In model SYNTHESIS.ALL we model the effects of the log number of L1 speakers and the proportion of L2 speakers (excluding semi-speakers) in competition with one another. According to the mixed logistic regression of the maximal model, log number of L1 speakers had a significant inverse effect on the degree of inflectional synthesis [log(λ) = −0.12 ± 0.026; χ 2 (1) = 15.2; p = 0.000095]. The proportion of L2 speakers (excluding semi-speakers) had also an inverse effect and this time also a significant effect on the degree of inflectional synthesis [log(λ) = −0.47 ± 0.20; χ 2 (1) = 5.8; p = 0.016]. We again tested the random effect structure with maximum likelihood ratio tests because most of the random effect variances for the slopes (both Stocks and Area) were very close to zero (of the magnitude of 1e-7) and ended up removing all the random slopes (all were non-significant).

SYNTHESIS.L2+.

According to this reduced model the log number of L1 speakers had a significant inverse effect on the degree of inflectional synthesis [log(λ) = −0.10 ± 0.026; χ 2 (1) = 12.3; p = 0.00046] and so did the proportion of L2 speakers [log(λ) = −0.47 ± 0.19; χ 2 (1) = 6.58; p = 0.010]. For the purpose of model comparison, we modeled the log number of L1 speakers in isolation from the proportion of L2 speakers but just for this smaller data set (n = 65), keeping the random structure identical (that is, modeling just the random intercepts). In this model the log number of L1 speakers again had a significant but slightly smaller inverse effect on the degree of inflectional synthesis [log(λ) = −0.09 ± 0.025; χ 2 (1) = 9.5; p = 0.0021] than when modeling the log number of L1 speakers in the same model with the proportion of L2 speakers. The coefficient for log of L1 speakers was −0.10 and its inverse logarithm is 0.905. This means that (in this smaller sample) as the population size becomes 10 times larger the language will have on average 9.5% fewer inflectional categories per verb conditioned by the random effect structure. The coefficient for the proportion of L2 speakers was −0.47 and its inverse logarithm is 0.625. This means that languages spoken by communities with 100% L2 speakers have about 37.5% fewer inflectional categories per verb than those with no L2 speakers conditioned by the random effect structure.

**Figure 8** presents the effect plots for the model predictors in model SYNTHESIS.ALL6 . The plots present the predictors' values on the x-axis and the predicted values of the response on the y-axis. Based on the effect plot for log L1 speakers as the predictor, the predicted degree of synthesis drops from roughly eight categories in communities with about 10 speakers [log(100) = 2] to about four in communities with 100 million or more L1 speakers [log(100,000,000) = 8]. The downward slope is very clear. Based on the effect plot for the proportion for L2 speakers, the predicted degree of synthesis drops from roughly

6Created using R package effects (Fox, 2003).

TABLE 4 | Results of model comparison for the reduced model SYNTHESIS.ALL. The full model includes both the log number of L1 speakers and the proportion of L2 speakers.

six categories in communities with no L2 speakers to about four in communities with about 80% or more L2 speakers. There is a downward slope but not as steep as for the log number of L1 speakers.

For model comparison we used AICc; the results are reported in **Table 4** in decreasing order of AICc. Based on the AICc values the model (1) which contained only the random intercepts but no fixed effects had the largest AICc value (306.4) and, therefore, it is the worst of the four models. In model (2) the proportion of L2 speakers was added as a fixed effect to the random intercepts-model and this decreased the AICc by 1.5 compared to model (1). This decrease is small and suggests that modeling the proportion of L2 speakers in isolation from the log number of native speakers produces a negligible effect. In model (3) the log number of L1 speakers was added to the random intercepts-model and this decreased the AICc by 7.2 compared to model (1). This large reduction suggests that the log number of L1 speakers has a reasonable effect on the degree of inflectional synthesis. In model (4) the proportion of L2 speakers was added as a fixed effect to model (3), which gives us the full model that contained the random intercepts and both of our fixed effects. In the full model the AICc value was the smallest, being 4.2 smaller than in model (3). We further used Akaike weights (the right-most column in **Table 4**) to compare these four models to one another (Burnham and Anderson, 2002). The Akaike weights scale the differences in the models' AIC values to a scale of 1 and thus provide an easy and effective way to interpret the models' AIC differences7 . Based on the Akaike weights, the model (4) which includes both the log number of L1 speakers and the proportion of L2 speakers has 88.4% chance of being the best model among our four models. These results suggests that modeling both demographic factors in the same model significantly improves the model fit compared to modeling them in isolation from one another8 .

Thus, to summarize, the log number of L1 speakers has a significant effect on the degree of inflectional synthesis both in the larger sample (SYNTHESIS.L1; n = 309) and in the smaller sample (SYNTHESIS.ALL; n = 65). Conversely, the proportion of L2 speakers has a clearly significant effect on the degree of inflectional synthesis only when modeling it in competition with the number of L1 speakers (p = 0.010) but not when modeling it in isolation (p = 0.051). These results are confirmed by comparing the AIC values.

#### 3.2.3. Discussion

Two of the four statistical tests that we carried out to investigate the effect of population data on the degree of inflectional synthesis yielded significant results. Altogether these findings replicate and expand on previous research (Lupyan and Dale, 2010; Bentz and Winter, 2013) and suggest that the hypothesis whereby verbal inflectional synthesis adapts to demographic variables is corroborated by the present data set.

Our first model (SYNTHESIS.L1) replicated the earlier findings by Lupyan and Dale (2010). However, our results were based on a data set (309 languages) that was more than two times larger than the data set (145 languages) in Lupyan and Dale (2010). We also used the original exact counts for the degree of inflectional synthesis from AUTOTYP rather than the conflated count categories from the WALS.

We then estimated the proportion of L2 speakers in the whole speech community in the spirit of Bentz and Winter (2013). In their study the proportion of L2 speakers had a significant inverse effect on the number of case distinctions but, importantly, the size of the speech community did not. The fact that in our models the proportion of L2 speakers (whether including or excluding semi-speakers) did not have a clearly significant effect on the degree of verbal inflectional synthesis suggests that the proportion of L2 speakers alone is not a sufficient predictor of adaptive effects for all kinds of different linguistic structures, although it may be sufficient for some, such as number of cases. This result is in line with the hypotheses of Trudgill (2011b), who argues that single sociolinguistic features may not be sufficient for showing correlations between language structure and sociolinguistic structure and that richer models of the sociolinguistic environment are necessary instead.

We also contrasted two measures for the proportion of L2 speakers, namely, one including semi-speakers and the other excluding them. While in the latter model (SYNTHESIS.L2) the proportion of L2 speakers was borderline significant, in the former (SYNTHESIS.L2+) it was not. In addition, in the former model the slope for Stocks was significant. This result may be related to the observation in section 3.2.2 that the median proportion of L2 speakers was much smaller than the median proportion of semi-speakers, all of which came from small languages of North America. In other words, the large median proportion of semi-speakers suggests a different sociolinguistic environment, and thus different conditioning factors for those

7The AICc values as well as the Akaike weights were computed using package MuMIn (Barton, 2018).

8Although our data is relatively small we also tested whether the interaction term between the log number of L1 speakers and the proportion of L2 speakers would have a significant effect on the degree of inflectional synthesis. It is possible that in very large languages the population size of L1 would act as a buffer against transfer effects from the L2 population (cf. our discussion in section 3.1). For this purpose we compared a model that included this interaction term to one that excluded it (using only random intercepts for both Stock and Area). Based on the result the interaction term had a negative but non-significant effect on the degree of

inflectional synthesis [log(λ) = −0.085 ± 0.082; χ 2 (1) = 1.1; p = 0.30]. This suggests that population size and the proportion of L2 speakers influence degree of inflectional synthesis independently of one another.

languages for which the number of semi-speakers was reported compared to those for which the number of L2 speakers was reported. For future research it may thus be necessary to treat L2 speakers separately from semi-speakers, to the extent that this is analytically possible.

Lastly in our model SYNTHESIS.ALL we included both the log number of L1 speakers and the proportion of L2 speakers (excluding semi-speakers) in the same model, which produced a set of interesting results. First, the number of L1 speakers had a significant effect even with the smaller sample (compared to model SYNTHESIS.L1). This result suggests that the number of L1 speakers is an important predictor of the degree of verbal inflectional synthesis and that the result in model SYNTHESIS.L1 was not just a consequence of larger sample size. Most interestingly, both our sociolinguistic factors had a significant inverse effect on the degree of inflectional synthesis when modeled as fixed effects in the same model and this model was also the best among competing models when using Akaike weights. In contrast, the proportion of L2 speakers did not have a clearly significant effect on the degree of inflectional synthesis when modeled in isolation (model SYNTHESIS.L2 and model SYNTHESIS.ALL). These results are in line with Trudgill (2011a,b)'s predictions. According to Trudgill, the sociolinguistic environment that attracts adaptation in the complexity of language structures cannot be systematically characterized by single sociolinguistic features, such as population size, but demands richer data. He further suggests that three sociolinguistic factors are decisive, namely, population size (here roughly the number of L1 speakers), degree of language contact (that we approximate by measuring the proportion of L2 speakers in the speech community), and the density of social networks. While our models did not include a factor for density of social networks, they still provided improved results compared to modeling the sociolinguistic factors in isolation. For future research our results suggest that the kind of sociolinguistic environment that may attract changes in the complexity of language structures cannot be easily captured by single demographic factors, but should preferably include information about population size, degree of contact vs. isolation, and possibly also other factors.

#### 3.3. Study 2: Morphological Complexity and Grammatical Gender 3.3.1. Materials and Methods

We collected data on the number of genders in 345 languages. The material is provided in the **Supplementary Material**. The data is largely based on Sinnemäki (unpublish) and Corbett (2013a) and therefore we follow the definitions in these two studies.

As outlined in section 2.2, we define gender as a grammatical strategy that groups nouns into classes. These classificatory distinctions are not necessarily marked on nouns, but must be marked on clausal constituents that are in a syntactic relationship (also known as agreement) with nouns.

TABLE 5 | Model names and predictors in case study 2.

The number of genders in a language was counted based on number of distinguishable agreement classes. Usually a gender class is marked consistently across inflectional paradigms. However, often not all distinctions are present in all paradigms, as is the case in Mufian (**Table 1**). For instance, verb prefixes in Mufian are identical in classes 1, 2, and 3 in the plural, but in the singular the classes are distinguished from one another. For this reason each of these classes was counted as a separate gender in Mufian; all sample languages were analyzed with the same principles.

Our hypothesis is that an inverse relationship exists between the number of genders and the demographic factors used as independent variables. Similarly to case study 1, we constructed generalized linear mixed effects models using the package glmmADMB (Fournier et al., 2012; Skaug et al., 2016) in R (R Core Team, 2017) to assess the relationship between the number of genders and the demographic factors. The Poisson regression modeling is complicated by the large number of zeroes. The sample contains 345 languages but 200 (58%) of them have no genders. We accounted for this high number of zeroes by using zero inflation models offerred by glmmADMB. As in study 1, in this case study, too, we set the L1 population sizes to 50 when the actual number of L1 speakers was 50 or less (and for the same reasons; see section 3.2.1).

We constructed four models in this case study following the same principles as in case study 1 (see section 3.2.1). The model names and their predictors are listed in **Table 5**. In all of the models the number of genders was the response and the random structure was the same: AUTOTYP stocks were used as a grouping factor for genealogical affiliation and the 24 areas of AUTOTYP as the grouping factor for areas. However, models containing random slopes may lead to overfitting and the random effect variances being zero or approaching zero. To improve our models we tested whether some of the random slopes could be removed.

The number of genders is discrete count data, ranging from 0 to 17, and therefore we used Poisson regression to model the data. Poisson distribution assumes that the sample mean is identical with the sample variance. However, the dispersion ratios met the assumption about identical sample mean and variance (that is, the dispersion ratios were not significantly different from 1) only in model GENDER.L1. In models GENDER.L2, GENDER.L2+, and GENDER.ALL the dispersion ratio was significantly different from 1 which means that the assumption about identical sample mean and variance was not met for these models (see **Table 6**). Our solution was to use negative

TABLE 6 | Dispersion ratio and deviance from 1 for models in case study 2.

binomial models for these three models and Poisson regression for GENDER.L1.

#### 3.3.2. Results

The sample contains data on log number of native speakers and the number of genders in 345 languages. It was possible to get data on the proportion of L2 speakers in 65 languages and for an additional 7 languages on the number of semi-speakers. The distribution of the number of genders is shown in **Figure 9**. The number of genders has a roughly negative exponential distribution, that is, it is strongly skewed to the right. This kind of distribution is typical for typological variables (Cysouw, 2010). The areal distribution of number of gender is provided in **Figure 10** on a world map.

The distribution of the demographic factors for the sample languages is shown in **Figure 11**. In this sample the median size of L1 populations was 10,000, which is somewhat smaller than in case study 1 but still larger than the total median of 7,000 for all spoken languages in the Ethnologue. The median proportion of L2 speakers was 19% and that of semi-speakers 58%. These figures are practically identical to those in case study 1 because roughly the same data was used.

According to the zero-inflated mixed logistic regression of the maximal model of GENDER.L1, log number of L1 speakers had a non-significant (positive) effect on the number of genders [log(λ) = 0.015 ± 0.069; χ 2 (1) = 0.048; p = 0.83]. However, while this maximal model converged the random effect variances for the slopes (both Stocks and Area) were very close to zero (of the magnitude of 1e-7). The maximum likehood ratio tests confirm that both slopes may be removed from the model [random slope over Stocks: χ 2 (1) = 0.14; p = 0.71; random slope over Area: χ 2 (1) = 1.49; p = 0.22]. According to the reduced model, the effect of log number of L1 speakers on the number of genders was non-significant [log(λ) = 0.024 ± 0.059; χ 2 (1) = 0.17; p = 0.68]. The non-significant p-value provides evidence that log number of native speakers has no effect on the number of genders.

According to the zero-inflated negative binomial mixed logistic regression of model GENDER.L2, the effect of the proportion of L2 speakers on the number of genders was negative but non-significant [log(λ) = −0.69 ± 0.49; χ 2 (1) = 1.1; p = 0.30]. Again, while the maximal model converged the random effect variances for both Stocks and Area were very close to zero (of the magnitude of 1e-7) and as a result the random slopes for both Area and Stocks were removed [random slope over Stocks: χ 2 (1) = 1.1; p = 0.29; random slope over Area: χ 2 (1) = 0.006; p = 0.94]. According to the reduced model the proportion of L2 speakers had an inverse and non-significant effect on the number of genders [log(λ) = −0.53 ± 0.61; χ 2 (1) = 0.59; p = 0.44]. Based on these results the proportion of L2 speakers has no effect on the number of genders.

**Figure 12** presents the number of genders as a function of the demographic variables in models GENDER.L1 and GENDER.L2. The curve indicates the fit of the mixed regression model. The figure on the left (**Figure 12A**) presents the fit to log number of L1 speakers. As is evident from the plot, the fitted line is almost flat. The figure on the right (**Figure 12B**) presents the fit to the proportion of L2 speakers. There is a small downward trend so that in communities with few L2 speakers the predicted number of genders is about three and approaching two as the percentage of L2 speakers grows closer to 100%.

According to the zero-inflated negative binomial mixed logistic regression of model GENDER.L2+, the proportion of L2 speakers (including semi-speakers) had an inverse but nonsignificant effect on the number of genders [log(λ) = −0.67 ± 0.46; χ 2 (1) = 1.29; p = 0.26]. We again tested the random effect structure with maximum likelihood ratio tests and removed the random slope for both Stocks and Area [random slope over Stocks: χ 2 (1) = 0.09; p = 0.76; random slope over Area: χ 2 (1) = 0.01; p = 0.93]. According to the reduced model the proportion of L2 speakers (including semi-speakers) had an inverse but non-significant effect on the number of genders [log(λ) = −0.59 ± 0.52; χ 2 (1) = 0.93; p = 0.34]. Based on this result the proportion of L2 speakers had no effect on the number of genders.

In model GENDER.ALL we model the effects of the log number of L1 speakers and the proportion of L2 speakers in competition with one another. This time we include semispeakers for reasons of improved convergence compared to when excluding semi-speakers. According to the zero-inflated negative binomial mixed logistic regression of the maximal model, log number of L1 speakers had a non-significant inverse effect on the number of genders [log(λ) = −0.13 ± 0.21; χ 2 (1) = 0.88; p = 0.35]. The proportion of L2 speakers had also an inverse but nonsignificant effect on the number of genders [log(λ) = −0.258 ±

FIGURE 10 | The distribution of number of genders on a world map (In the figure black dots represent languages with no gender and blue dots represent those with two genders. The deeper the red color, the more genders the language has).

0.74; χ 2 (1) = 0.51; p = 0.48]. We again tested the random effect structure with maximum likelihood ratio tests because most of the random effect variances for the slopes (both Stocks and Area) were very close to zero (of the magnitude of 1e-7) and ended up removing all the random slopes (all were non-significant). According to the reduced model the log number of L1 speakers had a non-significant inverse effect on the number of genders [log(λ) = −0.05 ± 0.10; χ 2 (1) = 0.21; p = 0.65] and so did the proportion of L2 speakers [log(λ) = −0.62 ± 0.50; χ 2 (1) = 1.08; p = 0.30].

We further used AICc for model comparison; the results are reported in **Table 7** in decreasing order of AICc. The model (4) which contained only the random intercepts but no fixed effects had the smallest AICc value (245.5). Based on the Akaike weights this model had more than 50% chance of being the best model among the four models. These results clearly suggests that neither of the demographic factors had any meaningful effect on the distribution of the number of genders.

As a summary, the results of study 2 suggest that the number of L1 speakers and the proportion of L2 speakers do not have a significant effect on the number of genders. The estimate was negative for both demographic factors (except in GENDER.L1), but since the effects were non-significant and the AICc values were small, the only reliable conclusion to draw from these results

TABLE 7 | Results of model comparison for the reduced model GENDER.ALL. The full model includes both the log number of L1 speakers and the proportion of L2 speakers (including semi-speakers).

is that the log number of L1 speakers and the proportion of L2 speakers have no effect on the number of genders.

#### 3.3.3. Discussion

None of the four statistical tests that we carried out to investigate the relationship between number of gender distinctions and population data yielded significant results. These (negative) findings replicate and expand on previous research by Dahl (unpublish) and suggest that the hypothesis whereby gender systems adapt to demographic variables must be rejected, at least based on the present data set.

Even though all the tests failed to reach significance, one interesting pattern emerged from the data as a function of the feature values assigned to our dependent variable "Number of genders." We first tested whether the overall results could be affected by counting the exact number of genders for any of the sampled languages. Thereafter, we tested the relationship between the number of genders and population structure by using the classification of Corbett (2013a) in WALS. This classification uses five values for number of gender distinctions: "none," "two," "three," "four," "five or more." Conflating number of genders greater than four into one bin, "five or more," means to assume that a language with, say, 12 genders would not behave differently from a language with five genders. However, we found that using the WALS classification had a big impact on the results.

In particular, when we modeled the effect of the proportion of L2 speakers on number of genders and used the exact count of gender distinctions for languages with more than five genders, we found a non-significant negative correlation between the proportion of L2 and the number of genders. When following the WALS coding, which collapses together all languages with five or more genders, the observed coefficient between the number of genders and L2 proportions was instead positive [maximal zeroinflated negative binomial model; log(λ) = 0.84 ± 0.45; χ 2 (1) = 2.98; p = 0.11], even though still non-significant. This same pattern was observed when the proportion of L2 speakers also included the number of semi-speakers. The correlation coefficient was negative (but non-significant) when the exact number of genders was factored in, but it became positive (and still non-significant) when we followed the WALS data coding structure [maximal negative binomial model; log(λ) = 0.66 ± 0.44; χ 2 (1) = 1.61; p = 0.20], that is, when we lumped together languages with five or more gender distinctions.

As for the number of L1 speakers, the choice of coding had a parallel outcome. When we modeled the effect of the number of L1 speakers on the number of genders and used the exact count of gender distinctions, we found a non-significant positive correlation between the variables. When, following the WALS coding, we collapsed together all languages with five or more genders the observed estimate was instead negative [maximal zero-inflated poisson model; log(λ) = −0.014 ± 0.05; χ 2 (1) = 0.07; p = 0.79], even though still non-significant.

While these results do not affect the overall outcome of the case study, the mismatching patterns demonstrate that data structure and data coding may act as crucial confounding factors when running statistical tests on already available databases. In this particular case, the results suggest that a less abstract coding approach than the one adopted by WALS is preferable when investigating sociolinguistic correlates of number of gender distinctions and that the assumption we make about the behavior of languages with five or more genders matters crucially.

With regard to data coding, a parallel case reported in the literature is the correlation between phoneme inventory size and population size by Atkinson (2011). Using the WALS data, Atkinson (2011) arrived at a significant negative correlation between phoneme inventory and population size, which seemed to be connected to geographical spread, namely, to the spread of languages out of Africa. The WALS data for number of consonants divides data into five bins: "small," "moderately small," "average," "moderately large," "large." Maddieson et al. (2011) took the underlying data for the same WALS chapter and still found a significant correlation, but Donohue and Nichols (2011) and Moran et al. (2012) used completely different data sets and found no significant correlation between phoneme inventory and population size reflected there. Alongside with our own results from number of genders and population size, the controversy about phoneme inventory and population data thus suggests that data, and data coding, clearly matter.

In addition, our impression is that, particularly in the case of grammatical gender, the confounding effect of data and data coding may even be a reflection of the type of variable chosen as a proxy of complexity. As outlined in section 3, recent research (Audring, 2014, 2017; Di Garbo, 2016) posits that number of gender distinctions is one of the three main dimensions of complexity variation in gender systems, along with gender assignment rules (whether gender assignment is semantic/formal, rigid/flexible), and formal marking (which word classes inflect for gender in a given language). These studies show that complexity at the level of gender distinctions predicts complexity in other domains of the gender system. For instance, Di Garbo (2016) observes that out of a sample of 84 African languages, particular instances of flexible gender assignment are only attested in languages with a high number of gender distinctions or a high degree of formal marking. Similarly, Audring (2014) observes that in languages with a high number of gender distinctions, complexity in the domain of formal marking (i.e., presence of gender marking on different types of targets in the clause) may facilitate the learning and use of gender distinctions (the more occurrences of gender marking within the utterance the easier to remember the gender of a noun). Thus, while it is no doubt that complexity in the domain of number of gender distinctions bears relevant interactions with complexities in other areas of the gender system of a language, it may well be that this type of complexity is not sensitive (or not in straightforward ways) to the effect of sociolinguistic variables. This would suggest that, in order to investigate the sociolinguistic typology of gender systems from a quantitative point of view, other typological variables than number of genders must be used. This consideration, which is also embraced by Dahl (unpublish), is the point of departure of recent research by Di Garbo and Verkerk (2017). They observe that neither the number of genders nor any of the other WALS variables for gender systems directly tackle the morphosyntactic encoding of gender distinctions, that is, the structural properties of gender marking systems. Under the assumption that it is morphosyntax which is directly sensitive to the effect of sociolinguistic variables, they thus look at synchronic variation in gender marking patterns in a sample of 253 Bantu languages, which are well known in the literature for their rather elaborated systems of gender marking. The study finds a significant positive correlation between incidence of restructuring in gender marking and population size whereby languages with larger populations show a preference for restructured gender marking systems9 . This result partially contradicts the findings on creole languages by Blasi et al. (2017), who find no evidence for adaptive patterns in gender marking on adjectival modifiers and personal pronouns, the two gender-related variables included in the APICS database (Michaelis et al., 2013), which the study is based upon. However, while Blasi et al. (2017) only look at these two domains of gender marking, Di Garbo and Verkerk take into account a wider range of syntactic domains (adnominal modification, predication, relative constructions and pronouns) and, within each of these domains they consider different kind of gender marking hosts (for instance, within the domain of adnominal modification, they look not only at adjectival modifiers but also at numerals, demonstratives, quantifiers and question words). These results thus suggest that support to the linguistic adaptation hypothesis in the domain of grammatical gender comes from typological variables that are not (entirely) part of those typological databases that have so far been used to run exploratory studies on the relationship between language structures and social structures.

## 4. GENERAL DISCUSSION AND CONCLUDING REMARKS

Starting from the assumption that languages are complex adaptive systems (Beckner et al., 2009), in this paper we investigated the hypothesis that morphological complexity is sensitive to sociolinguistic variables concerning population structure. This was done by means of two case studies, one in the verbal domain (degree of inflectional synthesis) and one in the nominal domain (grammatical gender). In both case studies, the same type of sociolinguistic data were operationalized as independent variables: population size (measured as log number of L1 speakers) and proportion of L2 speakers (including/excluding semi-speakers in different models). The raw data for the typological variables came from the AUTOTYP database for inflectional synthesis on the verb and from Sinnemäki (unpublish) and WALS (Corbett, 2013a) for grammatical gender. The raw demographic data were taken mostly from the Ethnologue (see the Supporting Material). While the results of case study 1 confirm that morphological complexity in the verbal domain is sensitive to population dynamics thus bringing support to the main hypothesis, the same could not be observed in the case of grammatical gender (case study 2).

However, irrespectively of how well the individual case studies support the main hypothesis, we think that both make a relevant

9 In this study, restructured gender systems are systems in which gender marking is partially or heavily based on animacy distinctions

contribution to the understanding of non-linguistic correlates of linguistic diversity. First, the results of the two case studies suggest that not all domains of grammar adapt to sociolinguistic variables to the same extent. More specifically, our data show that while the degree of inflectional synthesis is sensitive to population data, the number of gender distinctions is not. Whether this discrepancy is related to the different functions that the two grammatical domains display in discourse is an open question whose answer we leave to further studies. Our results ultimately suggest that no general prediction can be made about the relationship between morphological complexity and population data because the outcomes of this relationship are very much specific to the grammatical domain under study.

Second, the results from study 1 suggest that competitive models, where the effect of multiple sociolinguistic variables on language structures are tested simultaneously, are somewhat better than non-competitive models, where each factor is tested in isolation. These findings bring quantitative evidence in support of Trudgill's (2011a,b) suggestion that the effect of social structures on language structures must be studied by factoring in a multifaceted array of interacting variables, ranging from population size to degree of contact and social network density. While our study covers two of the three suggested dimensions population size and degree of contact (of which the proportion of L2 speakers is taken as a proxy)—nothing could be said about social network density. Operationalizing social network density as one of the critical variables in quantitative sociolinguistic typology would, in fact, require accessing a type of data that is at present not featured in existing databases.

Third, in line with previous studies addressing similar research questions, case study 2 fails to show any significant relationship between the complexity of grammatical gender systems (measured in terms of number of gender distinctions) and sociolinguistic variables. These results contradict the wellknown observation (supported by evidence from different linguistic families and areas) that while gender systems are generally very stable, their transmission tends to be disrupted under the pressure of language contact. In line with a recent suggestion by Dahl (unpublish) and ongoing research on the topic (Di Garbo and Verkerk, 2017) we think that a reasonable explanation behind this mismatch may be that the number of gender distinctions is not a suitable measure to test hypotheses on linguistic adaptation in the domain of grammatical gender, and that typological variables pertaining to patterns of gender marking should instead be considered. In addition, we found that using the number of gender distinctions as coded in WALS, with five cut-off points between no gender, two, three, four, five

#### REFERENCES

or more gender distinctions, leads to less accurate results than following a less abstract coding procedure where languages with richer gender systems are coded based on the exact number of distinctions that they display. For these reasons, we conclude that existing typological databases are not fully equipped to support quantitative sociolinguistic typologies of grammatical gender systems.

To sum up, while at least for one of the grammatical domains used as test cases this paper confirms the validity of the linguistic adaptation hypothesis, the paper also shows that a precondition to the advancement of research on nonlinguistic correlates of linguistic diversity lies in the refinement of the statistical methodologies used to test this hypothesis as well as in the types of data and data coding principles that are fed into the analyses. In order to test hypotheses about sociolinguistic typology, comparative data on sociolinguistic variables other than demographic variables, such as relative prestige, literacy, and multilingualism, need to be collected. Furthermore, given that approaching linguistic structures (and their complexity) from different perspectives may produce radically different results about adaptation, more exploratory studies need to be run in order to test which domains of grammar and what types of language structures within a given domain are most sensitive to the effect of social structures.

## AUTHOR CONTRIBUTIONS

The research was designed together by KS and FD, data collection was done primarily by KS (data on the number of genders for languages with 5+ genders was collected together by FD and KS), statistical analyses for the typological case studies were done by KS, and the write-up was done together by KS and FD.

## ACKNOWLEDGMENTS

KS gratefully acknowledges financial support by the Academy of Finland grant 296212 and by the University of Helsinki and Stockholm university collaboration fund. FD gratefully acknowledges financial support from the Anna Ahlström and Ellern Terserus' foundation.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2018.01141/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Sinnemäki and Di Garbo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Sociolinguistic Typology and Sign Languages

Adam Schembri1 \*, Jordan Fenlon2 , Kearsy Cormier3 and Trevor Johnston4

1 Department of English Language and Applied Linguistics, University of Birmingham, Birmingham, United Kingdom, 2 Languages and Intercultural Studies, Heriot-Watt University, Edinburgh, United Kingdom, 3 Deafness Cognition and Language Research Centre, University College London, London, United Kingdom, 4 Department of Linguistics, Macquarie University, Sydney, NSW, Australia

This paper examines the possible relationship between proposed social determinants of morphological 'complexity' and how this contributes to linguistic diversity, specifically via the typological nature of the sign languages of deaf communities. We sketch how the notion of morphological complexity, as defined by Trudgill (2011), applies to sign languages. Using these criteria, sign languages appear to be languages with low to moderate levels of morphological complexity. This may partly reflect the influence of key social characteristics of communities on the typological nature of languages. Although many deaf communities are relatively small and may involve dense social networks (both social characteristics that Trudgill claimed may lend themselves to morphological 'complexification'), the picture is complicated by the highly variable nature of the sign language acquisition for most deaf people, and the ongoing contact between native signers, hearing non-native signers, and those deaf individuals who only acquire sign languages in later childhood and early adulthood. These are all factors that may work against the emergence of morphological complexification. The relationship between linguistic typology and these key social factors may lead to a better understanding of the nature of sign language grammar. This perspective stands in contrast to other work where sign languages are sometimes presented as having complex morphology despite being young languages (e.g., Aronoff et al., 2005); in some descriptions, the social determinants of morphological complexity have not received much attention, nor has the notion of complexity itself been specifically explored.

Keywords: sign languages, sociolinguistics, typology, language complexity, morphology, linguistic diversity

## INTRODUCTION

In this paper, we examine the possible relationship between proposed social determinants of morphological complexity (Trudgill, 2011), the typological nature of the sign languages of deaf communities, and how this contributes to an understanding of linguistic diversity. We review the notion of morphological complexity as defined by Trudgill and how it applies to the grammar of sign languages, with a focus on British Sign Language (BSL), Australian Sign Language (Auslan) and American Sign Language (ASL). We then discuss the sociolinguistic situation of sign languages.

#### Edited by:

Antonio Benítez-Burraco, Universidad de Sevilla, Spain

#### Reviewed by:

Gary Lupyan, University of Wisconsin-Madison, United States Wendy Sandler, University of Haifa, Israel

> \*Correspondence: Adam Schembri<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

Received: 30 August 2017 Accepted: 05 February 2018 Published: 21 February 2018

#### Citation:

Schembri A, Fenlon J, Cormier K and Johnston T (2018) Sociolinguistic Typology and Sign Languages. Front. Psychol. 9:200. doi: 10.3389/fpsyg.2018.00200

## SOCIOLINGUISTIC TYPOLOGY

fpsyg-09-00200 February 19, 2018 Time: 14:51 # 2

Interest in social structures and linguistic diversity dates back at least a century, as pointed out by Perkins (1992). Based on cross-linguistic evidence, a number of scholars have proposed that spoken languages which undergo extensive second language acquisition by adults appear to have relatively less inflectional complexity (Kusters, 2003; Dahl, 2004; McWhorter, 2007; Wray and Grace, 2007; Miestamo et al., 2008; Sampson et al., 2009). This would suggest that the default state for human languages (i.e., those which lack a history of extensive second language acquisition by adults) is a high degree of morphological complexification, as appears to be true of languages such as the Athabaskan language Navajo (with its highly irregular verbal system) or Yimas (with its rich tense system) spoken in Papua New Guinea. As a result, the moderate degree of morphological complexity of languages like English and French might thus be viewed as a 'sociohistorical anomaly' (McWhorter, 2012), resulting from the particular sociolinguistic histories of these two major languages.

Trudgill (2011) introduced the term sociolinguistic typology: a 'sociolinguistically informed' approach to linguistic typology. This approach assumes that, despite a common set of communicative pressures and cognitive abilities in all humans, different types of languages develop in different places and at different points in time partly as a result of the influence of varying sociolinguistic situations. In particular, this theory proposes that there are specific distinctive social characteristics of speech communities that mold the grammatical organization of their languages. Trudgill (2011) proposed the following factors: (1) population size, (2) social network density, (3) degree of communally shared information, (4) social stability, and (5) degree of language and dialect contact. Morphological complexification, Trudgill suggests, tends to be found in languages used by small communities, composed of dense social networks, with high degrees of communally shared information and social stability, and stable situations of language contact. Stable language contact situations refer here to multilingual communities in which one or more languages are learned as children, as opposed to language contact situations in which large numbers of adults learn a second or additional language, perhaps as the result of some significant social change (e.g., displacement caused by war).

## MORPHOLOGICAL COMPLEXITY

What does Trudgill (2011) mean by morphological 'complexification'? He proposes that it consists of the following factors: high degrees of (1) irregularity, (2) morphological opacity, (3) syntagmatic redundancy and (4) morphological marking of categories such as tense, gender, voice etc. Trudgill (2011) illustrates (1) by discussing the irregular system of noun declension in Faroese, with the paradigm for the noun dagur 'day' showing, for example, completely unrelated forms for accusative [dεa], genitive [daås] and dative case [de:ji] (compare this to the more regular system for batur 'boat,' with accusative bat, genitive bats and dative bati). By (2), Trudgill (2011) is referring to the notion that the relationship of form and meaning should be as transparent as possible (Kusters, 2003). In a dialect of North Frisian, however, Trudgill reports that, depending on the syntactic context, the infinitive form of 'do' has several variant morphological forms (i.e., allomorphs), with it appearing either as douen, doue or dou. Trudgill (2011) illustrates (3) with data from East Flemish dialects in which subject arguments involves triple-marking as in we zulle-me wij dat doen 'we shall do that' (literally 'we shall-we we that do'). Lastly, with (4) he explores how the morphological marking in the demonstrative system in some dialects of Norwegian has evolved a three-way distinction between proximal demonstratives denne/dette/desse which are equivalent to 'this' in English, distal demonstratives danna/data/dassa which are similar to English 'that' but are used for something that the speaker can point to in contrast to a third type of demonstrative – i.e., the forms den/dae/dei which refer to something that is not visible but has been recently mentioned in the conversation.

These aspects of morphological complexity, Trudgill (2011) claims, predominate in smaller, dense, stable communities without large-scale adult second language contact. In fact, many of the examples he describes in Faroese, Frisian, Flemish and Norwegian have emerged in small dialect speaking communities, and represent complexifications in comparison to more standard varieties of each language. He suggests that, as all of these features appear to be difficult for post-critical-period adult learners to master, this reflects that fact that one expects to see morphological simplification – i.e., the reduction in features (1) to (4) – in languages spoken by larger communities with looser social networks that have greater numbers of adult second language learners. Evidence supporting this hypothesis includes a recent study, for example, showing that spoken languages with large numbers of adult second language learners tend to lose nominal case systems (Bentz and Winter, 2013).

## MORPHOLOGICAL COMPLEXITY AND SIGN LANGUAGES

We would like to focus here on how Trudgill's (2011) notion of sociolinguistic typology can inform, and can be informed by, the study of sign languages of deaf communities. To our knowledge, this notion has only been partly explored in relation to sign languages (Meir et al., 2012), and the specific predictions of Trudgill's proposal have not yet been applied to the languages of deaf communities. Sign languages can be divided into two very broad subclasses: (1)'macro-community' sign languages which may be used across an entire national deaf community, such as BSL, Auslan, ASL, German Sign Language (DGS) and Taiwan Sign Language (TSL), and (2) 'micro-community' sign languages which are used by smaller communities within a nation state, such as the so-called 'village sign languages' Kata Kolok in Bali and Al Sayyid Bedouin Sign Language in Israel (see Schembri, 2010 for a description of these two community types). These two types of sign language have developed in quite different social situations, so below we explore how they may provide an

interesting test case for the proposal by Trudgill (2011), albeit with some important qualifications.

First, we consider how the notion of morphological complexity might apply to sign languages. Applying Trudgill's (2011) theory to sign languages is controversial because there is little consensus on how some aspects of their structural organization are best analyzed. Sign languages are often described as morphologically complex languages (e.g., Supalla, 1982, unpublished). Indeed, some researchers have characterized the fact that sign languages appear to have complex morphology despite being young languages a 'paradox' (e.g., Aronoff et al., 2005). In contrast, a small number of linguists (e.g., Bergman and Dahl, 1994; Liddell, 2003a) have described sign languages as inflectionless languages, but this view is not widely accepted. After a brief overview of morphology in sign languages, we will work through each of the main features of morphological complexity that Trudgill (2011) discusses, with a focus on BSL, Auslan and ASL (the sign language varieties with which the authors of this paper are most familiar). As we will see, it appears that Trudgill's notion of morphological complexity and the social determinants associated with it offer some fresh insights into this debate about the structure of sign languages: drawing on this work, we might argue that there is, in fact, no 'paradox' to solve.

First, we provide a little background about sign language structure. Formationally, signs in BSL, Auslan and ASL are composed of contrastive hand configurations, locations on the body or in the space around the signer, movements of the hands, and non-manual features, such as mouth gestures and facial expressions. Morphologically, these formational features may be modified to convey a range of meanings, some of which we explain in more detail below (Sutton-Spence and Woll, 1999; Liddell, 2003a; Johnston and Schembri, 2007). Many of these morphological patterns are widely found in unrelated sign languages, perhaps because they are clearly iconically motivated. For example, time-related signs may incorporate numeral handshapes to show number (e.g., TOMORROW versus IN-TWO-DAY'S-TIME in Auslan in **Figure 1A**). A subset of verb signs, which we will refer to here as indicating verbs, may be directed toward locations associated with the referents of the verb's arguments, as we see in **Figure 3**. Another category of verb signs, known as classifier constructions or depicting signs, include handshape morphemes that represent classification of a referent into a number of semantic or shape categories. These handshapes combine with movement and spatial components to build complex iconic representations of the specific referent in motion, its relative location and/or its distribution, as we can see in **Figure 1B**. This example shows three possible combinations of a Auslan classifier handshape for person in relation to another classifier handshape for vehicle. These forms represent perhaps the most complex constructions in signs languages, but researchers do not agree on the most appropriate morphological analysis (e.g., Liddell, 2003b). For example, do the changes in relative location in the sign in **Figure 1B** act as discrete morphemes, or are they some kind of gradient gestural representation? In addition to alternations of distinctive formational features of a sign, reduplication of a subset of nouns is used to signal plurality (e.g., Auslan HOUSE versus HOUSE[PLURAL], see **Figure 1C**). Fast or slow reduplication of some verb signs may be used to signal habitual versus continuative aspect (as in Auslan JOKE versus JOKE[continuative] in **Figure 1D**). The rich system for modification of signs is what contributes to the claim by many sign language linguists (e.g., Aronoff et al., 2005) that sign languages are morphologically complex languages.

In terms of Trudgill's (2011) criteria for morphological complexity, however, the picture seems more mixed, as few of the phenomena identified as morphologically complex by sign linguists (e.g., classifier constructions) fit into his definition. First, none of these three sign languages (BSL, Auslan, or ASL) exhibit high levels of irregularity in any of the morphological phenomena described above. There are a very small number of irregular negative verb and modal forms in each sign language, including CAN and CANNOT in Auslan and in ASL; SHOULD and SHOULD-NOT in BSL, and HAVE and HAVE-NOT in BSL. Some of the negative forms in BSL/Auslan, however, appear to involve a now unproductive negative suffix, as in DISAGREE (cf. AGREE). This suffix appears to be related to the negative lexical item in BSL/Auslan which can mean 'not have,' 'did not,' 'without' etc. There are also irregular forms meaning 'people' in Auslan and BSL (unrelated to signs meaning 'person'). Apart from these small number of examples, however, there are few other examples of irregularity attested (see BSL SignBank and Auslan SignBank for examples of these signs1,2 ).

There is only limited allomorphy in ASL, BSL and Auslan that cannot be predicted on the basis of morphophonemic processes. For example, in all three sign languages, there is a high degree of variation in the handshape in first person singular pronouns, with the pointing sign directed to the chest appearing as an extended index finger in isolation, but often as some other handshape in connected signing (as we see in **Figure 2** BSL PRO1SG BREATHE 'I breathe' where the handshape in the first person pronoun has all fingers extended, matching the handshape of the following sign BREATHE). Empirical studies indicate that this variation may be conditioned in part by the handshape of the following sign (i.e., it is due to co-articulation, see Bayley et al., 2002; Fenlon et al., 2013). Some isolated examples of unpredictable allomorphy do occur in verbs. In one regional variety of Auslan, there are two forms of the non-first person to first person form of the sign GIVE. The form with the Y handshape (i.e., a little finger and thumb extended from the fist), anecdotal reports suggest, cannot be modified for first to non-first person marking3 . In ASL, there is a non-first person to first person marked form for CONVINCE that is directed toward a location on the neck, unlike other forms of the verb produced in the signing space in front of the signer's chest. The first person object form has been argued to be an idiosyncratic form (Lillo-Martin and Meier, 2011). However, it could be argued that this form is actually similar to other first person object forms for other indicating verbs which are directed toward particular parts of the body but otherwise are predictable in form (e.g., REMIND, LOOK-AT, etc.).

3http://www.auslan.org.au/dictionary/words/give%20back-1.html

1http:bslsignbank.ucl.ac.uk

2http://www.auslan.org.au

(D) Auslan JOKE versus Auslan JOKE [CONTINUATIVE].

There is limited syntagmatic redundancy in ASL, BSL, and Auslan, with plural marking of most nouns being optional, for example, even when the nominal occurs with a lexical quantifier or verb modified for number.

ASL, BSL, and Auslan do not employ any morphological markers for gender, tense, or voice. Although some scholars claim that ASL does mark for tense and passive voice (Neidle et al., 1999; Janzen et al., 2001), the claims are based on syntactic, rather than morphological, phenomena. The marking of aspect mentioned above is clearly iconically motivated and does not appear highly grammaticalized in Auslan (Gray, 2013). Furthermore, the aspect marking system is predictable: it involves the reduplication of punctual verbs marking habitual aspect, for example, whereas a similar modification for durative verbs

FIGURE 2 | Handshape assimilation in PRO1SG.

represents durational aspect. In some sign languages, in fact, aspect marking has been considered ideophonic (Bergman and Dahl, 1994).

Genitive case is optionally marked on nouns in Auslan and some varieties of BSL (Johnston and Schembri, 2007; Cormier and Fenlon, 2009): a possessive marker that is based on fingerspelled '-s' (borrowed from English) is sometimes used, as in (1). ASL also has a possessive marker based on a modified form of fingerspelled '-s' which is also optional (Pichler et al., 2008). This appears to be an example of morphological complexification as a result of language contact.

(1) MOTHER POSSESSIVE-S SISTER 'mother's sister'

Indicating verbs appear to share some characteristics with person and number agreement in spoken languages (Sandler and Lillo-Martin, 2006; Johnston and Schembri, 2007). This modification has been called 'agreement' because it was originally assumed that the form of the verb reflects aspects of the form or semantics of the subject or object noun phrase. In fact, these modifications, like pointing used by non-signers, actually most often reflect the location of a present referent, or the association between an absent referent and a location in the space around the signer's body (Liddell, 2003a; Fenlon et al., in press). This is arguably quite different from what we see in spoken language agreement systems (Corbett, 2006), and there is considerable debate in the literature about whether it should be called an agreement system at all (e.g., Liddell, 2011; Lillo-Martin and Meier, 2011). Regardless of this debate, it is clear from studies of BSL and Auslan data that this modification is not obligatory (e.g., de Beuzeville et al., 2009; Fenlon et al., in press), as one would expect from a canonical agreement system (Corbett, 2006).

Indicating verb signs may also be modified for number. An optional alternation of location features and reduplication is used to represent number and distribution of object arguments, as shown in **Figure 3**. With two object arguments, the sign may reduplicate to different locations, or may use a twohanded construction ('dual inflection'). With more than two, a sweeping movement may be added across the signing space ('multiple inflection'). Multiple reduplications may signal marking for distribution (the 'exhaustive inflection'). Again, these modifications are clearly iconically motivated, and do not appear to be obligatory for any sign language.

Overall, it might be argued that BSL, Auslan, and ASL are languages with relatively little obligatory inflection and, based on Trudgill's (2011) criteria, low to moderate levels of morphological complexity (in contradistinction to Aronoff et al., 2005). Indeed, previous analyses have compared ASL, BSL, and Auslan grammar

to spoken language creoles (Fischer, 1978; Ladd and Edwards, 1982; Johnston, 1989). Aronoff et al. (2005) pose this similarity to creoles as a "young language puzzle": i.e., why is it that sign languages are similar in some ways to spoken language creoles and yet they have complex morphology? Our response is that sign languages, by Trudgill (2011)'s definition, are not as morphologically complex as previously assumed.

## SOCIAL STRUCTURE AND SIGN LANGUAGE COMMUNITIES

So, what about the social factors at play in deaf communities? Sign language communities tend to be small, but not as small as many spoken languages. For example, Lupyan and Dale (2010) show that the median number of speakers of the 6,192 languages cataloged by Ethnologue is only 7000, although the mean is over 828,000. The total number of signers in North America, the United Kingdom and Australia numbers in the thousands (although this is likely to be in the hundreds of thousands in the North American case), so all of these sign languages would have a lower number than the mean for all languages given in Ethnologue, with only Auslan possibly approaching the much lower median. In terms of the density of social networks, there has been relatively little research into the network densities of macro-community sign languages (the work of Morris, 2016, being the only example). A small number of deaf individuals are from deaf families, work with deaf people and have deaf partners, and this core of the deaf community might have dense social ties with other signers. Over 95% of deaf people, however, are from hearing families (Mitchell and Karchmer, 2004). It is also likely that most deaf adults work with hearing people, and thus they have considerable contact with social networks that do not include people who can sign. It is not clear how to operationalize the variable related to the degree of communally shared information. This is likely to be high in terms of deaf community specific information, but access to information about the wider community is often limited and inconsistent, as the provision of sign language interpreting and captioning on broadcast video is patchy in deaf communities.  Both these factors are leading to changing patterns of language transmission. Given only a minority of signers who have ASL, BSL, or Auslan as a first language from signing deaf parents (e.g., Fischer, 1978; Mitchell and Karchmer, 2004), many deaf adults thus acquire these sign languages from other deaf children in primary or secondary school, or in early adulthood in deaf clubs. Some of these deaf adults may not have fully acquired English, and thus may have learnt these sign language varieties as delayed first languages (e.g., Emmorey, 2002). In fact, together with hearing adult second language learners of ASL, Auslan, and BSL, nonnative deaf signers constitute the overwhelming majority of the signing community. Together with extensive exposure to spoken and written English, native signers are in constant contact with delayed first language and second language learners. This leads to a sociolinguistic situation that is quite unique, although with some similarities to pidgin language contact situations in which nobody is a native speaker of the variety being used to communicate across language barriers (cf. Fischer, 1978).

## MORPHOLOGICAL COMPLEXITY IN VILLAGE SIGN LANGUAGES

One might predict that the relatively more dense, stable environments of some micro-community sign languages, such as Kata Kolok, might provide an environment in which complexification is more likely to emerge. We need more research

to explore this claim (see Zeshan and De Vos, 2012), but there are some possible hints in the literature. For example, we see some possible complexification in the pronoun and verb systems in Kata Kolok, where the grammar exhibits distinctions in person and aspect marking (Trudgill's criterion 4, see above). While pointing signs are used for present referents, list buoys (where signers point to fingers on their non-dominant hand, often used to refer to a list of items, cf. Liddell, 2003a) are reportedly used for absent referents (De Vos, 2012). Both pointing signs and list buoys exist in other sign languages, but studies appear to suggest the use of these systems is allocated different grammatical functions categorically in Kata Kolok. Another example might be the emergence of a mouth gesture in Kata Kolok (closed mouth opening, resembling the syllable 'pah', see **Figure 4**) which cooccurs with manual verbs to indicate perfective aspect (De Vos, 2012). This is a type of aspect marking which represents an increase in morphological complexity (a similar mouth gesture has been identified in other sign languages, although it does not appear to have the same grammatical role). Perfective aspect marking in ASL, BSL, and Auslan, however, involves the grammaticalization of a manual lexical verb sign meaning 'finish' (e.g., Johnston et al., 2015). Therefore, it may be the case that micro-community sign languages provide more dense, stable environments compared to macro-community sign languages, and it is here that we might see some emergent complexification, but more detailed investigation needs to be undertaken.

## CONCLUSION AND FUTURE DIRECTIONS

In this article, we have briefly explored the idea that socio-cultural and other non-linguistic factors can contribute to linguistic diversity using Trudgill's (2011) framework of sociolinguistic typology, and we have discussed this proposal with regards to sign languages used by deaf communities for the first time. We have argued that the unique sociolinguistic situation

#### REFERENCES

and language transmission patterns of sign languages may contribute as a factor (in addition to the relative youth of sign languages) to explain their relative lack of morphological complexification. This conclusion is controversial since sign languages are sometimes presented as morphologically complex languages that present a puzzle for linguistic theory when their youth is taken into consideration. However, when we apply Trudgill's notion of linguistic complexity, as we have done here, a clearer picture of the nature of sign languages and their relationship to their sociolinguistic situation emerges. If Trudgill is correct, even considerably longer histories may not lead to morphological complexification in macro-community sign languages. In future, more research needs to be carried out on the specific sociolinguistic situation of sign languages, particularly with regards to the relative impact of social network density on these languages, as well as their youth and propensity for highly iconic structures (e.g., Cuxac and Sallandre, 2007).

#### AUTHOR CONTRIBUTIONS

AS, JF, KC, and TJ all made substantial contributions to the conception of the work and the interpretation of data. AS led on the writing of the paper, with JF, KC, and TJ all contributing to revising it critically for intellectual content and style. AS, JF, KC, and TJ all gave final approval of the version to be published and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

#### FUNDING

This work was supported by funding from the Economic and Social Research Council of Great Britain [Grants RES-620- 28-0002, Deafness, Cognition and Language Research Centre (DCAL) and ES/K003364/1].

and Methodologies, eds E. Pizzuto, P. Pietrandrea, and R. Simone (Berlin: Mouton de Gruyter), 13–33.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Schembri, Fenlon, Cormier and Johnston. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Blues in Two Different spanish-speaking Populations

*Fernando González-Perilli1,2\*, Ignacio Rebollo1 , Alejandro Maiche1 and Analía Arévalo3*

*1Centro de Investigación Básica en Psicología, Universidad de la República, Montevideo, Uruguay, 2 Facultad de Información y Comunicación, Universidad de la República, Montevideo, Uruguay, 3Departamento de Neurologia, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil*

Several studies investigating color discrimination across languages have shown a facilitation effect in groups that employ more than one term to refer to a given color. While Uruguayans use "*azul*" to refer to dark blue and "*celeste*" for light blue, Spaniards use "*azul*" for dark blue and the compound terms "*azul celeste*" or "*azul claro*" for light blue. In this study, Uruguayan and Spanish participants discriminated between pairs of color stimuli that lie at different distances from each other on the blue color spectrum in three different sessions: a session with no interference (basic task), one with verbal and one with visual interference. Only the Uruguayans were more accurate at distinguishing between stimuli associated with different color terms. Furthermore, while both Uruguayans and Spaniards showed a category effect in response times, the effect was strongest for Uruguayans when items were closer to each other on the color spectrum (i.e., more difficult). This study is unique in that we observed different Whorfian effects in two groups that speak the same language but differ in their use of color-specific terms. Our results contribute to the discussion of whether and to what extent language or other cultural variables affect the perception of different color categories.

#### *Edited by:*

*Steven Moran, University of Zurich, Switzerland*

#### *Reviewed by:*

*Laura J. Speed, Radboud University Nijmegen, Netherlands Jing Zhao, Capital Normal University, China*

*\*Correspondence: Fernando González-Perilli<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Language Sciences, a section of the journal Frontiers in Communication*

*Received: 30 May 2017 Accepted: 14 November 2017 Published: 05 December 2017*

#### *Citation:*

*González-Perilli F, Rebollo I, Maiche A and Arévalo A (2017) Blues in Two Different Spanish-Speaking Populations. Front. Commun. 2:18. doi: 10.3389/fcomm.2017.00018*

Keywords: color perception, categorical perception, linguistic relativity, Sapir–Whorf hypothesis, cross-cultural cognition

## INTRODUCTION

To what extent do language and/or culture affect the way we process and organize the information and experiences that make up our world? The work of Sapir, Whorf, and others sparked this famous debate at least a century ago, and these questions continue to interest academics across fields to this day (Whorf, 1956; Lucy and Shweder, 1979; Kay and Kempton, 1984; Vygotsky, 1987; Lupyan, 2012; Levelt, 2014).

Most investigations addressing this topic have been characterized as either descriptive, simply reporting interesting differences between two or more languages, or aiming to explain how observed disparities are associated with different cognitive processes (Zlatev and Blomberg, 2015). These two perspectives are also associated with weak and strong versions of the Sapir–Whorf hypothesis (language and thought are interrelated vs. language determines thought, Brown, 1976). Both hypotheses have been criticized for being trivial and non-informative (weak version) or theoretically and/or methodologically wrong (strong version) (Bloom and Keil, 2001).

Zlatev and Blomberg (2015) propose approaching each investigation according to whether the focus is on the structure of language or on its implementation (discourse). Traditional cognitive approaches focus on abstract structural aspects of language and search for innate universal features. On the other hand, linguistic relativism concentrates on how the phenomenon of categorical

**147**

perception (CP, Harnad, 2005) is affected by different contextual factors, such as language and culture.

According to Lucy (1997), there are three "logical components" that are typically taken into account when studying linguistic relativity: (1) the distinction between language and thought, (2) the mechanisms explaining the instantiation of a possible influence, and (3) the identification of other factors involved in the phenomenon.

Regarding the first point, relativists often agree with a broad definition of *thought*, not just as a conscious reflective process (as understood in folk psychology) but also involving less aware, automatic processes, such as perception and categorization. Moreover, language and perception are not understood as isolated modules—as in classic cognitivism (Pylyshyn, 1999)—but are thought to interact with a myriad of processes. Thus, the role of verbal labels affecting perception and categorization is a key issue in contemporary approaches (Thierry, 2016). How basic cognitive processes are influenced by implicit recovery of linguistic (but also contextual and sociocultural information) is another key question, which involves points 2 and 3.

Therefore, the key notion leading the research on linguistic relativity is not whether minds are dependent on a given language but how verbal labels and categories interact with cognition across different contexts (Thierry, 2016; Zhong et al., 2017). Topics currently being studied include: cross-cultural comparisons (i.e., Boroditsky, 2001; Casasanto, 2008), the exploration of categorical effects under different interference conditions (i.e., Roberson and Davidoff, 2000; Gilbert et al., 2006; Winawer et al., 2007), and the time course of the effect, which informs whether perception or higher cognitive processes are involved (Mo et al., 2011; Clifford et al., 2012; He et al., 2014; Forder et al., 2017).

One line of research within this debate concerns the way in which different languages divide color space. The key question within this work is whether these varying linguistic representations affect performance on tasks that are seemingly non-linguistic. In other words, does the way in which a particular language categorizes colors affect the way its speakers think about and organize color in their minds, even in the absence of an explicitly linguistic task? One special case—that of the color *blue*—has been studied by researchers across a number of languages, including Greek (Androulaki et al., 2006; Athanasopoulos, 2009; Thierry et al., 2009), Italian (Bimler and Uusküla, 2014), Japanese (Athanasopoulos et al., 2010), Korean (Roberson et al., 2009), and Russian (Witthoft et al., 2003; Winawer et al., 2007). These languages share a common feature that distinguishes them from English: they divide the color blue into two distinct linguistic categories, one depicting lighter blues, and the other depicting darker blues. In the above studies, speakers of those languages were relatively better than English speakers at distinguishing between color samples along the blue color spectrum when the samples' names came from different linguistic categories, even though the task did not require linguistic output.

This kind of implicit linguistic effect is explained by theories arguing that linguistic labels can aid in the discrimination of stimuli that are hard to categorize (Lupyan, 2012) thanks to a predicting coding process in which "every level of the hierarchically organized system that constitutes the brain works to predict the activity in the level below" (Lupyan and Clark, 2015, p. 279). In such a predictive framework, the brain's function is to produce a percept that fits the best hypothesis regarding the state of the world that is being conceived (Lupyan and Clark, 2015). That is acquired through an interplay of top-down knowledge about the world and incoming bottom-up sensory information (Bar, 2003). In Lupyan's view, labels work as hubs of perceptual, semantic and contextual information related to specific categories. Their function is to reduce prediction error by enhancing the perception of typical categorical features. Therefore, verbal labels can be elicited to foster predictability and support cognition.

Aiming to clarify this issue, several studies include a verbal interference condition. That is, they introduce a concurrent task demanding linguistic resources (e.g., remembering a string of digits). This interference is expected to disrupt categorical effects (advantage for the discrimination of stimuli pertaining to different categories) if linguistic processes are necessary for CP to occur. For instance, Winawer et al. (2007) showed that when an additional task requiring verbal memory was included, the categorical effects found for the Russian participants vanished, suggesting linguistic resources are used by Russian speakers in this seemingly non-linguistic color perception task. The authors also presented a spatial interference condition that did not alter categorical effects, further supporting the view that the a disruption of the CP advantages was in fact due to a disruption in linguistic processing and not to the heavier cognitive load imposed by any interference task.

In the current study, we compared two groups of speakers of the same language that employ different verbal labels for the same color. This comparison is interesting because, unlike previous studies where groups of speakers spoke different languages, differences between the current groups should be much subtler, and may reflect cultural variations that affect the frequency of use of such labels.

Similarly to the languages investigated in previous studies (Androulaki et al., 2006; Winawer et al., 2007), in some variants of Spanish, the color blue is associated with two different linguistic terms: dark blues are *azul* and light blues are *celeste*. However, the Spanish language presents an interesting case, in that different populations of Spanish speakers differ in the way they implement this distinction. Namely, in some South American countries such as Uruguay, the term *celeste* (light blue) is used on its own. By contrast, in Spain, the term "*celeste*" is used as part of a compound word, i.e., *azul celeste*, making *celeste* a subcategory within the larger category of *azul*, or (regular or dark) blue. The word (and color) *celeste* also carries significant cultural weight in Uruguay, given that it is found on national emblems and by extension, national sports team uniforms. A recent study conducted by our group confirmed the use of *celeste* as a separate basic color term (BCT) for light blues in Uruguay. Thirty healthy participants were given 2 min to write down as many color names as they could remember while keeping their eyes closed (Elicited List task: Corbett and Davies, 1997). Following Berlin and Kay's (Berlin and Kay, 1969) work, one would predict that only 11 different color names would be elicited in more than 50% of the lists produced by participants. In this study, however,

Uruguayan participants consistently produced 12 names, as they included *celeste* as its own color category. In fact, both *azul* and *celeste* were consistently found among the first BCTs reported by Uruguayans (Lillo et al., 2016).

For the current experiment, we tested Uruguayan as well as Spanish participants on a color discrimination task we designed using stimuli along the *azul-celeste* boundary. Since cultural as well as linguistic differences have been used to explain Whorfian effects across different populations, the Uruguay-Spain comparison is interesting because the two populations come from different cultures but use the same language and very similar color space partitions. That is, when asked to assign segments of the color spectrum to different color terms, Uruguayans and Spaniards coincide perfectly on all terms except for *celeste*: the space Uruguayans call "*celeste*" falls into the greater category of "*azul*" for Spaniards (Lillo et al., 2016). Given the presence of the 12th BCT for the Uruguayans, we hypothesized that this group would display a relatively stronger categorical advantage than Spaniards.

### MATERIALS AND METHODS

#### Participants

A total of 73 individuals participated in this study: 35 were recruited from the Universitat Autònoma de Barcelona, Spain, and 38 were recruited from the Universidad de la República in Montevideo, Uruguay. All of them were native speakers of the Spanish spoken in their country, and 22 of the Spanish participants were also Catalan speakers. Nine participants (2 from Spain and 7 from Uruguay) who produced more than 25% errors and RTs < 200 and >3,000 ms were excluded from the analysis, for a final group of 33 Spaniards (mean age = 25.1, SD = 3; 18 female) and 31 Uruguayans (mean age = 22.5, SD = 3.2; 17 female). Groups did not differ significantly from each other in terms of gender or age [*F*(1,62) = 0.802, *p* = 0.374].

#### Stimuli

We created 20 computer-simulated color chips that ranged from light blue (*azul celeste* in Spain and *celeste* in Uruguay) to dark blue (*azul oscuro* in Spain and *azul* in Uruguay) (**Figure 1**). Stimuli coordinates (Commission Internationale de l'Eclairage, *Yxy*) ranged from *Y* = 29.26, *x* = 0.217, *y* = 0.274 for stimulus 1 to *Y* = 4.18, *x* = 0.182, *y* = 0.167 for stimulus 20. Stimuli varied primarily in the luminance axis (*Y*) and the *y* chromaticity axis, and were selected taking into account previous research on color categories in Spanish (Lillo et al., 2007) as well as cross-linguistic comparisons (Winawer et al., 2007; Roberson et al., 2009). The color squares measured 2.5 cm per side, and subjects viewed the screen from a distance of 60 cm. In addition, there were two categories of deviant stimuli: near and far. "Near" stimuli were colors that were two chips away from the target stimulus while "far" stimuli were four chips away (**Figure 2**). Discrimination between "near" stimuli was expected to be more difficult than between "far" stimuli.

#### Procedure

Prior to participation, an investigator explained the study to participants, who then signed an informed consent form. All study procedures were conducted with the approval of the Research Ethics Committee of the Department of González-Perilli et al. Blues in Two Different Spanish-Speaking Populations

Psychology at University of the Republic (Uruguay) and the Department of Basic Psychology at the Autonomous University of Barcelona (a separate ethics approval was not required as per the Autonomous University of Barcelona guidelines and as per Spanish regulations) and were in accordance with the Declaration of Helsinki. Participants viewed three color squares arranged in triads (1 above and 2 below) (**Figure 1**) and were asked to decide which of the two lower squares matched the one on top. The side (right or left) on which the distractor was presented was counterbalanced across trials. Each participant completed three blocks of 136 color discrimination trials: one regular block (Basic Task), one block that also included a secondary spatial interference task, and a third block that included a verbal interference task. Half of the comparisons included "near" stimuli and half included "far" stimuli. The two interference tasks (one verbal and one spatial) were included, following Winawer et al. (2007), to test whether either type of interference affected any observed categorical effects, thus shedding light on the type of processing employed by participants during the basic task.

#### Interference Tasks

#### Participants' Boundaries

Following the categorization tasks, participants also completed a *Border detection task* designed to test each individual's color boundary between dark and light blues. Participants viewed the 20 stimuli (which appeared 10 times and in random order) and pressed a key to indicate whether each color was *celeste* or *azul* (for Uruguayans) and *azul celeste* or *azul oscuro* (for Spaniards). They were asked to make all judgments as quickly and accurately as possible.

Overall, 36% of participants identified Stimulus 10 as the categorical boundary, 24% chose Stimulus 9, 20% chose Stimulus 8, 14% chose Stimulus 11, and 6% chose Stimulus 7. All Uruguayans categorized Stimulus 1 as *celeste* (light blue) and stimulus 20 as *azul* (dark blue), while all Spanish participants categorized Stimulus 1 as *azul celeste* (sky blue) or *azul claro* (light blue) and Stimulus 20 as *azul oscuro* (dark blue). Each participant's score was determined individually by using his/her color boundary to classify the color discrimination trials as either cross-category or within-category. This classification was made individually (i.e., not based on the group average).

#### Errors and Outliers

In order that we only analyzed data from trials in which participants were actively following the interference tasks, we systematically discarded all eight color trials preceding each incorrectly answered interference trial (5.74% of trials).

We also eliminated all trials with reaction times below 200 or above 3,000 ms (2.41% of trials across participants). RT analyses were conducted only on accurate responses (87.5%).

## RESULTS

We conducted a mixed ANOVA with three within-subject factors (Distance × Interference × Category) and one between-subjects factor (country: Uruguay vs. Spain).

#### Accuracy

Groups did not differ in terms of overall accuracy: Uruguay (M = 86.1, SD = 0.61) vs. Spain (M = 88.3, SD = 0.63), *F*(1, 62) = 1.942, *p* = 0.168, η2 = 0.030.

There were two significant main effects: Distance, *F*(1,62) = 303.109, *p* < 0.0001, η2 = 0.830, and Category, *F*(1,62) = 5.845, *p* = 0.01, η2 = 0.086. When analyzed together, participants were more accurate at distinguishing between far trials (M = 0.94, SD = 0.04) than between near trials (M = 0.80, SD = 0.09), and between cross-category trials (M = 0.87, SD = 0.07) than between within-category trials (M = 0.86, SD = 0.06). There were also three significant interactions: Interference × Country, Distance × Country and, most interestingly, Category × Country.

Interference × Country, *F*(1, 62) = 3.219, *p* = 0.043, η2 = 0.049. *Post hoc* analyses showed that the interference factor was not significant when analyzed separately for each group,

Figure 3 | Example of stimuli employed in the spatial interference block (left), and in the verbal interference block (right).

and that the difference between groups was significant only in the verbal interference condition, *F*(1,62) = 2.304, *p* = 0.025, *d* = 0.4.

Distance × Country, *F*(1, 62) = 4.252, *p* = 0.043, η2 = 0.064. Uruguayans had relatively greater difficulty discriminating between near stimuli (near: M = 0.78, SD = 0.13; far: M = 0.94, SD = 0.06) than did Spaniards (near: M = 0.82, SD = 0.13; far: M = 0.95, SD = 0.06).

*Post hoc* analyses (separate one-way ANOVAs for each group) showed that distance effects were significant for both countries, Uruguay.

*F*(1, 30) = 157.375, *p* < 0.0001, η2 = 0.840., Spain: *F*(1, 32) = 145.353, *p* < 0.0001, η2 = 0.820. Moreover, pairwise comparisons showed that neither near nor far cases showed differences between countries (*p* > 0.05).

Category × Country, *F*(1,62) = 2.123, *p* = 0.19, η2 = 0.086. Uruguayans showed an advantage for cross-category trials compared to within category trials (M = 0.87, SD = 0.07 vs. M = 0.85, SD = 0.07); *post hoc* analyses: *t*(1,30) = 3.268, *p* = 0.003, *d* = 0.29. Spaniards, on the other hand, did not show this advantage (within: M = 0.88, SD = 0.06, cross: M = 0.88, SD = 0.07), *p* > 0.05 (see **Figure 4**). All other effects and interactions were not significant (all *p* > 0.05).

#### RT

Overall, Uruguayans were significantly slower than Spaniards, *F*(1,62) = 8.196, *p* = 0.006, η2 = 0.117 (M = 1043 ms, SD = 278 ms vs. M = 900 ms, SD = 287 ms). There were also significant main effects of Distance, *F*(1,62) = 267.638, *p* < 0.0001, η2 = 0.812, and Category, *F*(1,62) = 27.331, *p* < 0.0001, η2 = 0.306.

In line with the accuracy results, participants were faster at discriminating between far trials (M = 862 ms, SD = 175 ms) than near ones (M = 1,081 ms, SD = 235 ms), and on cross-category (M = 952 ms, SD = 206) compared to within-category trials (M = 991 ms, SD = 198 ms) (see **Figure 5**).

The first-order interaction of Interference × Country was significant, *F*(1,61) = 3.517, *p* = 0.033, η2 = 0.054. Sessions with spatial interference, in which Uruguayans performed best, resulted in the Spanish group's slowest responses (Spain: Basic: M = 889, SD = 336; Spatial: M = 941, SD = 328; Verbal: M = 869, SD = 343; Uruguay: Basic: M = 1087, SD = 347: Spatial: M = 994, SD = 342; Verbal: M = 1048, SD = 354).

*Post hoc* analyses showed that differences across sessions were not significant within countries, but results comparing Spain and Uruguay were different for two of the three interference conditions. Differences between groups were significant in the Basic (no interference) session, *t*(1,62) = 3.271, *p* = 0.002, *d* = 0.58, and in the Verbal interference session, *t*(1,62) = 2.895, *p* = 0.005, *d* = 0.51.

Distance × Category, *F*(1,62) = 3.769, *p* = 0.019, η2 = 0.085. A category advantage (difference between cross- and withincategory trials) was stronger for far (Mdifference = 54 ms) than for near color comparisons (Mdifference = 25 ms).

Nevertheless, *post hoc* analyses reflected that both differences were significant: Far, *F*(1,63) = 3.769, *p* = 0.003, η2 = 0.129; Near, *F*(1,63) = 3.769, *p* = 0.000, η2 = 0.257. Additionally, categorical effects were significant at both distance conditions.

Cross-category: *F*(1,62) = 27.811, *p* = 0.000, η2 = 0.310; withincategory: *F*(1,62) = 62.927, *p* = 0.000, η2 = 0.504.

While the Category × Country interaction was not significant (*p* = 0.090), the three-way Country × Distance × Category interaction was, *F*(1,62) = 6.596, *p* = 0.013, η2 = 0.096. Uruguayans showed a stronger categorical effect on near trials than on far trials.

Separate two-way ANOVAs conducted for each group showed that the interaction between distance and category was significant for Uruguayans, *F*(1, 30) = 11.041, *p* = 002, η2 = 0.269, but not for Spaniards, *F*(1, 32) = 0.635, *p* = 0.902. η2 = 0.00. For the Uruguayan group, RTs were faster for near cross-category trials than near within-category trials (M = 1112 ms, SD = 238 ms vs. M = 1193 ms, SD = 231 ms); *post hoc* analyses were significant: *t*(1, 30) = 5.312, *p* < 0.0001, *d* = 0.34, while far cross-category trials did not differ significantly from far within-category trials (M = 922 ms, SD = 194 ms: vs. M = 944 ms, SD = 198 ms; *post hoc* analyses: *p* > 0.05) (see **Figures 5** and **6**).

*Post hoc* analyses also showed that categorical differences between countries were significant for near trials, *F*(1,62) = 6.852, *p* = 0.011, η2 = 0.100, but not for far ones, *p* > 0.05.

Interestingly, a non-significant difference was observed for categorical effects between countries in the different interference conditions (country by category by interference, *p* = 0.059). We calculated the differences between cross- and within-category trials to obtain a categorical effect score. Categorical effect size was greater for Uruguayans (68 ms) than Spaniards (10 ms) in the basic condition [pos hoc: *F*(1,62) = 6.089, *p* = 0.016, *e* = 0.089], more similar between groups in the spatial condition [56 vs. 24; *F*(1,62) = 1.513, *p* = 0.223, *e* = 0.024] and almost equal between groups in the verbal interference condition [30 vs. 43; *F*(1,62) = 0.407, *p* = 0.526, *e* = 0.007] (see **Figure 7**).

In sum, participants were faster and more accurate when discriminating between far stimuli than near stimuli and when stimuli pertained to different categories. Uruguayans were slower than Spaniards overall, less accurate and slower in the verbal interference condition, and slower in the no interference condition. Also, Uruguayans were less accurate than Spaniards at discriminating between near stimuli. The Uruguayan group showed more categorical effects in terms of accuracy, while both groups showed stronger categorical effects for near cases in terms of RT (with Uruguayans displaying significantly stronger effects). Finally, there was a non-significant trend for differences in the effects of verbal interference on categorical effects between groups for RT.

### DISCUSSION

The current study supports the Whorfian notion that language can influence color perception and is unique in that we were able to show differences in categorical effects in two groups of participants who speak the same language. Specifically, we found that Uruguayans, who have distinct color terms for light and dark blue, were more sensitive to color boundaries than Spaniards, who use a single color term for dark blue and two different compound terms for light blue. We also observed that a less frequent non-BCT—*azul celeste*—yielded some categorical facilitation. In this study, while both groups presented categorical effects in RT, the effect was strongest for Uruguayans on the more difficult "near" trials. Furthermore, only the Uruguayans were significantly more accurate at cross-category comparisons.

In contrast to previous studies where the color categories employed by the two populations clearly distinguished between dark and light blues (e.g., Russian and American participants in Winawer et al., 2007), one of the compound terms for light blue used by Spaniards (*azul celeste*) contains the monolexemic term (*celeste*) used by Uruguayans. From Lillo et al. (2016), we know that Spaniards do not consider "*celeste*" or "*azul celeste*" as a 12th BCT, as Uruguayans do, which may explain the weaker categorical effects observed among Spaniards relative to Uruguayans. Furthermore, as mentioned above, "*celeste*" is particularly salient in Uruguayan Spanish for cultural reasons, and may therefore appear more frequently for this population. According to several authors, the degree of exposure to color categories correlates with the strength of categorical effects in color discrimination tasks (Witthoft et al., 2003; Thierry et al., 2009; Athanasopoulos et al., 2011). An interesting future study would be to test category effects with a monolexemic color term whose frequency of use differs between two populations that speak the same language.

Importantly, several studies have shown that categorical effects on perception can be elicited by newly learned categories (Zhou et al., 2010; Clifford et al., 2012). In Zhou et al. (2010), participants who learned two new categories depicting light and dark shades of blue showed a categorical advantage compared with a control group, suggesting that the introduction of a novel verbal label can affect CP.

In Winawer et al. (2007), verbal interference disrupted CP for Russian but not for English speakers, suggesting a key role of language in CP (Roberson and Davidoff, 2000; Gilbert et al., 2006; Winawer et al., 2007). The results of the present study suggest that category saliency may also be affected by cultural factors.

Although the effect did not reach significance, we also observed that verbal interference diminished the categorical effect in Uruguayans and increased it in Spaniards (see **Figure 7**), which suggests CP effects are affected by linguistic input. Interestingly, Spaniards showed greater CP during the verbal interference block, suggesting the recruitment of the verbal label "*azul*" was inhibited. As shown by the *Stroop* effect (Stroop, 1935), automatic elicitation of a verbal label can interfere with color discrimination. Arguably, the discrimination between stimuli representing dark and light blues would benefit from the inhibition of the verbal label "*azul*" linked to the Spaniards' main blue category. Thus, further work is needed to clarify this issue. If replicated, it would be an unusual finding that has not been reported for English speakers in previous cross-cultural studies.

It should be noted that because part of our study was conducted in Barcelona, some of our Spanish participants also spoke Catalan, which uses "*blau cel*" as a term for light blue. We have not studied "*blau cel*" or Catalan speakers specifically, so we cannot say whether this term is more similar to any of the terms used by Spaniards in Spanish or by Uruguayans. In order to exclude this variable as a possible confound, we conducted an additional ANOVA comparing the subset of Catalan-speaking Spaniards (*n* = 18) to the non-Catalan-speaking Spaniards (*n* = 15) and found that groups did not differ on any of the variables or interactions of interest.

A recent interpretation of Whorfian effects (proposed more than 100 years ago by William James; James, 1890) is called the *Label feedback hypothesis* (Lupyan, 2008, 2012), which proposes that labels (i.e., words) are automatically recovered to solve difficult discrimination cases and are recruited unconsciously when an object is perceived in order to highlight characteristic features and thus assist in the categorization process.

Furthermore, recent studies have revealed that neural networks of color perception show strong connections between basic visual areas V1 and V4 and inferotemporal and nearby regions associated with categorization (Walsh, 1999; Roe et al., 2012; Gilbert and Li, 2013; Simanova et al., 2015; Winawer and Witthoft, 2015). Moreover, an fMRI study showed activation of language regions during color perception, supporting the notion of an interaction between higher level cognition and perceptual processes (Siok et al., 2009; Brouwer and Heeger, 2013).

In the present study, perceptual processes seemed to benefit from the words' referential attributes, but the effect differed between Spanish-speaking groups. This suggests that the interplay between categorization and perception only partially depends on a particular language's structure (Ozgen and Davies, 2002; Harnad, 2005; Lupyan et al., 2007; Collins and Olson, 2014).

An alternative interpretation is that perception could be driven by cultural—and not just linguistic—influences. In fact, cultural differences in speakers of the same language may even be the driving force behind the creation of different linguistic terms. The Emergence Hypothesis for BCTs (Kay and Maffi, 1999) proposes an explanation for how BCTs have evolved in different cultures. Kay and McDaniel (1978) suggest that derived categories are a fuzzy set of intersections among primary terms. According to this view, the emergence of a new category denoting a light shade of blue would be the result of the intersection between the blue and white categories, as Androulaki et al. (2006) proposed for Greek. Exactly why a language would add a new BCT is not clear. Casson (1997) proposed that a society's technological development will increase the importance of color as a distinguishing property of objects. Paramei (2005) and Steels and Belpaeme (2005) agree that cultural and social factors are key in the development of color lexicons. Such constraints imply that color names map onto color appearances in a culturally modal pattern (Frumkina, 1999; Jameson, 2005) and, in certain languages, could emerge as culturally basic.

Probably the main debate in linguistic relativity is whether CP occurs early on (during stimulus perception; Notman et al., 2005; Lupyan, 2012) or at the time a response is given (affecting post-perceptual processes; e.g., Pinker, 1995; Li and Gleitman, 2002). This question has been investigated using ERP, with studies showing early (Fonteneau and Davidoff, 2007; Thierry et al., 2009; Clifford et al., 2010; Mo et al., 2011; Forder et al., 2017), post perceptual (Clifford et al., 2012; He et al., 2014; Witzel and Gegenfurtner, 2016) and both effects (Holmes et al., 2009). This suggests that a strictly linguistic theory of CP is at best incomplete.

One unexpected result in the current study was that Uruguayans were both most accurate and fastest at the spatial interference block, relative to the other two blocks. One possible interpretation for this is that unlike verbal interference, spatial interference had a minimal effect on performance on a task where verbal aspects were critical, and that the added challenge resulted in higher accuracy. This would not, however, explain why that interference block would result in better accuracy than the block with no interference. We do not have enough data to answer this question at the moment but will investigate it in future studies.

Another interesting but not totally unexpected finding was that overall, Uruguayans gave slower responses than Spaniards. As observed by previous investigators, this may reflect differences in groups' experience as study participants (Witthoft et al., 2003; Winawer et al., 2007; Witzel and Gegenfurtner, 2015). In the present study, while both groups were recruited within university psychology departments, the Spanish group was generally more familiar with psychophysical experiments than the Uruguayan group. In order to ensure that categorical effects across groups were not related to overall RT, additional analyses were performed on the subset (50%) of Uruguayans with the fastest responses. Results confirmed the trends observed for the whole group.

To conclude, color terms (both monolexemic and compound) carry different degrees of enhanced frequency and saliency within a linguistic community, which in turn depend on social, cultural, and historical factors (see Berlin and Kay, 1969; Casson, 1997; Kay and Maffi, 1999; Paramei, 2005, but also see Saunders, 2000). The present work shows that these differences can lead to different CP effects across groups that speak the same language.

### ETHICS STATEMENT

This study was carried out in accordance with the recommendations of University of the Republic and Autonomous University of Barcelona ethics committees with written informed consent from all subjects. All subjects gave written informed consent in

### REFERENCES

Fonteneau, E., and Davidoff, J. (2007). Neural correlates of colour categories. *Neuroreport* 18, 1323–1327. doi:10.1097/WNR.0b013e3282c48c33

accordance with the Declaration of Helsinki. The protocol was approved by the University of the Republic ethics committee.

## AUTHOR CONTRIBUTIONS

AA and FG-P conceived the study which was designed with the collaboration of IR and AM. IR and FG-P carried out the experiments and the analyses were conducted by AA, IR, and FG-P. All the authors contributed to the writing of the article.

## FUNDING

FG-P received support from PRODIC (Programa de Desarrollo Académico de la Información y la Comunicación, FIC-UDELAR). FG-P and AM received support from CICEA (Interdisciplinary Cognition Center for Teaching and Learning - UDELAR).

Forder, L., He, X., and Franklin, A. (2017). Colour categories are reflected in sensory stages of colour perception when stimulus issues are resolved. *PLoS ONE* 12:e0178097. doi:10.1371/journal.pone.0178097

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 González-Perilli, Rebollo, Maiche and Arévalo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Linking Adult Second Language Learning and Diachronic Change: A Cautionary Note

Vera Kempe1 \* and Patricia J. Brooks 2

1 School of Social and Health Sciences, Abertay University, Dundee, United Kingdom, 2 College of Staten Island and The Graduate Center, City University of New York, Brooklyn, NY, United States

Keywords: linguistic niche hypothesis, first language acquisition, second language learning, inflectional morphology, case marking

#### Edited by:

Steven Moran, Universität Zürich, Switzerland

#### Reviewed by:

Olga Feher, University of Warwick, United Kingdom Eva Belke, Ruhr University Bochum, Germany Sean Roberts, University of Bristol, United Kingdom

> \*Correspondence: Vera Kempe<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

Received: 29 November 2017 Accepted: 21 March 2018 Published: 05 April 2018

#### Citation:

Kempe V and Brooks PJ (2018) Linking Adult Second Language Learning and Diachronic Change: A Cautionary Note. Front. Psychol. 9:480. doi: 10.3389/fpsyg.2018.00480 Traditionally, diachronic language change has been attributed to intra-linguistic factors, which, in analogy to genetic drift, result in diversification of languages as a consequence of the social and geographical separation of linguistic communities (Lupyan and Dale, 2016). More recently, extra-linguistic factors have been implicated in language change as languages adapt to ecological niches formed by geographic, demographic, and cultural characteristics of social environments (Dale and Lupyan, 2012; Reali et al., 2018). One way of conceptualizing these extra-linguistic factors is to distinguish linguistic communities along a continuum of variation in population size, geographical spread, and amount of contact with other languages: Inward-facing, esoteric communities have small populations with shared knowledge and little language contact whereas outward-facing, exoteric communities have large populations, assembled into diverse social networks with substantial amounts of non-shared knowledge and contact with other languages (Thurston, 1987; Wray and Grace, 2007).

According to the Linguistic Niche Hypothesis (Lupyan and Dale, 2010; LNH: Dale and Lupyan, 2012), larger proportions of non-native speakers in exoteric communities promote morphological simplification of the majority language. This is thought to occur because simplifying adjustments to non-native interlocutors produced by native speakers (Little, 2011) or linguistic forms better adapted to learning constraints of adult second-language (L2) learners are adopted and transmitted to subsequent generations. Support for this hypothesis comes from qualitative (McWhorter, 2007; Trudgill, 2011) and quantitative (Szmrecsanyi and Kortmann, 2009; Lupyan and Dale, 2010; Bentz and Winter, 2013) analyses suggesting a negative correlation between the proportion of L2-learners in a linguistic community and the morphological complexity of the majority language (but see Nichols, 1992; Atkinson et al., 2016, for failures to observe this link). Below we evaluate evidence for this proposal, consider an alternative, and suggest directions for future research.

Adult language-learners differ from children in terms of prior real-world knowledge and literacy levels. Such differences allow adults to map L2s onto fully developed conceptual and linguistic representations, and may render them oblivious to aspects of morpho-syntactic structure that are not present in their L1, especially if not underpinned by awareness gained through literacy (Tarone et al., 2007). Adults and children also differ in learning mechanisms: Children rely on procedural memory whereas adults utilize declarative memory, at least in the initial stages of L2 grammar learning (Hamrick et al., 2018). Finally, relative to adults, children's cognitive limitations restrict their ability to consider contextual and referential information (Trueswell et al., 1999; Snedeker and Trueswell, 2004; Weighall, 2008). Nettle (2012) has conjectured that, as a result, children might benefit more than adults from over-specification afforded by redundant cues in complex morphological systems. Indeed, for at least one esoteric language, Choguita Rarámuri, processing benefits have been observed from redundant morphological marking in situations where meanings of constructions are difficult to recognize (Caballero and Kapatsinski, 2015); however, evidence that benefits from over-specification are amplified in children is lacking. To test how learning and processing differences between adults and children shape morphology, the LNH has operationalized morphological complexity through estimates of the amount of morphologically-marked grammatical features and bound morphemes marking those features (Lupyan and Dale, 2010; Bentz and Winter, 2013).

Yet how strong is the evidence that children's cognitive limitations support learning of complex morphology? According to the "Less-Is-More" hypothesis (Newport, 1990), limited processing capacity focuses children's attention on smaller chunks of the input, facilitating its decomposition into sublexical units, such as inflectional affixes, and the mapping of these units onto grammatical features. Adults, in contrast, tend to process larger chunks of input, which may prevent them from noticing fine-grained variation crucial for learning inflectional morphology. Evidence for adults' limited decomposition ability has mainly been obtained from studies comparing the processing of regularly inflected vs. irregular forms (e.g., English past-tense verbs, German past participles). Evidence from priming and ERP studies suggests that native speakers rapidly decompose inflected regular forms into constituent stems and affixes, whereas adult L2-learners treat both regular and irregular forms as unanalyzed wholes (Clahsen et al., 2010), presumably because their initial reliance on declarative memory taxes cognitive resources and thus constrains the complexity of what can be learned (McDonald, 2006; Hamrick et al., 2018). Morphological complexity is also thought to impose a burden on production because it commits speakers to engage in additional "thinking for speaking," i.e., the obligatory encoding of information that may go beyond their immediate communicative intentions (Slobin, 1996, 2003). However, direct empirical support for the idea that cognitive limitations confer advantages for learning complex morphology is lacking: First, we know of no study that has directly compared children vs. adults in their tendency to decompose unfamiliar pseudo-linguistic stimuli. Second, neither connectionist models that varied memory capacity (Elman, 1993) nor experimental studies that imposed concurrent cognitive load on adult language learners (Cochran et al., 1999) yielded unequivocal and replicable evidence for superior decomposition or faster learning of morpho-syntax as a consequence of processing capacity limitations (Rohde and Plaut, 1999, 2003). There is to date no convincing evidence that cognitive limitations benefit input decomposition as an aid to morphology learning.

A related proposal attributes children's language-learning advantage to limitations in cognitive control (Thompson-Schill et al., 2009; Chrysikou et al., 2011). When exposed to artificial languages with competing variants of free morphemes distributed in unpredictable ways, children typically regularize the input by dropping less frequent variants, whereas adults tend to probability-match, i.e., to reproduce the statistical distribution of morpheme variants in the input (Hudson Kam and Newport, 2005). Such results suggest that children's inability to inhibit pre-potent responses may lead to regularization of unpredictable variation of the type encountered in pidgins. However, direct attempts to induce regularization in adults by imposing concurrent cognitive load have been unsuccessful (Perfors, 2012), suggesting that regularization is not a consequence of limitations in processing capacity and executive control, but rather a strategic response (Perfors, 2016). Additionally, while children's propensity to regularize may play an important role in creolization, it is unclear how it could facilitate morphology acquisition in natural languages, given that morphological structure has evolved to be quasi-regular and largely predictable (Kirby et al., 2015). If children were to regularize complex morphological systems, this would lead to neutralization of features and erosion of morphological contrasts—a prediction that contradicts the idea that children drive morphological complexity. Adults, on the other hand, regularize only at much higher levels of complexity, and only when variation is truly unpredictable, but not when it resembles the lexicallyconditioned morphological variation of natural languages (Hudson Kam and Newport, 2009). This and other evidence that adults are quite capable of learning complex morphological systems, adopt similar learning strategies as children, and may even often outperform children in controlled experimental studies (Braine et al., 1990; Brooks et al., 1993; Wonnacott et al., 2008; Wonnacott, 2011) is difficult to reconcile with the idea that non-native speakers of a language are responsible for the erosion of its morphological complexity. Moreover, for simpler morphological patterns to become established in a language, the changes must be adopted by the next generation of L1-speakers. Although the children of non-native speakers may regularize their parents' unpredictable input, this process may not yield a less complex system, as documented in case studies of children acquiring sign language (Singleton and Newport, 2004).

Other accounts have emphasized that the morphological features of languages used by esoteric communities are idiosyncratic, low in compositionality, and replete with irregularities and formulaic expressions (Wray and Grace, 2007). Such systems arise because members of esoteric communities share a great extent of knowledge, which enables them to use contextual cues to discern utterance meanings and leave the linguistic expressions themselves more ambiguous. While this view would be compatible with the general idea of language adapting to a sociocultural niche, it is at odds with the idea that redundant marking of grammatical features by bound morphemes is the relevant characteristic of esoteric communication (Lupyan and Dale, 2010). Instead, it leads to an alternative prediction: that morphological systems acquired predominantly by children should be more idiosyncratic and less transparent than the regular, transparent, and compositional morphological systems preferred by adult L2-learners. This alternative aligns with evidence of children's propensity to learn from larger, unanalyzed chunks (Peters, 1983; Pine and Lieven, 1993)—a proposal contradicting Newport's (1990) version of the "Less-Is-More" hypothesis. Indeed, recent evidence (Arnon and Christiansen, 2017; Arnon et al., 2017) suggests that due to limited processing capacity and lack of conceptual knowledge, children may under-segment the input and form representations of multi-word utterances along with their constituent components. Such concurrent representations enable children to harness predictive information inherent in the constituent components, which benefits learning of adjacent dependencies such as Spanish determiner-noun gender agreement (Arnon and Ramscar, 2012) or Chinese classifier-noun associations (Paul and Grüter, 2016). Even if adults form representations of multi-word utterances through chunking, their already existing conceptual knowledge may lead them to miss out on the predictive information from free morphemes contained in these utterances, focussing instead on the mapping of novel L2 content words onto existing concepts. However, while children's learning from larger, only partially decomposed multi-word utterances can explain acquisition of grammatical features marked by predictive free morphemes (e.g., determiners, prepositions), it does not explain the acquisition of bound morphemes, which are at the heart of the LNH.

A possible way to reconcile the different conceptualizations of how esoteric vs. exoteric communication affects language change is to acknowledge that transparency and complexity of morphological systems are orthogonal dimensions that jointly affect learnability, irrespective of whether instantiated by bound or free morphemes. Consider the following example: German nominal morphology comprises free (determiners) and bound (suffixes) morphemes marking number (singular, plural), gender (masculine, feminine, neuter) and case (nominative, genitive, dative, accusative), yet a considerable degree of neutralization and inflectional syncretism in its declension paradigm renders case markers fairly non-transparent and uninformative. In contrast, Russian nominal inflections are considerably more complex, with suffixes varying according to number (singular, plural), gender (masculine, feminine, neuter; with further inflectional variation for several nominal subclasses), and case (nominative, genitive, dative, accusative, instrumental, locative), yet the degree of neutralization and inflectional syncretism is substantially lower, rendering case markers more transparent and informative. If complexity is the main obstacle for adults learning nominal morphology, then L2-learners should exhibit greater difficulty with Russian than with German. If, however, lack of transparency poses the challenge, then German should be more difficult for L2-learners. Comprehension tasks comparing adult learners of Russian and German with comparable levels of L2-proficiency revealed that L2-learners of Russian processed case markers much more efficiently than L2 learners of German (Kempe and MacWhinney, 1998). When potential confounds between different L2s were controlled by manipulating features of morphological systems within

#### REFERENCES

languages, native English speakers who learned Russian case inflections for transparently gender-marked nouns progressed much faster than those who learned inflections for nontransparently gender-marked nouns, even though the two subsystems were of comparable complexity (Kempe and Brooks, 2008). These findings align with evidence that learners are biased toward morphological systems that maximize communicative efficiency (Fedzechkina et al., 2012) and suggest that conceptualizations of morphological complexity need to consider the informativeness of morphemes as cues to underlying syntactic and semantic structure (Bates and MacWhinney, 1989)—an approach compatible with connectionist (Kempe and MacWhinney, 1998, 1999; Mirkovic et al., 2011 ´ ) and information-theoretical approaches to learning and processing of inflectional morphology (Milin et al., 2009).

To provide more stringent tests of the role of child vs. adult learners as drivers of morphological change, a cognitively-grounded typology of informativeness—obtained through quantitative approaches—is needed, for example, using connectionist or deep-learning algorithms that estimate strength of association between morphological markers and thematic roles from morphologically-tagged language corpora, or inferential algorithms that operate on probability distributions of markers over thematic roles in analogy to what has been suggested for semantic typology (Kemp et al., 2018). Such estimates should be integrated with findings from cross-linguistic studies of how adults and children learn and process different morphological systems to complement existing models of learner biases in terms of exposure (Bentz and Berdicevskis, 2016) or preference for regularization (Cuskley et al., 2017), while taking into account more subtle differences in children's cognitive and pragmatic capacities. We expect especially strong insights to be gained from amplification of adult vs. child biases during transmission of language in iterated learning studies. Initial forays into this line of inquiry indicate that compositional morpho-syntax emerges more readily when systems are learned and transmitted by adults than by children (Flaherty and Kirby, 2008; Raviv and Arnon, 2016), suggesting that at present questions about how languages adapt to different learnability constraints imposed by children and adults are far from settled.

### AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication. The authors developed the ideas jointly and collaborated in writing the article.

Arnon, I., and Ramscar, M. (2012). Granularity and the acquisition of grammatical gender: how order-of-acquisition affects what gets learned. Cognition 122, 292–305. doi: 10.1016/j.cognition.2011.10.009

Atkinson, M., Smith, K., and Kirby, S. (2016). "Adult language learning and the evolution of linguistic complexity," in The Evolution of Language: Proceedings of the 11th International Conference (EVOLANG11), eds S. G. Roberts, C. Cuskley, L. McCrohon, L. Barceló-Coblijn, O. Fehér, and T. Verhoef (New Orleans, LA). doi: 10.17617/2.2248195

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Kempe and Brooks. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Recursive Combination Has Adaptability in Diversifiability of Production and Material Culture

Genta Toya\* and Takashi Hashimoto

School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Japan

It has been suggested that hierarchically structured symbols, a remarkable feature of human language, are produced via the operation of recursive combination. Recursive combination is frequently observed in human behavior, not only in language but also in action sequences, mind-reading, technology, etc. in contrast, it is rarely observed in animals. Why is it that only humans use this operation? What is the adaptability of recursive combination? We aim (1) to identify the environmental feature(s) in which recursive combination is effective for survival and reproduction, and that has facilitated the evolution of this ability, and (2) to demonstrate the possible evolutionary processes of recursive combination. To achieve this, we constructed an evolutionary simulation of agents that generated products using recursive combination and used the results to explore the types of fitness functions (that reflect the kinds of adaptive environments) that give rise to this ability. We identified two types of adaptability of the recursive combination: (1) diversifiability of production and (2) diversifiability of products. Through the former, recursive combination promotes robustness against failure of production caused by inaccurate manipulations or irreversible changes. In an environment in which diversified products are preferable, sharing a portion of the production process for these products entails producing multiple products in which recursive combination plays a key role. We suppose that recursive combination works as a driving force of material culture. Finally, we discuss the possible evolutionary scenarios of recursive combination that is later generalized to encompass many aspects of human cognition, including human language.

Keywords: recursive combination, hierarchical structure, evolutionary simulation, action grammar, evolutionary linguistics, tool manufacturing

## INTRODUCTION

One of the most remarkable features of human language is its hierarchically embedded structure (Chomsky, 1957). Although both animal calls and human languages use one-dimensional sound signals in communication, words are organized hierarchically into sentences in the latter unlike in the former (Hauser et al., 2002). This feature recognizes the fact that the meaning of a sentence depends on its hierarchical structure and not on word order alone (**Figure 1**). This structural dependency may cause misunderstandings in communication, since the structure determining the meaning is not expressed unambiguously in a linear word sequence but only via interpretation (involving selections from multiple possibilities inside the speaker's and the listener's minds). If the adaptability of language contributes to information transmission and mutual understanding, for

Edited by:

Antonio Benítez-Burraco, Universidad de Sevilla, Spain

#### Reviewed by:

Nathan Oesch, University of Oxford, United Kingdom Tao Gong, Educational Testing Service, United States

> \*Correspondence: Genta Toya<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

Received: 18 October 2017 Accepted: 31 July 2018 Published: 19 September 2018

#### Citation:

Toya G and Hashimoto T (2018) Recursive Combination Has Adaptability in Diversifiability of Production and Material Culture. Front. Psychol. 9:1512. doi: 10.3389/fpsyg.2018.01512

**161**

example, to promote cooperation in a group, structural dependency will cause a disadvantage. We need to consider the adaptive value of language equipped with hierarchically embedded structures and structural dependencies in the period of the language's origin.

An important perspective was proposed by Kirby (2017) that cultural effects have a stronger impact than biological effects on the origin of linguistic structure. Kirby claims that human behaviors developed rich systematic structure such as recursion, compositionality and hierarchical structure to be expressive. Although we agree with this claim, we need to clarify the origin of linguistic ability, that is, operation, to construct rich structures.

Studies on hierarchical structure as the fundamental aspect of language (from the perspective of generative grammar) assume that recursive combination capacity, defined as the capacity to combine two items into a set, is the most important ability required for constructing hierarchical structures. This capacity is applied to enable a recursive syntactic operation. The different hierarchical structures are created by two types of combination, recursive combination and non-recursive combination. On one hand, theoretical linguists suggest that Merge (**Figure 2**) is a setformation operation that can be used to create an unbounded number of sentences through its recursive application (recursive Merge) (Chomsky, 1993, 2013; Everaert et al., 2015). On the other hand, Unification is also a set-formation operation that has been proposed by other researchers (Jackendoff, 2002, 2011). Jackendoff claims that Recursion is found everywhere in higher cognition; therefore, operations such as unification that can be applied to language expression and to other mental structures are needed. The important point is that both Merge and Unification share recursive combination as the core of the operations.

Recursive combination of language has been hypothesized as a human-unique trait (Hauser et al., 2002; Fitch and Hauser,

2004; Fujita, 2009, 2016). How did recursive combination and structure-dependency originate? Structure-dependency greatly increases the capacity for ambiguity in language communication. Thus, it is unreasonable to assume that recursive combination evolved to meet the needs of simple communication with oneto-one mapping between meanings and forms.

Hierarchical structure and recursive combination are described in other domains as follows: