2. The usage-based approach to metaphor identification in child speech

Our usage-based approach to metaphor identification in child speech owes most of its features to MIP (Pragglejaz Group, 2007) and MIP-VU (Steen et al., Reference Steen, Dorst, Herrmann, Kaal, Krennmayr and Pasma2010), because both MIP and MIP-VU include even the most basic types of linguistic metaphors (encoded in delexicalised verbs and prepositions), which can help us to capture the origins of metaphoric speech. Although words only gain metaphoricity if examined in the context, we expect few metaphors stretching beyond the boundaries of words because the speech of children aged two is dominated by one-word utterances, and their utterance length develops only gradually over the third year of life (Brown, Reference Brown1973).

UBAMICS breaks down linguistic metaphor identification into six main steps:

1. 1) Read the transcript to understand what it is about.

2. 2) Decide what makes a word (i.e., the basic unit of analysis). Some collocations (e.g., phrasal verbs and idioms) are analysed on a par with individual words as long as they encode only one metaphor. Decide if the word, or its part, is metaphor related, that is, it has been used with a contextual meaning that is different from its basic meaning. Determine the contextual meaning of the given metaphor-related word (MRW). In some cases, where the metaphor is clearly observed on the level of affixes, it can be coded as such (e.g., to overspend, in the afternoon). This should extend the use of UBAMICS to more synthetic languages in which primary metaphors are often disguised in longer words containing multiple morphemes. For example, the Polish perfective aspect verb zaśpiewać ‘to sing’, which indicates one-time completion of the act, can be analysed as made up of an imperfective verb stem śpiewać ‘sing’, which does not specify completion of the act, and a metaphor-related prefix za ‘after/behind’ which some interpret as initiating an action by crossing a boundary (Tabakowska, Reference Tabakowska, Cuyckens, Berg, Dirven and Panther2003).

3. 3) Determine the basic meaning of the MRW using a dictionary (The Macmillan English Dictionary for Advanced Learners and The Longman Dictionary of Contemporary English). MIP-VU sees basic meanings as those which are more concrete, human-oriented, and more specific.

4. 4) Decide if there is a sufficient difference between the two meanings, disregarding, at least initially, whether the child has a grasp of these two meanings, and the order in which they were acquired. Include, at least initially, all instances of metaphors where the child may, or may not, have understood what they are saying (e.g., recasts of parental turns such as I’m not a parrot). Further tests can be performed at a later stage to determine whether children who produce MRWs also have their basic sense equivalents in their productive lexicons. Further tests can also confirm to what extent children can use MRWs independently (i.e., not as recasts of parental turns).

5. 5) Decide if these two meanings can be related by some form of similarity, or what CMT would refer to as a mapping between two different domains.

6. 6) Exclude non-metaphorical overextensions, pretence, and words where the link between the concrete and abstract form is obscured by some form of phonological modification.

The latter three language features are very common in speech, especially that of very young children. First of all, some words in child speech may be used in a seemingly metaphorical way because the child is yet to acquire a range of vocabulary and she has no choice but to refer to a carpet as grass, for example (Billow, Reference Billow1981; Winner, Reference Winner1979). As adult metaphors also help to express ideas difficult to explain using more literal language, we argue that only metaphorical extensions should be included, whereas non-metaphorical extensions are excluded (Voeniatou, Reference Voeniatou1987). These tend to be easy to discern, as metaphorical extensions are based either on the distinctions between two entities which are fairly distinct from each other, including both their abstract features (e.g., calling someone a monkey means that they are a lively and mischievous individual) and concrete features (e.g., calling someone a giraffe means they are a tall individual, or one with a long neck). Meanwhile, non-metaphorical extensions are based on the concrete features of entities that are fairly close to each other (e.g., referring to a meerkat as a monkey suggests that the child may not know the distinction between the two).

Second, pretence is very common in child language (e.g., Kavanaugh & Harris, Reference Kavanaugh, Harris, Bolter and Tamis-Le Monda2001). Like metaphor, pretence brings inanimate entities to life: both may present cases of non-literal language use, both are elaborate and complex, thus they may rely on the same cognitive processes (Pouscoulous, Reference Pouscoulous2011, p. 82). However, as is shown in Table 1, pretence and metaphor are somewhat different: only metaphor requires that the MRW be used in its abstract sense. Thus, to exclude cases of potential pretence in a systematic manner, it is important to adhere to a procedure of a) identifying word polysemy and b) distinguishing between the word’s basic and contextual meanings (Table 1). We exclude from analysis all cases of non-linguistic pretence which do not rely on polysemy (Table 1, rows 1 and 2). Where polysemous words are used in their basic sense (row 3), they are deemed non-metaphorical. For example, where the child says Do you like me, hoover?, she seems to enter the world of fiction, treating the hoover as a character able to hear, understand and respond to human speech. Therefore, the word like is seen as used in its basic sense (i.e., as if the hoover were capable of liking people). By contrast, where fictional characters are described by words in a sense which is distinct from their basic meaning (row 4), the words should be categorised as metaphorical. For example, a child may ask a bunny to watch out, where the word watch is used to refer to paying attention rather than seeing, both real-life qualities. Even though this word is used in relation to a fictitious character, in this example the polysemy does not cross the boundary between what is real and what is not.

Table 1. How to distinguish pretence from metaphor in the corpus (polysemy in bold)

Third, as UBAMICS examines transcripts of speech, it deals with spoken forms of language, and needs to consider how such language is produced and heard in the given situation. Some spoken forms may lose their polysemous character in speech. For example, where spoken forms are systematically contracted (e.g., I’m gonna go), their correspondence with their concrete counterparts is lost (gonna = going to), and they are excluded from analyses. Other spoken forms which include non-target pronunciation (e.g., Lickle instead of Little) are included because they are considered either as personal speech variations, or as temporary features of child speech and mere attempts at target-like pronunciation.

Steen et al. (Reference Steen, Dorst, Herrmann, Kaal, Krennmayr and Pasma2010, p. 72) also point out that in conversation analysis many aborted utterances may need to be disregarded if the context surrounding word use is unclear. In child language, this is exacerbated by the fact that some turns are presented as partly unintelligible. Some of these utterances could be clarified in future research if video recordings were available, particularly considering that children, more than adults, rely on gestures as a gap-filling strategy, both to name and request objects. Access to video recordings would allow researchers to access earlier stages of language acquisition and to explore multimodal metaphor use which combines gestures with the first words used.

Furthermore, unlike MIP or MIP-VU, UBAMICS does not code for some linguistic ontological metaphors such as container (e.g., in a choir) and substance metaphors (e.g., a lot of thinking) to make the process of metaphor identification less time-consuming, enabling analysis of multiple datasets recorded longitudinally over several years of development. Also, unlike MIP-VU, UBAMICS does not code for metaphoric expressions which have been either replaced with a pronoun (e.g., To embark on such a step is not necessarily to succeed immediately in realising it [the process of embarking on such as step]), or omitted through ellipsis (But he is! [an ignorant pig]). This is to capture the actual instances of linguistic metaphor use in the corpora, rather than their implied or potential use. Moreover, unlike MIP or MIP-VU, our approach does not require that words be of the same grammatical category in order to be compared. They merely need to have the capacity to fill the same slot within the utterance. For example, baby carrot can be analysed as a ‘small carrot’ even though baby is a noun and small is an adjective. This reflects the key assumption of UBT that children become aware of formal linguistic categories gradually in the process of acquisition as words of the same category tend to be used in the same slots (Tomasello, Reference Tomasello2003).

To facilitate quantitative analyses of input–output relations in metaphor acquisition, we propose to determine linguistic metaphor frequencies by adding up their numbers in each transcript of hourly interaction in child and adult speech. This is different from MIP or MIP-VU which have been designed to examine metaphor frequencies in written discourse, and which do so by calculating the total number of metaphorically used words and units out of the total number of words and units used in each text. MIP and MIP-VU style of determining the relative frequency can be adopted as an alternative; however, this process will be more time-consuming, as it will need to be implemented at the level of morphemes, rather than words and units, especially in more synthetic languages where metaphors are often disguised in longer words containing multiple morphemes.

3. The coding manual for UBAMICS

In this section, we present the coding manual developed for the analysis of linguistic metaphors in child speech. In the process of metaphor identification, all metaphors are coded at two levels: first for metaphor class, and then for metaphor mapping. The category of metaphor class allows us to cluster metaphors that share some broad features (e.g., all primary metaphors reflect basic relations between concrete entities and abstract notions). The category of metaphor mapping allows us to capture more specific characteristics within all metaphor classes (e.g., primary metaphors of time such as TIME IS SPACE or TIME IS MOTION). Personification and metonymy–metaphor combinations are the only two metaphor classes that are not associated with metaphor mappings.

As our approach is heavily indebted to MIP and MIP-VU, and by extension also CMT, we advocate working with the Master Metaphor List (MML, Lakoff et al., Reference Lakoff, Espenson and Schwartz1991), which can help to confirm whether the identified linguistic example can indeed be considered an instance of conceptual metaphor use. If a linguistic expression is not included on the MML, researcher intuition should be used to estimate its potential correspondence with the listed mappings. Otherwise, if the metaphor appears to rely on some form of similarity, either physical (e.g., Sarah is a giraffe, i.e., tall) or relational (Sarah is such a monkey, i.e., playful, and mischievous), it should be regarded as a resemblance metaphor (for more discussion of these metaphor types, see, e.g., Winner, Reference Winner1997).

We divide conceptual metaphors into six classes:

1. 1) Primary metaphors, that is, single- or multi-word orientational metaphors to do with space, motion, and direction, which are encoded in verbs, adverbs, adjectives, and prepositions, or combinations thereof (e.g., Let us make a snowman when winter comes = TIME IS MOTION (see Grady, Reference Grady2005)), and any other ontological metaphors which provide more complex mappings than just container and substance (e.g., This is just brilliant! = GOOD IS LIGHT).

2. 2) Verbs of perception, ^{Footnote 1} that is, single- or multi-word metaphors built around verbs of perception, or verb phrases (e.g., You get hurt because you never listen = OBEYING IS LISTENING; for specific mappings cf. Ibarretxe-Antuňano, Reference Ibarretxe-Antuňano, Speed, O’Meara, Roque and Majid2019).

3. 3) Personification, that is, single- or multi-word ontological metaphors usually encoded in nouns, verbs, and adjectives, or phrases built around them, which attributed human qualities to non-human entities (e.g., The traffic light says stop).

4. 4) Structural metaphors, that is, single- or multi-word metaphors which reflect complex systems of universal beliefs (e.g., Stop fighting with your friends = ARGUMENT IS WAR).

5. 5) Metonymy–metaphor combinations, that is, single- or multi-word formations where, in addition, a given concept is referred to by the name of something closely associated with that concept (e.g., My nose is running [i.e., the mucus, not nose (metonymy)], is moving very fast, but not running [i.e., using legs (metaphor)] (see also Radden, Reference Radden, Dirven and Pörings2002)).

6. 6) Novel conceptual metaphoric formations, that is, single- or multi-word examples, which are not familiar to the coder, but which can be paired up with a well-established mapping (e.g., You are sugary = PLEASURE IS SWEET).

Although most metaphors are likely to be captured by the proposed conceptual metaphor classes and mappings, there is also a need to account for those metaphors which are not rooted in sensimotor experience, but instead reflect a resemblance between the SD and the TD. When coding for resemblance metaphors, we distinguish between three different classes:

1. 1) Nominal A-to-B metaphors, that is, nominal metaphors encoded in single nouns or noun phrases which reflect a certain attitude to, or perception of, another person (e.g., petal, pumpkin, tennis ball, monster, dragon).

2. 2) Other resemblance metaphors, that is, any other metaphors grounded in a perceptual similarity which are encoded in single words other than nouns or noun phrases, or phrases built around them (e.g., catch a bus, pop into the office, hold on a minute).

3. 3) Novel resemblance metaphoric formations, single- or multi-word metaphors, which are not familiar to the coder, but which evoke some form of perceptual similarity (e.g., Your hair is worms).

Beyond the conceptual and resemblance metaphors, we follow MIP-VU (Steen et al., Reference Steen, Dorst, Herrmann, Kaal, Krennmayr and Pasma2010) and use two additional codes to account for phenomena whose use should be examined in relation to metaphor acquisition.

1. 1) MRW direct, that is, when an expression is used directly and its use may potentially be explained by some form of cross-domain mapping to a more basic referent or topic in the text (e.g., like a diamond in the sky). Some studies show that direct metaphors, or ‘similes’, can be understood earlier in life than normal metaphors because the metaphorical word is introduced with an explicit marker (Siltanen, Reference Siltanen2009) and because similes are more literal than metaphors (Katz et al., Reference Katz, Cacciari, Gibbs and Turner1998; Searle, Reference Searle1979) but this needs be verified for metaphor production.

2. 2) MFlag, that is, a word which functions as a signal that a cross domain mapping may be at play. Potential markers of analogy include like, as, compare, comparison, comparative, same, similar, analogy, analogue, regard as, conceive of, see as, imagine, think, talk, behave as if and so on. Unlike MIP-VU, UBAMICS also uses this code for words such as chocolate button and fishfinger, which contain some element of comparison, in that they either remind us of objects from other domains or have been created in their likeness. In these examples, instead of the well-established markers of analogy (e.g., like), the concrete notions of chocolate and fish support the interpretation of the whole phrase. Coding for markers of analogy can potentially help us to examine the relationship between the productive use of linguistic analogy and the acquisition of resemblance metaphors (Pouscoulous & Tomasello, Reference Pouscoulous and Tomasello2020; Di Paola et al., Reference Di Paola, Domaneschi and Pouscoulous2019).

As like is a marker used for both MRW Direct and MFlag, it is important to understand how its use differs in both contexts. Its use is coded as MRW Direct if followed by an MRW; if the word to follow it is not related to metaphor, like is coded as MFlag. It is also important to mention that although we expect most linguistic metaphors produced by children to be single-word, the classes proposed here (e.g., personification) could be further subdivided into single- and multi-word classes, most likely when studying data from older children.

Once all the linguistic metaphors have been identified, child metaphors should be re-examined to eliminate words and word combinations merely repeated after the caregiver who used them verbatim with the same meaning in one of the previous 10 turns (e.g., MOT: You’re such a monkey; CHI: I’m not a monkey), or any metaphors self-primed by those which the child repeated after the caregiver (e.g., any instances of I’m not a monkey the child produced repeatedly after the previous one). Also, names of books, shows, and characters from stories (e.g., Storm in a teacup, Wacky Warehouse, Bare Necessities, Little Red Riding Hood), as well as nursery rhymes (e.g., Like a diamond in the sky, Reach out for the stars) should be eliminated. In compiling an inventory of metaphors produced independently by each child, each linguistic metaphor production should be evaluated in light of the previous 10 caregiver turns to ensure it had not been primed, and in light of the previous recording to ensure it had been used independently at least once before and is on the way to becoming a more permanent feature of the child’s “metaphor-i-con”. Adopting this approach means that the first 10 lines of each recording, and the first recording in each corpus, should be disregarded from analysis.

At the next step, it is important to calculate overall metaphor types and tokens to examine the composition of each sample representing a given metaphor class and mapping. As the study of metaphor use is about meaning rather than form, we consider as metaphor type any morpheme or word, and all its grammatical variations, which are associated with one specific meaning; we consider as metaphor token each and every single instantiation of the given mapping or class. For example, when examining the class of primary metaphors, and the mapping of TIME IS MOTION, we take all grammatical versions of the verb come (as in The time has come) as instantiations of one metaphor type, and all grammatical variations of the verb arrived (as in Spring has arrived at last) as instantiations of another metaphor type. Meanwhile, the overall numbers of expressions for both metaphor types (e.g., come, came, coming, arrive, arrived, arriving) contribute to the overall number of metaphor tokens for this particular mapping. Distinguishing between type and token frequencies can help us to show how varied children’s metaphor pools are with respect to each metaphor class, or mapping.

Last but not least, all MRWs (e.g., Come here, honey) should be counted separately from their concrete equivalents (e.g., Drink some milk and honey) and compared against each other to justify the order of acquisiton of meanings in child’s own language.

4. Applying UBAMICS to Eleanor’s data

This section illustrates how UBAMICS works on corpus data available in the CHILDES Talkbank. The corpus analysed here is referred to as ‘Eleanor’ and it forms part of the MPI-EVA-Manchester corpus of interactions with English-speaking children, raised in the UK (e.g., Lieven et al., Reference Lieven, Salomo and Tomasello2009). The child nicknamed Eleanor was recorded at the ages of 2;0 (2 years) -3;1 (3 years and 1 month) on a dense sampling schedule: for 1 or 2 hours, five times a week in the first and last 4 weeks, and for 2 hours a week of every remaining month of the period. The corpus data consist of dyadic or triadic interactions between the child and her primary caregivers, totalling 179 recordings (179 hours). Metaphor is infrequent in conversation (Berber Sardinha, Reference Berber Sardinha2011; Steen et al., Reference Steen, Dorst, Herrmann, Kaal, Krennmayr and Pasma2010), which is why the use of densely sampled corpora increases the chances of metaphor detection and helps to make any claims of input–output relations more credible.

To establish the reliability of our coding scheme, transcripts based on the whole 179-hour corpus were coded independently by two coders, who were in regular contact about the coding procedure (i.e., unclear definitions in the code book were discussed without reference to examples from the corpus). When the results were compared for coding reliability, all checks showed near 0.97 agreement (Cohen’s kappa), with the largest disagreement within the category of sensory verbs (10%, i.e., 53 out of the total 540 metaphors not agreed on). This score is higher than either that of MIP (0.62 to 0.72 (Pragglejaz Group, 2007)) or MIP-VU (0.70 to 0.96 (Steen et al., Reference Steen, Dorst, Herrmann, Kaal, Krennmayr and Pasma2010)). The main reason for such a high score is that child data, and by extension also child-directed speech, are very basic so there were many linguistic metaphors used repeatedly whose occurrence seemed straightforward in classification. For example, the highly frequent metaphors such as There you go and Come on were coded in almost every single case as a combination of two elements: There (ACHIEVING A PURPOSE IS ARRIVING AT DESTINATION) and go (ACTION IS MOTION) or come (ACTION IS MOTION) and on (FUTURE IS ONWARDS). Following Steen et al. (Reference Steen, Dorst, Herrmann, Kaal, Krennmayr and Pasma2010), the latter example was only coded as non-metaphorical when it clearly reiterated the concrete verb spelled out elsewhere in the transcript (i.e., where Come on was used straight after Come!).

4.1. Metaphors identified in Eleanor’s speech

In total, 16,158 linguistic metaphors were identified in the corpus, of which 3,281 were produced by Eleanor. The remaining 12,877 metaphors were generated by her primary caregivers, her mother and father, as well as her aunts, uncles, cousins, and other children who occasionally came to visit and whose speech was all collapsed under the general term of ‘child directed speech.’ When primed metaphors were excluded from Eleanor’s spontaneous language use, the size of her productive” metaphor-i-con” was reduced to 2,784 metaphors. The proportions of unprompted productions seemed to increase in the first 9 months of the data sampling but clearly this trend was unstable, and there were times where the child relied more on repetitions (Figure 1).

Figure 1. Metaphors produced independently by Eleanor.

Appendices A–D further show the overall type and token frequencies of linguistic metaphors from different metaphor classes captured in Eleanor’s language during the data sampling. Also, examples are given to illustrate what types of linguistic metaphors Eleanor was capable of producing in the context of spontaneous conversation. Overall, the data show that some systematic metaphor production can already be captured at the age of two. The distinction between the use of conceptual and resemblance metaphors in Eleanor’s speech is very clear: Eleanor produced spontaneously 2,599 conceptual metaphors (93%) and 54 resemblance metaphors (2%) between the ages of 2;00 and 3;01, as well as 131 MFlags (5%). There are many classes of primary metaphoric expressions that Eleanor could already produce on her second birthday, with some produced regularly from the beginning of data sampling (see e.g., ACTION IS MOTION, MORE IS UP). At the same time, there are many primary metaphoric expressions which emerged only during the data sampling, and whose use was initially sporadic (e.g., FUNCTIONAL IS UP, GOOD IS LIGHT). Meanwhile, resemblance metaphors were used irregularly between the second and third birthday: nominal metaphors were extremely rare in production and the only subcategory where production was systematic from the start was that of ‘other’ resemblance metaphors usually encoded in verbs. Direct comparisons between UBAMICS and MIP or MIP-VU are not possible, as we have applied our manual to examine how metaphor frequencies compare across different metaphor types, whereas MIP and MIP-VU have compared frequencies of metaphors to those of non-metaphorical words. The data captured in Appendices A–D additionally show that UBAMICS is a useful tool for coding the speech of children aged two and onwards for at least three reasons. The tool reveals metaphor in high numbers; by distinguishing between the different metaphor classes, it also captures contrasts in their usage frequencies. Last but not least, it captures all metaphors produced by the child, both those used with and without the support of caregiver input.

At the following step, we show the use of all the metaphors produced independently by Eleanor and all the metaphors produced by her primary caregivers, starting with metaphor classes. Figure 2 shows that most of Eleanor’s metaphors have fallen into the same three classes that were most frequently heard in child-directed speech.

Figure 2. Linguistic metaphors recorded in child-directed and child’s own speech (by class).

Figure 3 additionally displays the 15 most frequent metaphor mappings in adult speech (from the most frequent on the left to the least frequent on the right) and the frequencies of the corresponding linguistic metaphors in Eleanor’s speech. The ACTION IS MOTION metaphor is the most frequent in both child-directed speech and in the child’s own speech, and there are further similarities between caregiver and child speech, but there are also some inconsistencies, likely caused by the complexity of some concepts. For example, the metaphor TIME IS SPACE is often invoked by the caregivers but less frequently adopted by the child herself, and so is the metaphor FINDING OUT IS SEEING.

Figure 3. Linguistic metaphors recorded in child-directed and child’s own speech (by mapping).

A closer look at the order of acquisition (Appendix E) shows that of the six most frequent metaphors in child-directed speech, four were already used spontaneously by Eleanor at the age of 2;00.03, whereas the two she had not acquired by that stage included TIME IS SPACE, and FINDING OUT IS SEEING, the same two which she used infrequently despite their high frequencies in caregiver input.

The last step of our analyses shows a sample of seven metaphoric expressions emerging in child speech in light of a) their concrete word equivalents, and b) the caregiver distribution of meanings (Table 2). This specific sample has been compiled from the pool of metaphors that emerged after the age of 2;06.00, as this allowed us to trace metaphorical meanings to their concrete equivalents in prior use. Personification could not be included in the sample as all its manifestations emerged in Eleanor’s language before the age of 2;06.

Table 2. Selected productive metaphors and their concrete and abstract meanings

The results show that Eleanor acquired concrete meanings of some words first (e.g., look, up, gone, bunny) and then extended her knowledge of these words by developing an additional abstract sense for the same word form. However, other metaphors (bet, fair) were acquired even though the child did not have concrete meanings of the MRWs in her productive lexicon. In all cases, the word meanings were developed in the order of their frequency of input, with concrete meanings often, but not exclusively, acquired first.