The linguistic dimensions of concrete and abstract concepts: lexical category, morphological structure, countability, and etymology

2.1 Lexical category (part of speech)

Most studies that test the behavioral, cognitive, and neuroscientific correlates of the abstract/concrete distinction only take into account nouns (see Vonk et al. 2019: 602–603 for discussion). However, different lexical categories, or “parts of speech”, such as nouns, verbs, and adjectives, can be expected to differ in their degree of concreteness based on linguistic grounds.

Parts of speech can be defined according to a multitude of linguistic criteria, which have been emphasized to varying degrees by different scholars and theoretical orientations. As already clearly outlined by Hermann Paul, “[t]he usual division [into parts of speech] has been effected by the consideration of three points: the meaning of a word, taken by itself, its function in the sentence, and its behaviour in regard to inflexion and word-formation” (Paul 1891: 403; emphasis in original); in other words, the main criteria are of semantic, syntactic, and morphological nature (Baker 2003; Baker and Croft 2017; see Croft 1991; Rauh 2010 for additional criteria and a discussion of different theoretical perspectives). Our data analysis reported below uses part-of-speech tags that are based on formal criteria (e.g., the function of words in a sentence), but to generate hypotheses with respect to the concreteness of different parts of speech, we need to focus on the semantic features of different lexical categories, which have been particularly emphasized within cognitive linguistics (see, e.g., Langacker 1987a, strongly advocating for a notional basis of grammatical categories).

Nouns, verbs, and adjectives, arguably the major categories of content words in English, have traditionally been associated with things, actions, and properties respectively. As is well known, cases of mismatch between semantic type and part of speech abound: for example, it is not difficult to find verbs that do not describe an action (e.g., to be, to understand) and, conversely, nouns that do refer to an action (e.g., slap, conquest; see Koptjevskaja-Tamm 2015). In the light of examples such as these, the semantic approach to parts of speech has been reassessed, especially within cognitive linguistics, in terms of prototypicality. From this perspective, parts of speech are seen as prototype-based categories, in which “[t]ypical nouns describe individual physical objects, typical verbs describe physical actions, and typical adjectives designate properties” (Murphy 2010: 144). Under this account, both nouns and verbs prototypically express physical percepts. Given that accessibility to the senses is considered as the main feature of conceptual concreteness (see Section 1), this broad semantic characterization of parts of speech already allows us to hypothesize that nouns and verbs might overall be more concrete compared to adjectives, because physical objects and actions are usually experienced through the senses, while not all properties are. That is, properties may be either perceivable through the senses (e.g., the properties expressed by the adjectives yellow, bitter) or not perceivable through the senses (e.g., intelligent, free).

To formulate a more precise hypothesis, and one which also includes other parts of speech, we need to introduce a further distinction: that between punctual and relational concepts. As summarized by Prandi, “[p]unctual concepts classify individuals and give access to instances of masses, while relational concepts give access to properties of individuals and instances of masses, as well as to processes involving individuals and instances of masses” (Prandi 2004: 122; our emphasis). This ontological distinction, which dates back at least to Aristotle (Prandi 2004: 123), is connected to the distinction between the prototypical representatives of the major word classes, with punctual concepts (e.g., tree) typically being the meanings of nouns, and relational concepts (e.g., blue, give) typically being the meanings of verbs and adjectives (Prandi 2004: 124). Within cognitive linguistics, a comparable distinction was introduced in Langacker (1987a), where it is proposed that linguistic predications are either nominal or relational, with nominal predications corresponding to nouns and relational predications corresponding to verbs, adjectives and adverbs.

The privileged association of nouns with punctual concepts (and nominal predications) on the one hand, and that of adjectives and verbs with relational concepts (and relational predications) on the other, allows us to make more specific predictions concerning the correspondence between parts of speech and degrees of concreteness. Sapir (1921: 102) already observed that there is a close connection between relational concepts and abstractness; more recently, Asmuth and Gentner (2017: 2016) state that “overall it is very likely that a relational word will also be abstract”, and Borghi and Binkofski (2014: 2) posit relationality as one of the defining features of abstract concepts, since the latter “evoke properties and relations more than objects and events”. The distinction between punctual and relational concepts therefore allows us to predict not only that adjectives and verbs will overall be rather abstract, but also that verbs will be more abstract than nouns, due to their relational nature. The idea that between nouns and verbs, nouns are relatively more concrete than verbs was also suggested by Givón (1979), based on the observation that nouns typically refer to entities that exist in time and space, while verbs would usually not be anchored in space. To sum up so far, going from more to less concrete we will have: nouns > verbs > adjectives.

With respect to the other parts of speech, adverbs are notoriously difficult to define as a category (see discussion in Pittner et al. 2015; Rauh 2015), especially because of their close connection with adjectives, which has even led some scholars to suggest that English adverbs and adjectives form a single class (Giegerich 2012). What is worth noting here is that not only are adverbs often morphologically related to adjectives (such as via the deadjectival suffix -ly) but, like adjectives, they also typically express relational concepts. As mentioned above, Langacker (1987a), for instance, characterizes both adverbs and adjectives as relational categories that differ from verbs with respect to being atemporal. We can therefore predict that adverbs, like adjectives, will overall be rated as being rather abstract.

Finally, function words, such as prepositions and conjunctions, are likely to lie on the most abstract end of the scale, because their prime role is coding relations. Some function words are moreover the result of grammaticalization, which is commonly held to involve a process of abstraction from more concrete meanings (Heine and Kuteva 2002; Hopper and Traugott 2003; Lehmann 2015; Traugott 1982; Žirmunskij 1966). Overall, our hypothesis is therefore that, starting from the most concrete part of speech, we will have: nouns > verbs > adjectives and adverbs > function words.

It is worth stressing that our predictions concerning parts of speech, like those that we will make in the following sections for the other linguistic variables, are probabilistic. That is, we are concerned here with the relative degree to which particular values of a linguistic variable tend to associate with particular degrees of concreteness. Of course, there are, for instance, adjectives that may be considered rather concrete, such as blond and wet (which express properties perceived through our senses), and abstract nouns, such as optimism and ugliness. Interestingly, de-adjectival quality nouns like ugliness were referred to in the medieval philosophical tradition as abstracta in opposition to the adjectives they derive from, called concreta (Rainer 2015: 1269).

In the analyses below, we use corpus-derived parts-of-speech tags from the SUBTLEX corpus of movie subtitles (Brysbaert et al. 2012). The SUBTLEX-derived part-of-speech tags come with information about how frequent each word form is used as a particular part of speech. For example, the word furl is indicated to be a verb 67% of the time in the SUBTLEX corpus, and 33% it is a noun. In the following analysis, we used the dominant parts of speech for each word form. This is an important detail of our analysis since the participants, when they rated the corresponding word forms in the concreteness rating study, presumably thought of the most dominant part of speech when they saw the word in isolation. Importantly, the corpus-derived part-of-speech tags do not take semantics into account, they are exclusively based on a formal characterization of lexical categories in terms of their functioning in a sentence. By correlating these formal distinctions with the concreteness ratings, we assess the extent to which semantic differences correspond to the formal criteria. This not only serves to show that lexical category is an important factor to consider in psycholinguistic studies using concreteness ratings, it also empirically tests the cognitive-linguistic idea that lexical categories differ in their semantics.

Using a corpus of movie subtitles may seem like a strange choice from a methodological standpoint, but the SUBTLEX-US corpus has been argued to emulate spoken language really well and it has been demonstrated that it closely corresponds to behavioral data above and beyond other corpora (Brysbaert and New 2009). In addition, the part-of-speech tags generated from this corpus are one of the most extensive sets of part-of-speech tags that are also readily available for statistical analysis, and are widely used in psycholinguistic research (Brysbaert et al. 2012). The results we obtain here replicate with other part-of-speech tags (e.g., from the English Lexicon Project, Balota et al. 2007), which shows that the choice of SUBTLEX-US as a corpus does not matter for our analysis of part-of-speech.

2.2 Morphological structure

Research on abstract nouns indicates that a useful criterion for identifying them, among all nouns, is based on their morphological features. In English, as observed by Zamparelli (2020: 203) “one could regard as abstract all the nouns derived from the suffixes -ness, -ity, -tion or -hood, -itude, -cy, -ment, -ship […], or more generally, all the nouns derived from gradable adjectives”. Additional abstract-noun-forming suffixes listed by Plag (1999: 67) are the deverbals -age (steerage), -al (betrayal), -ance (annoyance), -y (enquiry), and the denominals -age (orphanage), and -ism (despotism). Reilly and Kean (2007) demonstrate that nouns with suffixes are rated as more abstract than nouns without suffixes. Here, we investigate whether suffixation may be useful as a signal of abstraction not only for nouns (which, incidentally, should be overall rather concrete, see Section 2.1) but also for words of other parts of speech. To test this hypothesis, we examined the most frequent suffixes of English: if the most frequent suffixes can be classed as abstraction-triggers, then there are chances that suffixed words are overall more abstract than non-suffixed ones.

The MorphoLex database (Sánchez-Gutiérrez et al. 2018) contains information on a set of derivational morphological variables for 68,624 complex words from the English Lexicon Project (Balota et al. 2007). Such variables also include measures of suffix frequency. For our purposes, the relevant measure is morphological family size (Schreuder and Baayen 1997), that is, morphological type frequency defined as the number of word types in which a given morpheme, in our case a given suffix, is a constituent: “For instance, in the example {attendance, pleasance, pleasure, appearance}, the suffix -ance has a morphological family size of 3 {attendance, pleasance, appearance}, while the root -pleas- has a morphological family size of 2 {pleasure, pleasance}” (Sánchez-Gutiérrez et al. 2018: 1572). Based on this measure, we selected the 20 most frequent suffixes, listed here in Table 1.

The suffixes in Table 1 comprise about 84% of the morphologically complex words in the MorphoLex data. We can notice that the majority of such suffixes form adjectives (-ly, -ic, -able, -est, -ious, -ive, -less; e.g., lovely, terrific, measurable, smartest, cautious, active, hopeless) and/or adverbs (-ly, -est; e.g., honestly, soonest); words that contain such suffixes should therefore be rather abstract, based to the hypothesis outlined in Section 2.1, according to which adjectives and adverbs should overall be more abstract compared to most other parts of speech. The verbalizer -ize (e.g., humanize) should also form relatively abstract words, again based on the hypothesis in Section 2.1 (that is, verbs should overall have “intermediate” concreteness values). As for nominal suffixes, most are among the typical abstraction-triggering ones listed in works on abstract nouns mentioned above (-al, -ness, -ity, -ion, -ance); the remaining two form nouns referring to persons, which, on the contrary, are arguably concrete (-er, -ist, e.g., plumber, artist).

Since the majority of the most frequent derivational suffixes of English form words referring to rather abstract concepts, we expect morphologically complex words (with suffixes) to be overall more abstract than morphologically simplex words (without suffixes). For nouns only, this result has already been established by Reilly and Kean (2007). Here, we extend their result to more parts of speech (not only considering nouns), and we look at differences between types of suffixes, focusing on the suffixes shown in Table 1. Moreover, we are interested in investigating whether the prediction made above (suffixed words being more abstract) extends to words with more than just one suffix, and whether abstractness grows as a function of the precise number of morphemes, i.e., does abstractness increase monotonically when words become progressively more morphologically complex? It has been shown that word length correlates with abstractness (see Lewis and Frank 2016; and the experimental studies in Reilly et al. 2012, 2017), and hence we want to further assess the extent to which morphological structure and length are independently associated with abstractness.

2.3 Countability

For the class of nouns, a much researched distinction is that between count and mass nouns (e.g., Fieder et al. 2014; Moltmann 2020). Linguists generally base the count/mass distinction on morphosyntactic criteria (Jespersen 1924). English count nouns, for instance, allow plural marking (cats, hats) and determination by cardinal numerals (two cats, one hat), while mass nouns generally don’t (*sands, *one sand). The count/mass distinction, however, does not characterize nouns, but senses of nouns, that is, a given noun (e.g., matter) may be classified as count in one of its senses (e.g., ‘a vaguely specified concern’, as in several matters to attend to) and as mass in another (e.g., ‘that which has mass and occupies space’, as in physicists study both the nature of matter and the forces which govern it) (examples from Kiss et al. 2016: 2810–2811). In addition, a noun in a single sense (e.g., cake as a ‘baked good’) may also be count or mass depending on the context (e.g., a cake vs. some cake), to the point that, as Langacker observed (1987b: 67), “[g]iven proper circumstances, almost any count noun can be construed as designating a homogeneous, unbounded mass; it may thereby come to function as a mass noun grammatically”. This is reflected in Langacker’s well-known example After I ran over the cat with our car, there was cat all over the driveway (1987b: 67), where the count noun cat displays mass noun semantics. This has led some scholars to argue that the count/mass distinction concerns in fact neither lexemes nor senses of lexemes, but ways of using them (see discussion and references in Franzon and Zanini 2019). According to Chierchia (2010: 111), the fact that “some nouns are ambiguous, [and] most can be coerced” appears to be a property that is universally associated with the count/mass distinction, and he labels it “elasticity” (for an interpretation of contextual countability shifts in terms of coercion, see, among others, Michaelis 2005; Pustejovsky 1995).

In addition to the differences in morphosyntactic properties and behaviour, it is also often assumed that the distinction between count and mass nouns corresponds to a semantic and conceptual distinction, the precise nature of which is still much debated (see discussion in Doetjes 2017; Fieder et al. 2014; Kiss et al. 2021; Langacker 1987a; Middleton et al. 2004; Rips and Hespos 2019; Talmy 2000; Wierzbicka 1985). In this regard, several researchers have remarked about the count/mass distinction being also characterized in terms of concreteness. Mass nouns can be more abstract or more concrete (hope vs. sand), and the same holds for count nouns (idea vs. cat), so that “[c]ountability has often been considered ‘orthogonal’ to concreteness in the description of nouns” (Franzon and Zanini 2019: 169). However, the literature suggests that there may be a privileged relation between mass nouns and abstraction.

First, grammars of English and theoretical studies occasionally mention in passing that most mass nouns are abstract (e.g., Gillon 2017: 296–297; Quirk et al. 1972: Ch. 4), a hypothesis for which there also is some corpus-based evidence. For example Katz and Zamparelli (2012) show that among the nouns that are most frequently used in mass contexts, almost all are also abstract. Second, the case of “elastic” nouns is particularly revealing in this respect. Since elasticity concerns both concrete and abstract nouns, concrete nouns (e.g., cake) can be used as count (a cake) or mass (some cake), and abstract nouns (e.g., hope) can be used as count (She had two hopes for her future) or mass (She sees some hope for the future) (on countability within abstract nouns see Husić 2020; Zamparelli 2020). Interestingly for our concerns, it has been argued that concrete nouns are relatively more abstract in their mass uses (some cake) than in their count uses (a cake), because “the former entail the suppression of the reference to shape, which is a salient property in the representation of entities” (Franzon and Zanini 2019: 167). Finally, if we consider concepts expressed by concrete nouns only, experimental research has shown that count ones (e.g., apple) afford direct manual grasp, while mass ones referring to substances and aggregates (e.g., water, sand) tend to require the intermediation of an instrument, such as a cup or a spoon (De Felice 2015). Since graspability correlates with concreteness (Pexman et al. 2019), this may indicate that concrete mass nouns are relatively more abstract than concrete count nouns. For all these reasons, we expect mass nouns to be, on average, more abstract than count nouns.

In our analyses, we use data from the Bochum English Countability Lexicon (Kiss et al. 2016), a manually annotated database that addresses the issues discussed above, namely, that the count/mass distinction does not apply to nouns but to senses of nouns, and that a noun in a given sense can often be both count and mass. As for the first issue, the database provides countability information for noun-sense pairs rather than for nouns, e.g., matter ₁ (‘a vaguely specified concern’) is annotated as count, while matter ₂ (‘that which has mass and occupies space’) is annotated as mass. The second issue is addressed by the fact that each noun-sense pair is assigned to one of four distinct classes rather than to count or mass only, i.e., count, mass, both count and mass, neither count nor mass. The database contains ≈11,800 noun-sense pairs that have been assigned to one of the four classes based on the answers that annotators gave to several syntactic and semantic questions about the usage of the noun-sense pair. For example, a noun-sense pair is annotated as count only if the answer to “Can the noun-sense pair in its plural form appear together with more?” is “yes” (Kiss et al. 2016: 2811).

2.4 Etymology

Whether conceptual concreteness plays a role in diachronic change or stability of languages’ vocabulary is a matter of debate (Monaghan and Roberts 2019: 149). In the specific case of English, however, social and cultural correlates of well-known historical facts allow us to formulate predictions about the degree of concreteness of words originating from two languages in particular: Latin and French. These two languages are major sources of lexical borrowing in English, with Latin and French words forming a large portion of the vocabulary of Modern English (for an overview of external influences on English, see Durkin 2014; Miller 2012).

The borrowing of Latin lexemes reached its quantitative peak during the Renaissance (see Oxford English Dictionary timelines),^[2] when Latin, as the international language of science, provided lexical material for new technic, scientific, and philosophical concepts: it is therefore likely that most Latin words that entered the lexicon in this period, and thus a considerable number of Latin loanwords in general, are rather abstract. French, whose words started being borrowed after the Norman Conquest (1066), continued to enjoy great political and cultural prestige for centuries. For example, a disproportionate amount of English legal terms, which are highly abstract, derive from French (lawyer, attorney, mortgage, defendant, culprit, jury, larceny, parole, plaintiff, etc.) because the Normans were in power and ruled the court. Orr (1944: 3) speaks of “the far more abstract or intellectualized vocabulary of French”. Textbooks and popular publications, moreover, often mention triplets like rise, mount and ascend, or go, depart and exit, where “[t]he Anglo-Saxon word is typically a neutral one; the French word connotes sophistication; and the Latin or Greek word, learnt from a written text rather than from human contact, is comparatively abstract and conveys a more scientific notion” (Hitchings 2008: 21).

Although the idea that the English vocabulary is stratified in such a clear-cut way is over-simplistic, the contact history with Latin and French leads us to expect that words of Latin and French origin will, on average, be more abstract than the remaining part of the vocabulary, composed of words of Germanic and other origin. Reilly and Kean (2007) already provide some evidence for this, because they showed that nouns of Latinate origin are overall more abstract compared to those of other origin. We will see whether our data confirm their result for more than just nouns. In contrast to the other variables (part of speech, morphological structure, and countability) this prediction is based on extra-linguistic considerations, i.e., contingent facts about the history of English. Taking etymology into account is, however, also important for theoretical reasons: because English mass nouns are disproportionately French- or Latin-derived, our hypotheses about the abstractness of mass nouns need to control for the fact that we expect these nouns to be more abstract on purely historical grounds. Similarly, the idea that morphological complexity is correlated with abstractness needs to be assessed while controlling for etymology, because Latin words have a tendency to be slightly more morphologically complex, as reported below. Thus, certain hypotheses motivated by linguistic theory need to be assessed while holding etymology constant to make sure that these two factors are not confounded.

To establish the origin of words, in the analyses below we retrieve etymological information from the Oxford English Dictionary.

2.5 Limitations: language-specificity and omitted variables

There were some variables that we excluded from our consideration even though they would seem relevant on the grounds of cognitive linguistic theory. We did so partially to limit the scope of our investigation, but also because predictions for these other variables were not as clear-cut. One of the variables we do not consider here is polysemy. Intuitively, it could be hypothesized that more polysemous words should be more abstract because i) metaphor is a prominent mechanism of semantic extension, and ii) metaphoric extended meanings of polysemous words are known to often be more abstract than the meanings from which they stem (Lakoff and Johnson 1980). For instance, the polysemous word support has a concrete meaning that refers to a structure that holds a weight, and a relatively more abstract metaphorical meaning that refers to the help or approval given to a friend, an idea, or an organization.

However, predictions for the relation between polysemy and concreteness ratings are not as clear-cut. First, polysemy also includes relations between senses that are not based on metaphor, and hence that do not clearly follow a concrete > abstract direction, such as metonymy, which often involves relations between concrete senses. For example, the word dish is metonymically polysemous between a food sense (This is a great dish) and a container sense (Can you hand me the dish?), both of which are rather concrete. Second, polysemy does of course not only involve metaphor and metonymy, but also hypernymy (e.g., cow ‘female’ to the more generalized sense cow ‘of either sex’) and hyponymy (e.g., to drink ‘anything’ to the more specialized sense to drink ‘only alcohol’), both of which can be seen as making opposing predictions with respect to concreteness. Third, even for metaphorical polysemy, predictions are not as clear-cut. Metaphor is generally thought to primarily involve mappings from concrete to abstract meanings, which means that 1) we can expect metaphorically polysemous words to be more abstract (because they have acquired abstract senses), but also 2) since metaphor is thought to preferentially draw from concrete source domains, metaphorically polysemous words could also be, on average, more concrete.

Additionally, there are methodological reasons for us not to consider polysemy here. In concreteness rating studies such as Brysbaert et al. (2014), ratings are provided for isolated word forms, not for word-meaning pairs or words in context. We therefore do not know which senses of polysemous words have been rated. As observed by Löhr (2021), the same word could plausibly have multiple different concreteness ratings depending on which sense is implied, something that has been empirically addressed by Reijnierse et al. (2019), who show that when the meanings of a word are disambiguated, the elicited concreteness ratings are actually different. Another methodological issue is the fact that it is not clear what the best way of quantitatively operationalizing polysemy at large scale is, especially vis-à-vis the fact that some word senses are more dominant than others (cf. Werkmann Horvat et al. 2021). We therefore leave it for future studies to explore the relation between polysemy and concreteness ratings.

Finally, it is worth highlighting why we focus on one language only. First, the choice of the relevant variables to be investigated is at least in part language-dependent (e.g., one would not consider morphological structure when working on a strongly isolating language). Second, concreteness scores are language-dependent too, because it cannot be assumed that there is a perfect match between two lexemes from two different languages – however semantically similar such lexemes may be – and a given concept. The English noun air, for instance, has a concreteness score of 4.11/5 in Brysbaert et al. (2014), while the French noun air ‘air’ has a much more abstract score of 1.93/5 in Bonin et al. (2018). Such discrepancies across languages may depend on a variety of motivations, ranging from actual differences in conceptualization to semantic anisomorphism between the two lexemes or, more trivially, to slight differences in the instructions provided to speakers. This led us to focus in this paper on a single language, leaving a much-needed exploration of cross-linguistic variation in the relation between linguistic features and conceptual concreteness for future research.

3.1 Datasets

3.2 Statistical analysis

All analyses were performed with R version 4.1.1 (R Core Team 2019) and the tidyverse package version 1.3.1 (Wickham et al. 2019). The car package version 3.0.11 (Fox and Weisberg 2018) was used to compute variance inflation factors to assess collinearity. All datasets and code are made publicly available in the following Open Science Framework repository: https://osf.io/rej6b/.

We performed a series of regression analyses, each time with concreteness as the main response variable. We first consider each linguistic predictor in isolation before we perform a simultaneous regression analysis with all predictors. One reason for performing separate regression analyses first is the presence of missing values for different words in the different data sets, as there are only few words having data for all variables. Running individual regressions allows us to assess each variable’s influence on concreteness ratings for the biggest dataset possible. Another reason for performing separate regressions is that some variables only have values for certain subgroups of the data. In particular, the count/mass distinction only exist for nouns. After having performed the individual regressions, we performed a simultaneous analysis with all predictors together.

4.1 Lexical category (part of speech)

For our first analysis, we consider a word’s “dominant part of speech”, which corresponds to the part of speech that a word occurred most frequently in within the SUBTLEX corpus. For example, the word form leak was used 81% as a noun and 19% as a verb. This word form would thus be treated as a noun in the following analysis. Figure 1 shows the concreteness ratings as a function of part of speech.

Figure 1:

Concreteness ratings as a function of part of speech.

Except for adverbs, the results were largely as predicted: nouns were the most concrete (M = 3.59, SD = 1.03), followed by verbs (M = 2.95, SD = 0.81), adjectives (M = 2.49, SD = 0.73), function words (M = 2.38, SD = 0.77), and adverbs (M = 2.08, SD = 0.55). An omnibus test reveals that there is an overall effect of part of speech (F (4, 19123) = 1722.0, p < 0.0001) that described a considerable 26% of the variation in concreteness ratings across words (R ² = 0.26). When the same analysis was repeated with regression weights penalizing high-SD words, the part of speech predictor described even more variance, R ² = 0.34.

4.2 Morphological structure

In their analysis of morphological structure, Reilly and Kean (2007) excluded compound words. Here, we follow this analysis approach given that our hypotheses for morphological structure (laid out in Section 2.2) relate to derivation and not to compounding as a word formation process. We compiled a list of English compound words by collating the lists of Juhasz et al. (2015), Gagné et al. (2019), and Kim et al. (2019), which was used to exclude these words.

First, we look at the presence or absence of suffixes. This analysis compares morphologically simplex words (1 word = 1 morpheme) against morphologically complex words with one of the suffixes listed in Table 1 (1 word = 2 morphemes). Words without suffix were considerably more concrete (M = 3.44, SD = 1.03) than words with suffix (M = 2.77, SD = 0.89). The corresponding linear model (t = 32.80, df = 11,164, p < 0.0001) described 9% of the variance in concreteness ratings. Again, the weighted regression performed even better, describing 13% of the variance.

Figure 2 shows the result broken up by the most frequent suffixes (corresponding to the suffixes listed in Table 1). This shows that Reilly and Kean’s (2007) claim that words with suffixes are on average more abstract clearly needs to be qualified. While we have replicated this finding in our previous analysis for a larger dataset than they consider (including not only just nouns), Figure 2 shows that there are vast differences in the average concreteness of particular suffixes. In fact, words with the suffix -er (as in worker, dreamer, swindler, pensioner) had similar average concreteness (M = 3.91, SD = 0.66) to monomorphemic words without suffixes (M = 3.44, SD = 1.03). This clearly shows that it matters which suffix one considers. Words with the suffix -ly were the most abstract (M = 2.11, SD = 0.42), in line with the previous analysis of part-of-speech tags, given that this suffix forms adverbs in English, which we have found to be the most abstract. An omnibus test for suffixed words showed that there was a reliable effect of type of suffix (F(19, 3284) = 114.5, p < 0.0001) that described 40.0% of variation in concreteness ratings for these words. When regression weights were incorporated to penalize high-SD words, this described variance rose to 47%.

Figure 2:

Concreteness ratings as a function of specific suffixes.

In a separate analysis, we regressed concreteness ratings onto morpheme counts from the English Lexicon Project. This analysis still excludes compound words but focuses on the full range of morphologically complex words, including words with multiple derivational affixes (e.g., unbelievable, surpassingly, harmoniousness). As shown in Figure 3, words with more morphemes were more abstract. We incorporated morpheme count as a continuous variable into the regression model, with an estimated decrease of 0.53 concreteness rating points for each additional morpheme (t = 70.61, df = 15,933, p < 0.0001). The morpheme count variable described 24% of the variance in concreteness ratings, and 31% in the weighted regression. Our analyses present a further extension of the results by Reilly and Kean (2007), which is that it is not just a binary distinction between morphologically complex and simplex words that matters, but instead, morphological complexity is monotonically related to the concreteness/abstractness distinction.

Figure 3:

Concreteness ratings as a function of morpheme counts.

As mentioned above, Lewis and Frank (2016) found that word length was related with abstractness or, more specifically, with the related concept of conceptual complexity (see also Kelly et al. 1990). Because of this, it makes sense to estimate whether the morpheme count result above is independent of word length. In a simultaneous regression, we incorporated phoneme counts from the English Lexicon Project (Balota et al. 2007) as an additional predictor, on top of morpheme counts. For this analysis, there was an effect of phoneme count on abstractness (b = −0.08, SE = 0.004, t = 32.33, df = 15,932, p < 0.0001), as well as an effect of morpheme count (b = −0.36, SE = 0.01, t = 19.94, df = 15,932, p < 0.0001). The fact that the morpheme count coefficient shrunk (from −0.53 concreteness rating points per morpheme above to −0.36 concreteness rating points in this analysis) suggests that in the previous analysis, the effect of word length was in fact confounded with morpheme length. However, both have independent effects on abstractness. Comparison of coefficients as well as of R ² shows that incorporating length only leads to slightly better model fit (25% as opposed to 24% variance described), which suggests that morphology matters more than just length.

4.3 Countability

For nouns only, we investigated the distinction between mass and count nouns, using data from the Bochum English Countability Lexicon (Kiss et al. 2016). The BELC database is organized around senses (see Section 2.3), rather than word forms. To match this data to the concreteness ratings, which do not separate senses, we decided to analyse only the subset of those nouns for which all senses were either count or mass without exception. This dataset included a total of 3,599 count nouns and 801 mass nouns. As seen in Figure 4, count nouns were on average more concrete (M = 3.78, SD = 0.91), and mass nouns relatively more abstract (M = 2.76, SD = 0.95) (t = 28.74, df = 4,398, p < 0.0001), and described 16% of the variance in concreteness ratings. As before, the weighted regression penalizing high-SD words described even more variance, 19%.

Figure 4:

Concreteness ratings as a function of the count/mass noun distinction.

4.4 Etymology

We processed etymologies for a total of 287,341 unique word senses from the Oxford English Dictionary (OED). For sequences of borrowing (e.g., French < Latin), we used the proximate source (in this case, French) rather than the ultimate source (in this case, Latin), as it is the language that is most directly tied to the contact history of English. Of course, distinct word senses can have distinct etymological histories: typically, from the perspective of historical linguistics and OED, this is homonymy, with the same word form resulting from different origins. To match the OED data to the data from the concreteness rating study (which does not distinguish between word senses, as discussed above), we collapsed etymologies across distinct entries using a majority vote criterion (i.e., if 9 etymologies for a word form were indicated to be French-derived and 3 were indicated to be Latin-derived, we assigned “French” to the word form). In most cases, this majority vote criterion did not have to be exercised because different word senses were indicated to have the same etymologies. For the classification of an etymology as “French”, we included “law French” but excluded “Canadian French” and “French Creole”. Words that were not Latin or French were assigned the “other” category, which included words of Germanic origin as well as borrowings from other languages (e.g., Dutch, Chinese), words derived from person names or place names, imitative forms, and uncertain etymologies.

On average, Latin-derived words were the most abstract/least concrete (M = 2.86, SD = 0.95), closely followed by French-derived words (M = 2.95, SD = 0.99). Words in the ‘other’ category were much higher in concreteness (M = 3.21, SD = 1.10), as shown in Figure 5. An omnibus test reveals a reliable effect of the factor ‘etymological origin’ (F(2, 25686) = 150.24, p < 0.0001). However, this factor only described a total of 1% of the variance. The described variance rose to 1.5% in the weighted regression that penalizes high-SD words.

Figure 5:

Concreteness as a function of etymological origin.

As discussed above, it is important to keep in mind that etymology interacts with the other variables considered so far, specifically morphological complexity and the count/mass noun distinction. A simple Chi-square test tabulating etymology against the countability data from Section 4.3 shows that the count/mass distinction differed reliably across etymologies (χ ² = 37.77, df = 2, p < 0.0001), with French words being much more likely to be mass nouns (Pearson adjusted standardized residual = +5.2). Similarly, Latin words were more likely to be mass nouns (+2.4). Finally, “other” words were much more likely to be count nouns (residual: −6.1). With respect to morphological complexity, Latin-derived words also had higher morpheme counts (M = 2.10) than French-derived words (M = 1.82), with “other” words being in between the two (M = 2.07). A Poisson regression model (used because the dependent measure morpheme count is a count variable) shows that etymological origin affects morpheme counts (likelihood ratio test against null model: χ ² = 75.0, df = 2, p < 0.0001). The next section will see whether etymological origin has a unique effect, or whether its contribution to predicting concreteness ratings can be reduced to the other variables.

4.5 Simultaneous regression analysis

So far, we have only considered each linguistic property by itself. However, as the previous section has discussed, some of the predictors are confounded with each other. To assess whether the different variables have independent effects when controlling for the others, we entered them all into the same simultaneous regression model. To account for the count/mass noun distinction in the full model, the “noun” category of the part-of-speech factor was split into two levels: “mass noun” and “count noun”. Thus, the full model contained a total of four predictors: part of speech (including the count/mass noun distinction), the number of morphemes, the number of phonemes, and etymology. We used the rsq package version 2.2 (Zhang 2021) to compute partial R ² as an estimate of the unique contribution of each predictor to the total variance of concreteness ratings.

The results are shown in Table 3. With all four predictors together, the overall model described 46% of the variance in concreteness ratings, and 56% for the weighted regression model. Partial values suggest that part of speech (including the count/mass distinction) describes the biggest share of variance, followed by morpheme counts, phoneme counts, and etymology, in that order.