 suggesting a moderate correlation and no corrective measures were adopted.
2 In further analysis, years of education and the age of the participants were included in the two regression models to explore any modulation in phonological neighbourhood effects (see for age modulations, Gordon & Kurczek, Citation2014). There was a main effect of education (p < 0.001), with faster naming latencies for participants with more years of education. The main effect of age was not significant (p = 0.331). Interactions between education and PhonN and between education and PhonN_MFreq were not significant (ps > 0.561) and overall the effects remain identical as the models without the variables education and age.
3 To explore whether the variables were affected by multicollinearity, we calculated the variance inflation factor (VIF) for each independent variable (Name agreement, AoA, Lexical frequency, PhonN, and PhonN_MFreq). Following the suggestion of an anonymous reviewer, word length (number of phonemes) was also included. VIF values were < 2.18 suggesting a moderate correlation and no corrective measures were adopted.