Highlights

• • Native English speakers and learners of Kaqchikel gave semantic associates to words.

• • Networks were created of semantically related words for both groups.

• • English networks had structures similar to networks of creative individuals.

• • Kaqchikel networks had structures similar to networks of less creative individuals.

• • Different semantic network structures may account for the foreign language effect.

3. Results and discussion

As many have noted (e.g., De Deyne et al., Reference De Deyne, Navarro, Perfors, Brysbaert and Storms2018), there is variability across studies using the semantic associate task in the number of responses that participants are asked to provide (e.g., the first, more than one, as many as possible in a set amount of time, etc.), and in the criteria used to “count” a response (e.g., 2 or more participants provide the response, etc.). Given the limited number of Kaqchikel learners and the small number of cue words (relative to other studies) in the present study, we created our networks (using the Gephi software; Bastian et al., Reference Bastian, Heymann and Jacomy2009) by having nodes represent the 71 cue words with directed edges connecting to nodes representing all of the (real-word) responses provided by participants (regardless of the number of participants who provided that response). Table 1 lists several commonly used measures for the two resulting networks. See Appendix B for definitions of each measure.

Table 1.

Network measures of the Kaqchikel learners and monolingual English speakers

Many of the values reported in Table 1 are “points,” not mean values of a distribution. Because there are no degrees of freedom in such measures, conventional statistics cannot be used to assess the statistical significance of observed differences. Previous studies (e.g., Watts & Strogatz, Reference Watts and Strogatz1998; Vitevitch et al., Reference Vitevitch, Castro, Mullin and Kulphongpatana2023) used an informal heuristic of a value in one network being “different” if it is more than 1.5 times greater than the same measure from the other network. Using that heuristic, none of the measures differ “significantly,” except nodes, edges, graph density and connected components.

Although the monolingual English speakers and the Kaqchikel learners received the same 71 cue words, the results in Table 1 show that the network created from the responses from the English speakers had approximately 3 times as many nodes (i.e., words) and edges than the network created from the responses from the Kaqchikel learners. Similarly, when we consider the out-degree of just the 71 cue words we again see a statistically significant difference (using an independent samples t-test, t (140) = 24.68, p < .001), with the English speakers producing more responses to each cue word (mean = 26.69 words, SD = 6.20) than the Kaqchikel learners (mean = 7.27 words, SD = 2.36).

The larger number of responses from the monolingual English speakers (n = 90) compared to the Kaqchikel learners (n = 9) is of course due in part to the difference in sample size for each group. However, the difference in the number of responses from the two groups is also indicative of a difference in the variety of the responses provided by each group. Consider for example the responses to the cue word Ixïm (corn) for the Kaqchikel learners and the monolingual English speakers (Figure 1).

Figure 1.

The top panel shows the ego network for Ixïm (corn) from Kaqchikel learners (with a density of 0.144). The bottom panel shows the ego network for corn from monolingual English speakers (with a density of 0.054).

Notice (in the examples in parentheses) that both groups produced responses related to locations where corn is grown (English: farm, crop, Kansas, Nebraska, field, cornfield; Kaqchikel: Guatemala, tecpán (Tecpán = a town in Guatemala), land, field, hills, mountain), food items related in some way to corn (English: tortilla, beans, syrup; Kaqchikel: tortilla), components of corn (English: cob, husk, stalk; Kaqchikel: seed), and occupations related to corn (English: Husker, farmer; Kaqchikel: farmer). Thus, both groups tended to focus on similar characteristics or features of the cue words.

However, the responses provided by the Kaqchikel learners appear to be more direct or utilitarian, whereas the responses provided by the English speakers appear to be more original or “creative” in nature (e.g., (corn)dog, (corn)flake, candy(corn), Thanksgiving, maze, (corn)starch, popcorn, etc.). Previous studies suggest that speakers of a foreign language make decisions about moral dilemmas that are more utilitarian in nature than native speakers of a language (Costa et al., Reference Costa, Foucart, Hayakawa, Aparici, Apesteguia, Heafner and Keysar2014; Planchuelo et al., Reference Planchuelo, Hinojosa and Duñabeitia2024; cf., Rühle & Lev-Ari, Reference Rühle and Lev-Ari2024). This so-called “foreign language effect” may have contributed to the difference we observed in how utilitarian (or creative) the responses were from Kaqchikel learners (or English speakers).

The observation that both groups focused on similar characteristics or features of the cue words casts some doubt on an explanation based completely on the foreign language effect. Further doubt is cast on the foreign language effect by findings of a diminished foreign language effect in bilinguals who are proficient in both languages (Degner et al., Reference Degner, Doycheva and Wentura2011; Dylman & Champoux-Larsson, Reference Dylman and Champoux-Larsson2020), suggesting that proficiency in L2 may influence the extent to which a response is utilitarian/creative in the present study. Recall that the participants in the present study were (self-reported) native English speakers with 18+ years of exposure to the language and learners of Kaqchikel enrolled at the same Midwestern university with approximately 5 months of exposure to the language. The more proficient English speakers had a richer, more nuanced semantic knowledge to draw upon for each cue word than the Kaqchikel learners who knew a limited number of words in that language. This enabled the English speakers to produce unique and creative responses, whereas the limited vocabulary of the Kaqchikel learners restricted them to common, rigid, utilitarian responses.

The hypothesis that the size or structure of the semantic network in novice/proficient language users might influence the utilitarian/creative nature of their responses is consistent with other work that examined the semantic networks of people who were high or low in creativity (Kenett et al., Reference Kenett, Anaki and Faust2014). Kenett et al. found that less creative individuals had semantic networks that were rigid, and had few connections between nodes. The semantic networks of more creative individuals had broader connections (connecting seemingly distant concepts) and were more spread out. Consistent with the idea that novice language users might produce less creative/utilitarian responses and proficient language users might produce more creative responses is the density of the Kaqchikel and English networks. As shown in Table 1 the Kaqchikel network (density = 0.003) was more rigidly connected than the English network (density = 0.001) where seeming disparate concepts might be connected. A difference in network density can also be seen in the ego-networks of the cue word Ixïm/corn shown in Figure 1 (with the density values reported in the caption).

Additional support for the hypothesis that the structure of the semantic network of novice/proficient language users might influence the utilitarian/creative nature of their responses can be seen in the number of components in each network. The English network had only 1 component, meaning that every node was connected in some way to every other node in the network. The implication of such a structure is that even disparate concepts can be reached and might appear to be related in some way.

In contrast, the network of the Kaqchikel learners had 3 components: a giant component (containing 409 of the 421 nodes that were connected to each other), and 2 smaller components that were not connected to the other components. (One of the small components contained the cue word taluwäch/television and 6 responses, and the other small component contained the cue word ti’ij /meat and 5 responses.) The implication in this case is that there is a literal “gap” in semantic knowledge (i.e., 3 components that are disconnected from each other), which makes it impossible to get from some concepts to others, and makes it increasingly difficult to use words in a flexible, novel, or original way to produce a “creative” response. The observation that learners of a language might have a semantic network with several disconnected components also suggests that language instructors might target vocabulary words that bridge together the components that are disconnected from each other to aid acquisition by better approximating the semantic structure of proficient/native speakers.

Consider further the responses from the proficient/native English speakers (in the bottom panel of Figure 1). Notice that several of the responses are compound words with complex compositional meanings (e.g., corndog, cornflake, candy corn, cornstarch, popcorn). The Common European Framework of Reference (CEFR) states that having “…a good familiarity with idiomatic expressions and colloquialisms…” is indicative of CEFR-level C2 “proficiency” (from the Spoken Interaction criterion) in a foreign language. Perhaps a related marker of proficiency in a foreign language might relate to the facility that speakers have in not only understanding compound words with complex compositional meanings but in creating novel, humorous concepts, such as cabbage cage, by pairing existing concepts (Siew et al., Reference Siew, Engelthaler and Hills2022). Further investigation of such responses from the English speakers in the present study may be useful in determining how a mechanism known as “generators” develops in the lexical network of a native speaker or an L2 speaker to model the creation of such novel concepts or relationships between existing concepts (Hills & Kenett, Reference Hills and Kenett2022).

More broadly, further investigation of the responses from the English speakers in the present study might contribute to questions about the way that morphology is represented and processed in the mental lexicon (Embick et al., Reference Embick, Creemers, Goodwin Davies, Papfragou, Trueswell and Gleitman2022). The investigation of Kaqchikel learners in the present study also highlights the importance of considering less taught/non-WISP languages with morphological systems that differ significantly from the English language when addressing questions about morphological processing, or when attempting to use networks to model the lexicon (Vitevitch et al., Reference Vitevitch, Martinez and England2024).

The present study demonstrates a novel way to use a semantic association task and the mathematical techniques from cognitive network science to study the mental lexicon of native speakers and language learners. For other examples of network science being used to investigate bilingualism see Borodkin et al. (Reference Borodkin, Kenett, Faust and Mashal2016)), Tiv et al. (Reference Tiv, Gullifer, Feng and Titone2020)), and Luef (Reference Luef2022).

Perhaps unsurprising, the results of the present analysis show that the semantic network of language learners is smaller than the semantic network of native speakers of a language. Crucially, however, the present analysis suggests that the more “loosely” connected structure of the semantic network of native speakers of a language may facilitate the production of linguistically creative responses (i.e., associating seemingly disparate concepts). In contrast, the rigid and disconnected structure of the semantic network of (novice) language learners may constrain their responses to be more common, straightforward, and utilitarian in nature.

Although the utilitarian decisions made by people in L2 compared to their native language (known as the “foreign language effect”) are often attributed to the reduced emotional response elicited by the L2 (Costa et al., Reference Costa, Foucart, Hayakawa, Aparici, Apesteguia, Heafner and Keysar2014), the present observation suggests an alternative interpretation. Namely, the size and structure of one’s semantic network may allow an experienced speaker to creatively connect disparate concepts (see Kenett et al., Reference Kenett, Anaki and Faust2014), or constrain a less experienced speaker (with a smaller and disconnected network) to produce responses that are more utilitarian and less creative in nature.

The present analysis also draws attention to less taught/non-WISP languages, and illustrates how studying such languages can lead to insights that might help researchers better understand how all languages (even L1) are learned (e.g., Luef, Reference Luef2022). For example, language instructors might use the tools of network science to map out the semantic or phonological knowledge of their students and target new vocabulary words that would bridge the “gaps” between disconnected clusters of related words. Finally, the present analysis provides another way in which the tools of network science can be used to understand another aspect of bilingualism (see also Borodkin et al., Reference Borodkin, Kenett, Faust and Mashal2016 and Tiv et al., Reference Tiv, Gullifer, Feng and Titone2020).