ords could be shortened or morphed to form other words (e.g. openness to open or opens) multilinguals performed much worse than monolinguals. The BLINCS model acknowledges lexical frequency to play a role in semantic networks, and easily morphable words have strong semantic and phonetic connections to other similar words that can be easily activated. Monolinguals, on the other hand, were not as sensitive to these words, and it can be explained as having enough linguistic experience to classify similar words differently. Future research that involves predicting how well perceived a sentence will be in noise for monolinguals and multilinguals should systematically measure word frequency, sentence predictability and morphable words in their framework, as this study shows they take prominence, alongside the effects of contextual meaning and linguistic load. Research should also continue to investigate if Native English Speakers proficient in other languages perform as well as a monolingual in noise and if performance in multilinguals increases the more languages they are proficient in. Factors such as language learning and listening strategies could be an underlying cause for increases in performance in both cases, especially in polyglots (Cohen et al., 2007). Main 41 limitations of this paper include low power in the statistical tests due to the separation of 8 different conditions for a small sample size; and typing speed possibly playing a role in how well written answers were. Despite this however, this study has concluded word frequency, sentence predictability and morphable words to be important measures to predict the perception of sentences in noise for both monolinguals and multilinguals, with linguistic load and context playing important secondary roles, especially in multilinguals, and the length of sentences having no clear effect on speech perception in noise. 42 References Bahrick, H. P., Hall, L. K., Goggin, J. P., Bahrick, L. E., & Berger, S. A. (1994). Fifty years of language maintenance and language