Under review

  • Lev-Ari, S. & McKay, R. (under review) The Sound of Swearing: Evidence for Universal Patterns in Profanity
  • Lev-Ari, S. (under review) Larger communities create more granular and better
    structured categories


  • Boduch-Grabka, K. & Lev-Ari, S. (2021) Exposing individuals to foreign accent increases their trust in what non-native speakers say. Cognitive Science, 45,11, e13064TLDR
    People believe information less when it is delivered in a foreign accent even if they are not prejudiced. They doubt it because it is harder to process foreign-accented speech and people implicitly infer form the difficulty that the information is wrong. This bias against believing non-native speakers, however, can be reduced by exposing listeners to foreign-accented speech, because exposure trains listeners to become better at processing foreign-accented speech
  • Lev-Ari, S., Kancheva, I., Marston, L., Morris, H. & Zaynudinova, M. (2021) ‘Big’ sounds bigger in more widely-spoken languages. Cognitive Science, 45, 11, e13059.TLDR
    Languages that are spoken by larger communities (i.e., millions of speakers) are more sound symbolic than languages spoken by smaller communities (e.g., thousands of speakers). We demonstrate it by showing that people are better at guessing the meaning of words in unfamiliar languages if those languages are spoken by large rather than smaller communities. The reason for that is that communication is harder in larger communities, so the languages of large communities adapt and overcome the greater challenges by relying on sound symbolism, that is, having words that sound like what they mean.
  • Lev-Ari (2021) Richer color vocabulary is correlated with color memory, but its relation to perception is unknown Proceedings of the National Academy of Science, 118, 10, e2024682118. TLDR
    A commentary on Hasantash and Afraz (2020) explaining that their experiment cannot determine whether language can influence perception because of ceiling performance. It also explains that the correlation between color memory and color vocabulary does not show an influence of langauge on memory as it is likely that the causality is in the opposite direction – better color memory leads to using more color labels.
  • Lev-Ari, S., Haidari, B., Sayer, T., Au, V., & Nazihah, F. (2021) Noticing how our social networks are interconnected can influence language change Language, Cognition & Neuroscience, 36, 1, 119-134. TLDR
    Real world social networks are interconnected, so information we receive from several people might originate from the same person. We show that people tend to neglect to consider this interconnectivity when learning information, and even more so when the information is linguistic (how to call something) compared to opinions. We also show that this neglect facilitates the spread of language and trends.


  • Raviv, L., Meyer, A.S. & Lev-Ari, S. (2020) The role of social network
    structure in the emergence of linguistic structure
    Cognitive Science, 44, 8, e12876. TLDR
    We compared the emergence of linguistic structure in fully connected, small‐world, and scale‐free communities. We did not find an effecet of community structure on any of our measures (systematicity, stability, convergence, and communicative success), but small world communities consistently showed greater variation in their behavior and scale-free networks showed the least variation in their performace. These results suggest that network structure might influence vulnerability to drift.
  • Lev-Ari, S. & Sebanz, N. (2020) Interacting with multiple partners improves communication skills Cognitive Science, 44, 4, e12836.TLDR
    We show that talking to more people improves communication skills even when those we talk to are passive listeners who don’t talk back. We further show that this might be because talking to multiple people increases the tendency to take perspective.
  • Iacozza, S. , Meyer, A.S. & Lev-Ari, S. (2020) How in-group bias influences the level of detail of speaker-specific information encoded in novel lexical representations Journal of Experimental Psychology: Learning, Memory, and Cognition 46, 5, 894–906.TLDR
    We process and represent the speech of ingorup and outgroup members differently. The greater individuals’ implicit ingroup bias, the more they store speech from ingroup speakers in more detail than speech from outgroup speakers. We show this by looking at participants’ source memory for novel words they learn from (speakers presented as) ingroup and outgroup members.
  • Lev-Ari, S. (2020) Communities of different size create different categorization systems. In Ravignani, A. and Barbieri, C. and Martins, M. and Flaherty, M. and Jadoul, Y. and Lattenkamp, E. and Little, H. and Mudd, K. and Verhoef, T. (Eds.) The Evolution of Language: Proceedings of the 13th International Conference (EvoLang13), Brussels, Belgium.
  • Lev-Ari, S. (2020). The influence of social network properties on language processing and use. In M. S. Vitevitch (Ed.), Network Science in Cognitive Psychology. New York, NY: Routledge.


  • Lev-Ari, S., Dodsworth, R., Mielke, J. & Peperkamp, S. (2019) The different roles
    of expectations in phonetic and lexical processing
    . In Proceedings of the 20th Annual Conference of the international Speech Communication Association (INTERSPEECH)TLDR
    Previous research investigates how the expectations listeners have of speakers influence language processing using either a lexical or a phonetic task, and the two are treated as interchangeable. We show that the two are not equivalent and have different underlying mechanisms that are sensitive to different individual differences. Performance on the phonetic task was sensitive to working memory whereas performance on the lexical task was sensitive to implicit bias.
  • Iacozza, S. , Meyer, A.S. & Lev-Ari, S. (2019) How in-group bias source memory for words learned from in-group and out-group speakers Frontiers in Human Neuroscience 13:308 TLDR
    We teach participants novel words from speakers from their own university (in-group) and another university (out-group). We show that people spontaneously encode speakers’ in/out-group status, and that the greater participants’ (implicit) in-group bias, the more hesitant they are about attributing learned words to speakers, especially in-group speakers.
  • Raviv, L., Meyer, A.S., & Lev-Ari, S. (2019) Larger communities create more systematic languagesProceedings of The Royal Society B 286, 201912 TLDR
    By having small and large groups play a communication game in the lab using only words that they invent we find that larger communities create more structured languages, suggesting that cross-linguistic differences in grammatical and morphological complexity could be (partially) accounted for by community size. We also find that this effect is driven by the greater input variability.
  • Raviv, L., Meyer, A.S., & Lev-Ari, S. (2019) Compositional structure can emerge without generational transmissionCognition, 182, 151-164. TLDR
    An experimental study showing that structure can emerge within a language during the first generation of users, and that one of the main pressures for its emergence is having multiple interaction partners.


  • Lev-Ari, S., Ho, E., & Keysar, B. (2018) The unforeseen consequences of interacting with non-native speakers. Topics in Cognitive Science, 10, 835-849. doi:10.1111/tops.12325. TLDR
    Because people process information in less detail when they interact with non-native speakers, they remember less well what they themselves said in those interactions.
  • Lev-Ari, S. (2018) The influence of social network size on speech perception. Quarterly Journal of Experimental Psychology, 71, 10, 2249-2260.  doi:10.1177/1747021817739865 TLDR
    People who regularly interact with more people are better at speech perception and this is driven by the greater variability in the speech input they receive.
  • Raviv, L., Meyer, A., & Lev-Ari, S. (2018). The role of community size in the emergence of linguistic structure. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 12th International Conference (EVOLANGXII). Toruń, Poland: NCU Press, pp. 402-404. doi:10.12775/3991-1.096


  • Lev-Ari, S., & Peperkamp, S. (2017). Language for $200: Success in the environment influences grammatical alignment. Journal of Language Evolution,2(2), 177-187. doi:10.1093/jole/lzw012. TLDR
    The less successful people are (in a non-linguistic domain), the more they attend to and learn from the environment, including the grammatical patterns in it. We show this using a corpus analysis of the gameshow Jeopardy and a Go Fish experiment in the lab. The results suggest that the spread of linguistic changes might accelerate in times of crisis.
  • Lev-Ari, S., & Shao, Z. (2017). How social network heterogeneity facilitates lexical access and lexical prediction. Memory & Cognition, 45(3), 528-538. doi:10.3758/s13421-016-0675-y. TLDR
    Individuals with social networks that are more varied age-wise show better lexical prediction and are faster to name difficult-to-name items. This might be because they have less entropy in the input they receive from their environment.
  • Lev-Ari, S., van Heugten, M., & Peperkamp, S. (2017). Relative difficulty of understanding foreign accents as a marker of proficiency. Cognitive Science,41(4), 1106-1118. doi:10.1111/cogs.12394. TLDR
    The study shows that as the linguistic competence of L2 learners increases, so does their relative difficulty of understanding foreign-accented speech. In other words, finding foreign-accented speech relatively difficult to understand is a marker of acquiring more accurate phonological representations.


  • Lev-Ari, S. (2016). How the size of our social network influences our semantic skills. Cognitive Science, 40, 2050-2064. doi:10.1111/cogs.12317. TLDR
    People with larger social networks are better at understanding evaluative language, such as restaurant or product reviews. The paper provides joint evidence from an individual difference study and an experiment where social network size was experimentally manipulated.
  • Lev-Ari, S. (2016). Selective grammatical convergence: Learning from desirable speakers. Discourse Processes, 53(8), 657-674. doi:10.1080/0163853X.2015.1094716. TLDR
    Social factors such as speakers’ social standing and how much they are liked influence whether listeners adopt their grammatical patterns. The experiments test influence on generalized learning, that is, not convergence during interaction but behavior afterwards.
  • Lev-Ari, S. (2016). Studying individual differences in the social environment to better understand language learning and processing. Linguistics Vanguard,2(s1), 13-22. doi:10.1515/lingvan-2016-0015. TLDR
    An opinion/review paper that argues that individual differences in the social environment influence linguistic skills and performance even in adult native speakers. Specifically, it reviews evidence that shows that differences in input can affect performance by (1) influencing people’s knowledgebase, (2) by modulating their processing manner, and (3) by shaping expectations.
  • Lev-Ari, S., & Peperkamp, S. (2016). How the demographic make-up of our community influences speech perception. The Journal of the Acoustical Society of America, 139(6), 3076-3087. doi:10.1121/1.4950811. TLDR
    By recruiting participants from across the US and matching their location to census data, the paper shows that the demographic make-up of the community influences people’s expectations about what foreign languages sounds like, and how they consequently perceive the speech.