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BY 4.0 license Open Access Published by De Gruyter Mouton April 3, 2023

The associative system of early-learned Hebrew verbs and body parts: a comparative study with American English

  • Josita Maouene , Nitya Sethuraman EMAIL logo , Sigal Uziel-Karl and Shohei Hidaka
From the journal Cognitive Linguistics


This paper compares the associative system of early-learned verbs and body parts in Hebrew with previously published data on American English (Maouene, Josita, Shohei Hidaka & Linda B. Smith. 2008. Body parts and early-learned verbs. Cognitive Science 32(7). 1200–1216). Following the methodology of the former study, 51 Hebrew-speaking college students gave the first body part that came to mind for each of 103 early-learned Hebrew verbs, 81 of which were translational equivalents. Rate of convergence and divergence and underlying patterns were used to make inferences about the constraints at work. Overall convergence (92.3% of the Hebrew data and 93.7% of the English data) reveal similar entropy levels, comparable semantic field shapes of verbs organized by body parts and similar general cluster patterns of verbs by body parts. Most divergence lies in the infrequent responses (offered fewer than 1% of the time) which arise around body parts that are internal, very detailed, very general categorically, used in figurative language, uniquely provided and tend to be subject to cultural taboos. This is a new contribution, as previous work has not quantified the relative proportion of convergent to divergent associations. We discuss how these findings support neural and developmental continuity and stability in the verbal system with respect to the categorization of verbs by body parts cross-culturally.

1 Introduction

This paper describes and compares how early-learned verbs are associated with body parts in adult knowledge of Hebrew and English. We provide a quantification of the relative proportion of convergent versus divergent verb–body-part associations and discuss how this supports theoretical principles of developmental and neural continuity and categorization principles. At first glance, there may be no a priori reason to believe that the body, its subjective experience and its neural correlates are forces of convergence in adult verb–body-part associations. Arguably, there are many divergent linguistic forces acting on these associations, including the large diversity of cultural inventions in figurative meaning (e.g., idioms, similes, analogies, metonymies, metaphors), the combinatorics of co-occurring words as in latent semantics (Landauer and Dumais 1997) and the cultural relativism of the conception and categorization of body parts (Enfield et al. 2006). However, evidence from cross-linguistic behavioral studies suggests that embodiment matters for verbs, whether early-learned or not. Monolingual Cantonese and bilingual Cantonese-English adults in an image-verb matching task (using both early- and late-learned verbs) were slower to decide mismatches when the image and verb made primary use of the same effector, suggesting interference between actions that use the same effector (Bergen et al. 2010). In a second study, speakers of English, Japanese, Spanish and Dutch watched short video clips of a woman walking or running at different inclines and labeled the movement. Although a different number of labels was provided for each language, the categories overlapped, with speakers of all four languages labeling the movements using the same major distinction between gaits. This suggests that the semantic distinctions between walking and running verbs reflects real-world properties of joint segmentation of the leg and gait (Malt et al. 2008). In two further studies, the effects of relative embodiment in lexical and semantic processing were recorded for 687 English verbs (Sidhu et al. 2014) and 219 Japanese verbs (Mochizuki and Ota 2020).

Additionally, neuroimaging studies in children (James and Maouene 2009) and adults have shown a converging systematicity between certain body parts (hand, leg, mouth) and the motor systems (hand-, leg-, mouth-related premotor or motor region activations) in several different languages (English: Carota et al. 2012—a review of 12 studies; German and Dutch: De Grauwe et al. 2014; Chinese: Wu et al. 2013). Note, however, that some of these results have been found to be task- and context-dependent (Postle et al. 2008; see also Meteyard et al. 2012).

Finally, specific evidence from cross-cultural studies, summarized in Table 1, has found strong convergence of associations between early-learned verbs and parts of the body in several languages (English: Maouene et al. 2008; Mandarin Chinese: Chen and Zhu 2014; Japanese: Maouene et al. 2007). These studies revealed that adult speakers systematically associate early-learned verbs with a small, overlapping set of body parts. These body parts are organized around head, trunk, upper-limbs and lower-limbs, with particular emphasis on hands, a body part associated with around half of the inventory of early-learned verbs in each studied language. Similar results for 100 adult common verbs were also found in three Indian languages, Telugu, Hindi and Dakkini Urdu, as well as in English as a second language (Vasanta et al. 2011).

Table 1:

Main body parts/regions provided in directed associations examining verb-body-part associations in various languages.

Language Body part/region agreed upon by 50% or more participants Reference
American English
50 participants

101 early-learned verbs

61 body parts
hands, legs, eyes, mouth, ears

54% verbs associated with hand, fingers and arm

Total upper-limbs parts: 10
Maouene et al. (2008)
Mandarin Chinese
50 participants

169 early-learned verbs

76 body parts
hands, legs, eyes, mouth, ears, head, back, nose, waista

52.7% verbs associated with hand, arm and forearm

Total upper-limb parts: n/a
Chen and Zhu (2014)
50 participants

129 early-learned verbs

45 body parts
hands, legs, eyes, mouth, ear, face, mind, backb

46.5% verbs associated with hand, finger and arm

Total upper-limbs parts: 11
Maouene et al. (2007)
Hindi, Dakkini Urdu, Telugu, English as a Second Language
12 participants

100 adult verbs

About daily activitiesc
hands, legs, mouthd

58% verbs associated with hand, finger and nail

Total upper-limb parts: 3
Vasanta et al. (2011)
  1. aBack, nose and waist are associated with the Chinese equivalents of ‘carry on the back,’ ‘smell’ and ‘bow,’ respectively, which are absent from the English verb list. bBack is associated with the Japanese equivalent of ‘piggybacking,’ which is absent from the English verb list. cThis study only examined highly concrete verbs. dAll other body parts supplied by participants were combined into one category and not examined separately.

In these prior cross-linguistic associative studies, participants provided the most body parts in English, with all the other languages offering fewer body-parts associates on average. This raises the question of whether we would find different patterns of associations if speakers of a language provided more body parts in response to a set of early-learned verbs. Pilot data suggested that speakers of Hebrew, a language typologically different from previously studied languages, would provide a larger range of body parts and verb–body-part associations. Hebrew belongs to the Semitic language family, in which verbs are based on integrated, circumflex constructs of consonantal root plus affix in one of five morphological patterns called binyan conjugations, each of which is marked for the same rich system of inflections (Berman 1993). Thus, Hebrew provides an interesting morpho-syntactic contrast with previously studied languages.

The use of word associations to access adults’ and children’s organization of conceptual and linguistic knowledge has a long history in psychology of language (DeDeyne and Storms 2008; Deese 1965; Entwistle 1966; Nelson et al. 2004). The hypothesis is that association is one of the principles of memory organization, where “associated linguistic forms are generated as inferences, and as pointers to associated conceptual information” (Barsalou et al. 2008: 249). It is also a plausible model of learning and development (Rakison et al. 2008; see also Oakes et al. 2011). To collect data in Hebrew, we use the method of collecting directed associations used in the studies in Table 1, following the assumption that the correlations we elicit will reflect the regularities found in free associations in language and in the world (Deese 1965; Hills et al. 2010). We compare these results with English and anticipate to find a range of relationships, presumably in the same proportion in the two linguistic systems, such as an instrumental relation (linshox ‘bite’—shen ‘tooth’), an object relation (linshox ‘bite’—lashon ‘tongue’), first- and second-order perspectives (linshox ‘bite’—[your own] lexi ‘cheek’ vs. ‘bite’—[somebody else’s] ozen ‘ear’), different types of vicarious meanings learned through language and not observed (la’azor ‘help’—moax ‘brain’ or sexel ‘mind’) and figurative meaning (lishpox ‘spill’—lev ‘heart’). Consequently, we expect multiple types of organization and not just organization by proximity as originally reported in Maouene et al. (2008) for a subset of the body parts in English.

We ask adult native speakers of Hebrew to name the body part that comes to mind in response to a list of early-learned verbs and we compare these associations with the associative responses from adult native speakers of American English (results published in Maouene et al. 2008). We quantify convergence and divergence using three measures: entropy, to measure the degree of organization in the two systems; a correspondence analysis, to find the most important dimensions by which body parts cluster early-learned verbs, without a priori body categories; and a frequency analysis, to examine the overlap. To visualize the convergence and divergence in the two associative systems, we draw homunculi, or body maps, showing body parts proportional to the percentages of agreement.

We expect to find mostly overall convergence in the body regions that cluster early-learned verbs in Hebrew and English, with the most similarities around body-part terms at a mid-level of generality, which are fewer in number and faster to access (Rosch et al. 1976; Tversky and Hemenway 1984). This convergence may result from the diverse use of body parts in the context of human activities, including direct experience (e.g., seeing and touching body parts, sensing them internally, hearing or reading body part names) and vicarious experience (e.g., thinking, talking about and appraising body parts, remembering bodily experiences: Borghi and Cimatti 2010). Further, neurally, body parts follow multiple organization principles such as shape similarity, spatio-temporal proximity, synchronicity and functional similarity, among others (Bracci et al. 2015). Embodiment, in our perspective, includes morphological proximity, organizational similarity and continuity, but it can also be discontinuous, segmented, or unconscious (e.g., the organization of the homunculus in the motor cortex).

We also expect to see a small degree of divergence due to the presence of verbs unique to one of the languages (e.g., levarex ‘bless, congratulate’ and livxor ‘choose’ are found only in Hebrew; pretend, think and wish are found only in English). Divergence may be due to differences in conventions and gestures (e.g., eating, dancing, praying and other daily activities occur with different parts of the body in different cultures); cultural knowledge regarding the human body, mind, emotions and soul; cultural permissibility regarding referring to certain body areas; and slang meanings. Individual differences may also explain part of the divergence, as people may report highly emotional personal experiences with specific body parts, express their association at different levels of specificity or generality, or have different levels of knowledge and comfort regarding parts of the body.

Although the response patterns are from WEIRD participants (Western, Educated, Industrialized, Rich, Democratic countries) (Henrich et al. 2010), this work provides a method to report quantitatively cross-cultural convergence and divergence in associations between early-learned verbs and body parts at the junction of perceptual and linguistic categorization.

2 Method

2.1 Participants

Fifty one native speakers of Hebrew (34 female, 17 male), classified as mid-high SES, were tested at Ono Academic College, Kiryat Ono, Tel Aviv area, Israel. The American English data reported in Maouene et al. (2008) was collected from 50 native speakers of English (half female, half male) who were traditional-aged undergraduates tested in a large Midwestern college town in the United States. The Hebrew speakers tested were older than the English-speaking participants because many students in Israel perform military service before college.

2.2 Stimuli

The verbs in this study are the 103 early-learned verbs listed on the Hebrew Communicative Developmental Inventory (HCDI) (Maital and Dromi 1997; Maital et al. 2000), given in Table 2. The HCDI, an adaptation of the English MCDI (Fenson et al. 1994), is based on a stratified sample of 253 Israeli mothers reporting on their toddlers aged 12–24-months (Maital et al. 2000). Associative data for the 101 early-learned verbs listed on the English MCDI (Table 2) was previously collected and reported (Maouene et al. 2008). The English version is based on a sample of 1,803 children from 16 to 30 months as per parental report.

Table 2:

Verbs used to collect verb–body-part associations.a

81 Translational Equivalent Pairs b

Hebrew/English: linshox ‘bite’/bite, linshof ‘blow’/blow, lishbor ‘break’/break, lehavi ‘bring’/bring, livnot ‘build’/build, liknot ‘buy’/buy, litpos ‘catch’/catch, lirdof ‘chase, pursue’/chase, lenakot ‘clean’/clean, letapes ‘climb’/climb, lisgor ‘close’/close, lexasot ‘cover’/cover, livkot ‘cry’/cry, laxtox ‘cut’/cut, lirkod ‘dance’/dance, lecayer ‘draw’/draw, lishtot ‘drink’/drink, linhog ‘drive’/drive, lehapil ‘drop’/drop, leyabesh ‘dry’/dry, le’exol ‘eat’/eat, lipol ‘fall’/fall, leha’axil ‘feed’/feed, limco ‘find’/find, ligmor ‘finish’/finish, lehat’im ‘fit, match’/fit, letaken ‘fix’/fix, latet ‘give’/give, lalexet ‘go’/go, lisno ‘hate’/hate, lishmo’a ‘hear’/hear, la’azor ‘help’/help, lehaxbi ‘hide-transitive’/hide, lehakot ‘hit’/hit, lehaxzik ‘hold’/hold, lexabek ‘hug’/hug, lemaher ‘hurry’/hurry, likpoc ‘jump’/jump, liv’ot ‘kick’/kick, lenashek ‘kiss’/kiss, lidpok ‘knock’/knock, lelakek ‘lick’/lick, lexabev ‘like’/like, lehakshiv ‘listen’/listen, lehabit ‘look’/look, le’ehov ‘love’/love, la’asot ‘make’/make, liftoax ‘open’/open, licboa ‘paint’/paint, lesaxek ‘play’/play, limzog ‘pour’/pour, limshox ‘pull’/pull, lidxof ‘push’/push, lasim ‘put’/put, likro ‘read’/read, lirkav ‘ride’/ride, likro’a ‘rip, tear’/rip, laruc ‘run’/run, lehagid ‘say, tell’/say, lir’ot ‘see’/see, lashir ‘sing’/sing, lashevet ‘sit’/sit, lexayex ‘smile’/smile, lishpox ‘spill’/spill, la’amod ‘stand’/stand, la’acor ‘stop’/stop, letate ‘sweep the floor’/sweep, lisxot ‘swim’/swim, lehitnadned ‘swing’/swing, lakaxat ‘take’/take, ledaber ‘talk, speak’/talk, lit’om ‘taste’/taste, lizrok ‘throw’/throw, legdagdeg ‘tickle’/tickle, laga’at ‘touch’/touch, lexakot ‘wait’/wait, lehit’orer ‘wake up’/wake, lishtof ‘wash’/wash, lenagev ‘wipe’/wipe, la’avod ‘work’/work, lixtov ‘write’/write

Verbs unique to Hebrew (22 verbs)

levarex ‘bless, congratulate’, laxsom ‘block, stop’, lehavrish ‘brush’, leshanot ‘change’, livxor ‘choose’, lesarek ‘comb’, ligzor ’cut with scissors’, laredet ‘get down, go down’, lakum ‘get up, wake up’, lacet ‘go out’, lehixtabe ‘hide-intransitive’, lishmor ‘keep’, lehadlik ‘light, turn on light’, lin’ol ‘lock, put on’, lanu’ax ‘rest’, lexapes ‘search, look for’, lehistovev ‘spin, turn’, lehatiz ‘spray, move’, lehisha’er ‘stay’, lifshot ‘take (something) off’, lesaper ‘tell a story’, lexabot ‘turn off’

Verbs unique to English (20 verbs)

bump, carry, clap, cook, dump, get, have, pick, pretend, shake, share, show, skate, sleep, slide, splash, think, walk, watch, wish
  1. aWe chose to consistently transliterate Hebrew verbs into English using the formal pronunciation rather than the slang pronunciation (e.g., we transcribe the verb translated as ‘knock’ as lidpok rather than lidfok and similarly for other verbs that differ in their formal and slang pronunciations). bSome decisions had to be made in determining translational equivalent pairs. In the verb lists above, say is matched with lehagid ‘say, tell’ and lesaper ‘tell a story’ is listed as unique to Hebrew; cut is matched with laxtox ‘cut’ and ligzor ‘cut with scissors’ is listed as unique to Hebrew; hide is matched with lehaxbi ‘hide-transitive’ and lehixtabe ‘hide-intransitive’ is in the unique to Hebrew list.

To establish translational equivalency between the verbs, we used a modified version of the backward-forward translation method proposed by Brislin (1970) and expanded by Wang et al. (2015). For the forward translation, we asked two native Hebrew speakers with native-like proficiency in English to translate the verbs on the Hebrew CDI into English. For the backward translation, we concomitantly asked two different translators who were native English speakers with native-like proficiency in Hebrew to translate a list of English translations of the Hebrew verbs, based on the English CDI, which was provided by the third author.

In total, the Hebrew and English CDIs had 81 pairs of verbs judged as translational equivalents. Of the remaining verbs, 22 were semantically unique to Hebrew and 20 were semantically unique to English. As a final check, the four translators were asked to provide quality and difficulty rating scores for each translation based on two Likert scales. For each verb, we calculated an average among the raters and then averaged all the averages. For the forward translation (Hebrew to English), on average, 89.3% of the quality of the translational equivalents (TE) found was rated as high to very high (Mean: 4.7 where 5 = high quality), and 74.8% of the TE were found easily to very easily (Mean: 1.5, where 1 = very easy). In the backward translation (English to Hebrew), on average, 93.2% the quality of the TE found was rated as high to very high (Mean: 4.8) and 88.4 of the TE were found easily to very easily (Mean: 1.4).

2.3 Procedure

Hebrew-speaking participants were tested as a group in a computer lab. Participants were given a list of 103 verbs in a random order and asked to write down the body-part term they associated with each verb. Participants were naïve to the goals of the study and free to supply any body part at any level of scale (e.g., fingernails, arms, whole-body). They were provided with the following instructions: את/ה מתבקש/ת לכתוב עבור כל פועל לאיזה איבר גוף הוא מקושר אצלך.

At/a mitbakesh/et lixtov avur kol po’al le-eyze eyvar guf hu mekushar eclex/a ‘Please write next to each verb the body part with which it is associated for you.’ This follows the method used to collect data in Chinese and other languages, where participants were asked to write down their answers.

The English data (Maouene et al. 2008) was collected slightly differently, with participants requested to orally supply the first body part that came to mind in response to each verb, with the data recorded by the experimenter. Subjects heard the following instructions: You will hear a list of verbs. Please tell me the first body part that comes to mind. English-speaking adults were asked to provide oral responses so that this method could also be used with children for a subsequent study (Nesheim et al. 2012).

Hebrew speakers interpreted the instructions as that they could provide multiple parts, and 14% of the responses provided included more than one body part. In that case, we selected the first body part provided. By this method, participants offered 73 unique body parts in total, compared with 61 parts in English. To check whether more parts were provided in Hebrew due to the instructions, we went back through the 86% of responses that were single body-part terms and found all 73 parts provided in the single responses. Thus, we are confident that the ambiguity in the Hebrew instructions did not influence the higher number of body parts in Hebrew.

2.4 Reporting body-part terms

Synonyms (e.g., stomach, tummy, belly) and nested parts (e.g., lip, mouth, head) were counted as unique body-part terms but instances of singular and plural forms of the same noun (e.g., yad ‘hand’, yadayim ‘hands’) were counted as the same term. All body-part terms provided by the participants are reported in the singular form. We use italics for the Hebrew and English responses, with the English gloss in single quotes (e.g., sexel ‘mind’/mind). The body regions are in all capitals (e.g., ecba ‘finger’ and kaf yad ‘palm’ both belong to the YAD ‘HAND’ region). The verbs used and all the body parts provided are given in the Supplementary Materials I and II, with Hebrew script listed for Hebrew terms.

3 Results

Hebrew speakers offered more body parts per verb (73 uniquely different body-part terms for 103 verbs, M = 7.6 body parts per verb, range 1–13) than English speakers (61 body-part terms for 101 verbs, M = 6.4 body parts per verb, range 1–18). Using a Poisson regression, the results indicate that the expected log count of body parts per verb in English is 0.17 lower than the expected log count of body parts per verb in Hebrew, p = 0.0002. Alternatively, the incident rate ratio (IRR) for the number of body parts per verb in Hebrew is 1.8 times that of the IRR in English, p = 0.002. ‘No’ responses were given more often in Hebrew (0.15% in Hebrew; 0.04% in English).

The two systems of associations were analyzed using three measures: entropy comparisons and a principal component analysis suitable for categorical data (Section 3.1); a descriptive examination of the verb-body region associations in Hebrew (Section 3.2); and a descriptive comparison of frequent and infrequent verb–body-part associations in Hebrew and English using body map representations (Section 3.3).

3.1 Hebrew and English convergence relations: entropy and correspondence analyses

3.1.1 Entropy

To compare consistency in verb–body-part pairings within and across the two associative systems of responses, we calculated entropy, a measure of disorder and uncertainty in information. Higher entropy indicates greater randomness in the verb–body-part pairings and lower entropy indicates more order. The analysis consisted of calculating the entropy value for the actual choices that were made in both languages as compared with randomly simulated choices of answers. The rationale is that if a common constraint (e.g., real-world experiences in connection to body morphology) is influencing the actual participants’ decisions, the analysis should yield lower entropy values for the real answers compared with the randomly simulated ones. We calculated entropy using the formula H ( B ) = b 1 N P ( B b ) log P ( B b ) where P ( B b ) = B b / M is the sample probability of the body part B b out of N body parts with M ratings. The terms of the formula are as follows:

  • H (B) = entropy

  • M = the number of ratings

  • N = the number of body parts offered in each language

  • B b  = a body part

  • P (B b ) = sample probability of a B b for each verb.

We compared the entropy value in the actual participant responses with the entropy for 10,000 computer-generated random pairings, assuming independence of the verbs and body parts, such that any verb could be paired with any body part regardless of real-world context.

We found much lower mean entropy for the actual data sets in both languages, 1.50, compared to 5.11 for Hebrew and 5.47 for English in the randomly selected pairings (N = 10,000; Table 3). This suggests greater consistency in the actual data and greater divergence in the simulated verb–body-part pairings.

Table 3:

Entropy values of the Monte Carlo simulation for minimum, maximum and mean compared to the actual entropy values from the Hebrew and English samples.

English Hebrew
Monte Carlo Max 5.66 5.33
Mean 5.47 5.11
Min 5.22 4.78
Samples Max 3.35 3.11
Mean 1.47 1.48
Min 0 0.28

Comparing the entropy values of the Hebrew and English systems using a paired t-test revealed no significant difference in the magnitude of the disorder, t (204) = 0.1559, p = 0.87, SD = 0.7, CI [−0.1830, 0.2144]. This indicates that speakers of both languages, despite having no explicit constraints on their responses, strongly agreed overall with the body parts associated with the early-learned verbs. It also suggests that similar principles may be constraining the responses to comparable internal organizations in both languages and that some disorder is present together with high order in both systems.

3.1.2 Correspondence analysis

Next, we used a correspondence analysis (CA) to compare the dimensions accounting for the overall patterns, the overall shape, the similarity of the verbs with respect to their body-part associations and the similarity of the shared verbs relative to the shared body parts in the two associative systems. The CA is a dimension-reduction technique appropriate to categorical data based on positive and negative correlations and does not pre-determine categories based on body morphology or other types of body-part organization. The data in a high-dimensional set is reduced into a lower-dimensional description while maintaining the variance structure among individual instances. Note that correlations are sensitive to infrequent body parts such that a very infrequent response (e.g., kaved ‘liver’/liver for lishtot ‘drink’/drink in both languages) will be highly salient and load strongly on a dimension clustering verb belonging to a different region (in this example, around pe ‘mouth’/mouth). Thus, in the CA, there is room for divergence at the level of very unique responses.

The CA was performed on a contingency matrix of size m × n, where m is the number of rows (verbs) and n is the number of columns (body parts) for each language. Specifically, the matrix for Hebrew consisted of 103 verbs by 73 body parts and the matrix for English (Maouene et al. 2008) consisted of 101 verbs by 61 body parts. Both matrices were composed of “meaning” vectors created for each verb, comprising the number of participants listing each body part as associated with that verb. For example, the vector for the Hebrew verb lehavi ‘bring’ has the values [36-yad ‘hand’, 8-regel ‘leg’, 2-kaf yad ‘palm’, 1-pe ‘mouth’, 1-moax ‘brain’, 1-rosh ‘head’, 1-gapa ‘limb’] and the vector for the English verb bring has the values [38-hand, 9-arm, 2-finger, 1-brain]. Dimensions

In dimension reduction, the CA analyses indicate that the first four dimensions in each language accounted for 41% of the cumulative variance in Hebrew and 34.7% in English (Table 4); 8 dimensions accounted for 66% of the cumulative variance in Hebrew and 60% in English; and 20 dimensions accounted for 89.2% of the cumulative variance in Hebrew and 91.6% in English. This shows that a small and identical number of dimensions in both associative systems account for the majority of the variance. The higher variance in Hebrew is explained by the Hebrew speakers associating more unique body parts with certain verbs (body parts that are mentioned only once, e.g., shablul ‘cochlea’ with lishmo’a ‘hear’). However, it is important to note that English speakers had stronger agreements with one another and thus higher correlational patterns across the dimensions.

Table 4:

The correlations and cumulative variance for the first four dimensions of the CA.

Correlations Cumulative variance
Dimension English Hebrew English Hebrew
1 0.9427 0.8389 0.0997 0.1137
2 0.8576 0.7995 0.1904 0.222
3 0.7627 0.7259 0.2711 0.3204
4 0.7209 0.6499 0.3473 0.4084 Overall shape structures

Next, we examined the overall shape of the Hebrew and English structures. The CA enables a set of data to be displayed in three-dimensional graphical form by decomposing the chi-squared statistics associated with the contingency matrix into orthogonal factors based on the chi-square distance. Points further away from the centroid (0, 0, 0) contribute more to the dimension whereas points on the centroid contribute very little or nothing. The shape of the CAs exhibited by the first four dimensions in both Hebrew and English present highly similar 3-arm shapes with well-delineated clusters, shown in the 3D space in Figure 1a and b.

Figure 1: 
A 3D overall shape structure in (a) Hebrew and (b) English verb–body-parts associations. Dimensions 2–4 are shown. Scores along each dimension are standard deviations.
Figure 1:

A 3D overall shape structure in (a) Hebrew and (b) English verb–body-parts associations. Dimensions 2–4 are shown. Scores along each dimension are standard deviations.

These 3-arm structures in both languages represent clusters of verbs organized by mouth-related parts; hand- and leg-related parts; and eye-related parts. Each dot represents a verb, and closer distance between dots represents greater agreement in body-part association. Verbs in the same cluster have more similar body-part associations, with verbs higher in the clusters being more strongly associated with that body part. Verbs with different patterns of associations appear in different clusters and dots between clusters indicate associations with two or more body regions. Verb similarity structure within each language

Next, we examined the similarity structure of the verbs with respect to their body-part associations within each language. Each CA points to mainly convergence between the two languages, where the first four dimensions present a coherent and overall shared structure of verbs falling into four clusters. This data compression strongly suggests that within each CA, the two corpora of verbs are each systematically related to a small set of common body parts. The Hebrew semantic space organized by dimensions 2–4 with the 103 verbs identified is shown in Figure 2 (shown from a different perspective than Figure 1a, as these are dynamic views). For the 3D English semantic space, see Maouene et al. (2008: 1205).

Figure 2: 
The coordinate space derived from the CA for Hebrew with the complete list of the 103 verbs. Dimensions 2–4 are shown. Scores along each dimension are standard deviations. (For a corresponding figure which includes the complete list of the 101 verbs that make up the English semantic space, see Maouene et al. 2008: 1205).
Figure 2:

The coordinate space derived from the CA for Hebrew with the complete list of the 103 verbs. Dimensions 2–4 are shown. Scores along each dimension are standard deviations. (For a corresponding figure which includes the complete list of the 101 verbs that make up the English semantic space, see Maouene et al. 2008: 1205).

To make the structure revealed by the Hebrew CA in Figures 1a and 2 more intuitive, we describe some of the verbs and body parts associated with each cluster. The cluster that displays the highest specificity in pairing between body parts and verbs is organized by ozen ‘ear’ and shablul ‘cochlea’, driven by just two verbs, lehakshiv ‘listen’ and lishmo’a ‘hear’ (first dimension, not shown here.) The top-most cluster in Figures 1a and 2 contains verbs associated with eye and some eye parts. Verbs higher in the cluster are more strongly associated by participants with ayin ‘eye’ (e.g., lehabit ‘look’, lir’ot ‘see’). This is different from the English cluster, where a subcluster of head, brain and heart was loading on that dimension too. The cluster on the right side of Figures 1a and 2 shows the verbs associated mostly with legs and hands: the right-most verbs (e.g., lashevet ‘sit’, liv’ot ‘kick’) are more strongly associated with leg parts (e.g., regel ‘leg’, yashvan ‘bottom’); the verbs toward the center in the middle (e.g., letapes ‘climb’, lipol ‘fall’) are less strongly associated with leg parts and have additional associations with hand/arm parts (e.g., yad ‘hand’, katef ‘shoulder’); and going further into the center, the verbs in the mass (e.g., lisxot ‘swim’, la’avod ‘work’) are associated with hand parts (e.g., yad ‘hand’), a subcluster of which are verbs associated with rosh ‘head’, moax ‘brain’, sexel ‘mind’ (e.g., limco ‘find’, lexapes ‘search’) and with lev ‘heart’ (e.g., lisno ‘hate’, le’ehov ‘love’). Finally, the bottom left-most cluster in Figures 1a and 2 consists of verbs (e.g., lelakek ‘lick’, lit’om ‘taste’) strongly associated with mostly mouth parts (e.g., safa ‘lip’, lashon ‘tongue’).

The English CA (Maouene et al. 2008: 1205) also shows a 3-arm structure. In Figure 1b, the cluster on the left side shows verbs (e.g., lick, taste) associated with mouth parts. The middle cluster consists of verbs associated with both hand and leg parts, with the verbs at the top showing the highest correlations with leg parts, the verbs in the middle (e.g., climb, drive) having associations with both hand and leg parts, and the verbs at the lowest part of the cluster (e.g., sweep, throw, hit, knock) associated with hand parts (e.g., hand, arm, fist, knuckle). Finally, the cluster on the right side shows verbs strongly associated with eye (e.g., see, look, cry) and those nearer the center are associated with eye, brain and mind (e.g., think, pretend, wish) and with heart and mind (e.g., love, hate, like).

The similar structures found in the Hebrew and English CAs, as with the earlier similar entropy results, suggest that the principles driving these overall commonalities may be shared between the two associative systems. Note that in both systems, it is not just proximity of localization that determines these clusters, as body parts belonging to the same region can load onto different dimensions (e.g., zeret ‘little finger’ is clustered with ayin ‘eye’ for the verb likro ‘read’ in Hebrew; mouth and kidney are clustered with drink in English). Although we discussed the results of the four dimensions separately, in fact in both languages, they interact and combine in many semantic relationships, including fine-motor versus gross-motor activities (e.g., mouth and eye vs. hand and leg) and goal-directed functionality versus spatial exploration (e.g., hand and mouth vs. eye, hand and leg), among others. This suggests a multifaceted and distributed model of verbs organized by body parts. Shared similarity structures

In the previous section, the CA analyses showed that the verbs clustered by the body parts partitioned the two semantic spaces in similar ways. To bolster this finding and visualize whether the four dimensions align in the two systems, in Figure 3 we show a new CA graph using just the 81 translational equivalent verbs and the 42 shared body parts in Hebrew and English. We see again a 3-arm structure. In this solution, the left cluster is driven by mouth-, lip- and tongue-related verbs; the middle cluster is driven by eye-related verbs; and the right cluster shows verbs associated with leg parts. The two ear-related verbs are included in this space but are not easily visible from the perspective shown (they form the first dimension).

Figure 3: 
The 81 translational equivalents clustered by the 42 shared body parts. The Hebrew verbs are shown in green and the English verbs are shown in red. The coordinate space is derived from the CA organized by dimensions 2–4. Scores along each dimension are standard deviations.
Figure 3:

The 81 translational equivalents clustered by the 42 shared body parts. The Hebrew verbs are shown in green and the English verbs are shown in red. The coordinate space is derived from the CA organized by dimensions 2–4. Scores along each dimension are standard deviations.

Next, we ran a correlational analysis on the 81 shared verbs and the 42 shared body parts vectors. The bivariate correlations for dimensions 1 through 4 yield strong r values that range from 0.79 to 0.99, shown in Table 5.

Table 5:

Bivariate correlations for dimensions 1 through 4.

Dim 1 Dim 2 Dim 3 Dim 4
Hebrew Dim 1 0.99a
Dim 2 0.91a
Dim 3 0.79a
Dim 4 0.80a
  1. aThese correlations are significant at p < 0001.

To further support our claim that verbs overall can be clustered similarly by the shared body parts in both languages, we used a different clustering method, k-means, which is based on the distances to the centroid of the CA from the shared verbs and body parts. Five successive analyses were run, with k = 1, 2, 3, 4, 5. In Figure 4 below, a 3D plot with k = 4 shows a clear 4-arm structure. For both languages, cluster 1 (black and red dots) includes verbs related mostly to mouth parts; cluster 2 (black and red upward triangles) includes verbs mostly related to eye; cluster 3 (black and red squares) includes verbs mostly related to leg parts; and cluster 4 (downward triangles) includes verbs mostly related to hand parts. In this solution, leg-related verbs become separated from hand-related verbs, and head, mind, brain, heart are clustered with hand. Interestingly, k = 5 shows a subcluster of verbs related to hand separate from cluster 4. Most of the verbs in cluster 5 have a distribution that span multiple body regions or have whole-body in the vectors, a point we will return to in the next section. Of note, this algorithm does not output the ear-related verbs as a separate dimension.

Figure 4: 
The 3 dimensional CA space of 81 translational equivalents clustered by the 42 shared body parts using the k-means method, with k = 4. The English verbs are in black and the Hebrew verbs are in red. Different clusters are indicated with different markers (circles, squares, upward triangles and downward triangles). In these sets of coordinates, the scores along each dimension are normalized to have the unit measure equal one standard deviation.
Figure 4:

The 3 dimensional CA space of 81 translational equivalents clustered by the 42 shared body parts using the k-means method, with k = 4. The English verbs are in black and the Hebrew verbs are in red. Different clusters are indicated with different markers (circles, squares, upward triangles and downward triangles). In these sets of coordinates, the scores along each dimension are normalized to have the unit measure equal one standard deviation.

Finally, five verbs that differ between Hebrew and English in their main body region association (e.g., feed associated with mouth in English but yad ‘hand’ in Hebrew) appear in different clusters in Figure 4. We address these verbs in Section 3.2.2, which looks specifically at the divergence between the two systems.

To provide supportive statistical evidence, we then ran a series of three chi-square tests on the independence of the k by k cluster membership. We counted the pairs of verbs (e.g., lixtov ‘write’ in Hebrew, write in English) in the same cluster versus distributed independently (the null hypothesis). The reasoning was that if the cluster membership structures for the English and Hebrew verbs were independent, the joint probability distribution would follow the multiplication of the marginal probabilities of English and Hebrew verbs in the clusters. We tested this null hypothesis of English-Hebrew independence with the formula χ 2 = i , j n 2 ( p i , j p i p j ) 2 n p i p j which follows the chi-square distribution of the degree of freedom ( k 1 ) 2 . In all four cases, the null hypothesis was rejected by significantly small p values (p < 0.01). These results suggest that the cluster membership structure of Hebrew and English verbs was tightly correlated. Table 6 reports an example of the structure of these multiple comparisons.

Table 6:

Cluster membership of English and Hebrew verb pairs for k = 4. For the 81 translational equivalent verbs, the number of verb pairs in which the English verb is in cluster i and the Hebrew verb is in cluster j.

English-Hebrew Hebrew in cluster 1 Hebrew in cluster 2 Hebrew in cluster 3 Hebrew in cluster 4
English in cluster 1 13 0 0 0
English in cluster 2 0 5 1 0
English in cluster 3 0 0 11 1
English in cluster 4 2 0 0 48

In conclusion, the four measures used to examine the verbs and body parts common to both languages (CA, bivariate correlations, k-means clustering and the chi-square tests of independence) show mainly convergence and suggest that the principles driving these overall commonalities may be shared between the two associative systems. Divergence in the verbs and body parts is a point we examine in detail in Section 3.3.2.

3.2 Central body regions

Here we take a more detailed and descriptive look at which verb is associated with which central body region as identified by frequency of mention. We examine the fit of the Hebrew and English verbs to these central regions, examine whether verbs with similar meanings cluster similarly, and report exceptions.

3.2.1 Defining central body regions

We used frequency of mention to determine the most-frequent body-part terms given in Hebrew and English and grouped these into central body regions. Fourteen body-part terms in Hebrew and 16 in English were mentioned 1% out of the total number of responses. These body parts comprise the same eight central body regions in both languages and are listed in Table 7.

Table 7:

Eight central body regions defined by the most-frequent body-part terms, those mentioned ≧1% of the total number of responses.

Central body regions [Hebrew/English] Most-frequent body parts in Hebrew Most-frequent body parts in English
YAD ‘HAND’/HAND yad ‘hand’, ecba ‘finger’, kaf yad ‘palm’ hand, finger, arm
REGEL ‘LEG’/LEG regel ‘leg’, yashvan ‘bottom’ leg, bottom, feet
PE ‘MOUTH’/MOUTH pe ‘mouth’, safa ‘lip’, lashon ‘tongue’ mouth, lips, tongue
AYIN ‘EYE’/EYE ayin ‘eye’ eyes
OZEN ‘EAR’/EAR ozen ‘ear’ ears
ROSH ‘HEAD’/HEAD rosh ‘head’, moax ‘brain’ head, brain, mind
LEV ‘HEART’/HEART lev ‘heart’ heart
KOL HA-GUF ‘WHOLE-BODY’/WHOLE-BODY kol ha-guf ‘whole-body’ whole-body

3.2.2 Verb clusters as determined by central body regions

The eight central body regions defined in Table 7 were used to fit the Hebrew and English verbs to body regions based on a 50–100% agreement threshold between participants. In total, 92.2% (95 of 103) of the Hebrew verbs and 93.1% (94 of 101) of the English verbs met this threshold and were attributed to just one central body region. An independent Mann-Whitney U test indicated that the distribution of percentages per central body region did not differ across the two languages, U = 29.5, p = 0.793. As detailed in the Supplementary Material IIIa, half the verbs in each language are associated with just the YAD ‘HAND’/HAND region, with a smaller proportion of verbs associated with only the REGEL ‘LEG’/LEG or PE ‘MOUTH’/MOUTH regions. In both languages, a dozen verbs were associated with each of the AYIN ‘EYE’/EYE, OZEN ‘EAR’/EAR, ROSH ‘HEAD’/HEAD and LEV ‘HEART’/HEART regions.

KOL HA-GUF ‘WHOLE-BODY’/WHOLE-BODY is mentioned more than 1% of the time in both languages but no verbs meet the 50–100% agreement threshold for this region. Instead, KOL HA-GUF ‘WHOLE-BODY’/WHOLE-BODY is important for verbs in both languages that span multiple central regions, including verbs that involve upper- and lower-limbs, as well as some eye-related verbs. As reported in Supplementary Material IIIb, eight (7.8%) Hebrew and seven (6.9%) English verbs did not meet the 50% agreement threshold with any one central body region. KOL HA-GUF ‘WHOLE-BODY’/WHOLE-BODY is an important associate for 6 of these 15 verbs and all 15 verbs are associated with multiple body regions that span the body.

3.2.3 Do verbs with similar meanings cluster similarly?

Next, we examine whether verbs with similar meanings in the two languages share similar central body region associates, based on the regions listed in Table 7. Eighty one verbs in each language (out of 103 in Hebrew and 101 in English) were translational equivalents. Of these, 73 pairs (90.1%) matched, suggesting a fairly strong agreement between the two languages for the clustering of the translational equivalent verbs by central body regions. The correlation between the region-based percentage of verb–body-part agreement for the matching 73 verb pairs yielded a significant coefficient of r = 0.73, p = 0.001 (1-tailed). Further, to compare the distribution of agreement within the translational equivalent pairs, we used the Kolmogorov-Smirnoff test, a general non-parametric method sensitive to differences in both location and shape of the empirical cumulative distribution functions of the two samples. The test showed that the distribution was not significantly different, SK = 0.94, p = 0.35 (one-tailed). In other words, despite some differences in percentage of agreement for verb–body-part pairings, overall, the two systems of associations show intriguing similarities in their actual percentages.

Five pairs of translational equivalents did not match in body region and are listed in Supplementary Material IIIc. These pairs show divergence in the first-most associated body region pairing but are not random when considered in terms of body morphology and cultural practice (e.g., the action described by leha’axil ‘feed’/feed involves both pe ‘mouth’/mouth and yad ‘hand’/hand, with Hebrew speakers focusing more on pe ‘mouth’ and English speakers placing more emphasis on hand).

In sum, the two associative systems have similar organizational structures (entropy analysis); the same first four dimensions show clustering of early-learned verbs in the two systems (correspondence analysis); and the same eight central body regions, defined by the most-frequent body parts mentioned, cluster most verbs similarly in the two systems.

3.3 The body parts

Hereafter we focus on the latent structure created by the body parts. We discuss the congruence and divergence in the body parts, examined with respect to most-frequent and least-frequent terms, and show body maps for easier visualization. The body maps are constructed based on the number of mentions of each body part in the total data set, such that the size of a body-part term is proportionally larger when mentioned by a greater number of participants. Details on the construction of these body maps are given in Supplementary Material IV.

3.3.1 Most-frequent body-part terms

The 14 most-frequent body parts in Hebrew and the 16 in English (those mentioned 1% or more in each data set and comprising the central body regions in Table 7) account for the majority of responses in both languages (Hebrew: 92.3%; English: 93.7%). These most-frequent body-part terms are given in Table 8 with ranking and frequency of occurrence.

Table 8:

Ranking of the most-frequent body-part terms (those mentioned ≧1% of the total number of responses) in Hebrew and in English, by frequency.

Hebrew English
Rank Body part Frequency Rank Body part Frequency
1 יד yad ‘hand’ 40.4% 1 hand 32.7%
2 רגל regel ‘leg’ 14.1% 2 arm 9.4%
3 פה pe ‘mouth’ 8.9% 3 eye 8.7%
4 עין ayin ‘eye’ 5.7% 4 leg 8.5%
5 ראש rosh ‘head’ 4.8% 5 mouth 8.2%
6 מוח moax ‘brain’ 3.8% 6 foot 6.6%
7 לב lev ‘heart’ 3.6% 7 finger 3.0%
8 אצבע ecba ‘finger’ 1.9% 8 bottom 2.9%
9 אוזן ozen ‘ear’ 1.8% 9 whole-body 2.3%
10 ישבן yashvan ‘bottom’ 1.8% 10 mind 2.3%
11 כל הגוף kol ha-guf ‘whole-body’ 1.4% 11 ear 2.1%
12 שפה safa ‘lip’ 1.4% 12 head 1.8%
13 לשון lashon ‘tongue’ 1.4% 13 tongue 1.7%
14a כף יד kaf yad ‘palm’ 1.3% 14 heart 1.3%
15 lip 1.1%
16b brain 1.1%
  1. aGav ‘back’ is the 15th most-frequent term in Hebrew, with 0.9% of the total responses. bTooth is the 17th most-frequent term in English, with 0.75% of the total responses. The sum of the percentage of mention of hand plus arm in English is 42.1% and the sum of yad ‘hand’ pluszro'a ‘arm’ (+0.04%) in Hebrew is 40.44%. Similarly, the sum of leg plus foot in English is 15.1% and the sum of regel ‘leg’ plus kaf regel ‘foot’ (+0.02%) in Hebrew is 14.13%. Both of these show high levels of convergence.

Except for three body-part terms (zro’a ‘upper arm’ and kaf regel ‘foot’ in Hebrew; palm in English), the most-frequent terms were translational equivalents, albeit with different rankings. The majority of the most-frequent body parts fall into the same three body areas: upper-limbs (yad ‘hand’/hand, ecba ‘finger’/finger; Hebrew: kaf yad ‘palm’; English: arm); lower-limbs (regel ‘leg’/leg; yashvan ‘bottom’/bottom; English: foot); and head (ayin ‘eye’/eye, pe ‘mouth’/mouth, ozen ‘ear’/ear, safa ‘lip’/lip, lashon ‘tongue’/tongue, moax ‘brain’/brain, rosh ‘head’/head; English: mind). The last two terms are lev ‘heart’/heart and kol ha-guf ‘whole-body’/whole-body. Visually, in both systems, these most-frequent terms span one large-size surface (whole-body), most medium-size surfaces (leg, arm, head), some small-size surfaces (eye, finger, lips, tongue, etc.) and internal organs (brain, mind, heart).

The top most-frequently mentioned body-part term in both languages was yad ‘hand’/hand, accounting for 40% of all responses in Hebrew and 33% of all responses in English and was given more than twice as often as the next most-frequent response in each language. Although the list of most-frequent parts in the two languages has significant overlap, there are also clear differences, starting with the second most-frequent part (Hebrew: regel ‘leg’, 14%; English: arm, 9.4%). Additionally, ayin ‘eye’/eye and kol ha-guf ‘whole-body’/whole-body occur more often in English; moax ‘brain’/brain and lev ‘heart’/heart more often in Hebrew; sexel ‘mind’/mind is most frequently mentioned in English but doesn’t make the list in Hebrew; and kaf yad ‘palm’/palm is most frequently mentioned in Hebrew but below the threshold in English.

There are further differences in how Hebrew and English speakers talk about the limbs, following a common divergence in languages around the world (e.g., approximately one-third of the world’s languages studied do not have separate terms for hand and arm, Brown 2005). English has separate terms for arm and hand and for leg and foot. By contrast, Hebrew speakers use yad ‘hand’ to also refer to the whole arm. Additionally, Hebrew speakers use two additional terms for the upper limbs: zro’a ‘upper arm’ which refers to the area between the shoulder and the elbow and ama ‘forearm’ which refers to the area between the elbow and the wrist. For the lower-limbs, Hebrew speakers gave regel ‘leg’ which refers to the area from the hip joint to the foot and includes the foot, and kaf regel ‘foot’ which refers to the foot only, like leg and foot in English. Our data suggests that the two languages linguistically carve up the limbs in different ways, which may reflect differences in how actions are conceptualized: whereas hand, arm, leg, and foot occur on the most-frequent list in English, in Hebrew, only yad ‘hand’ and regel ‘leg’ are most frequently associated with our list of early-learned action verbs; zro’a ‘upper arm’, ama ‘forearm’ and kaf regel ‘foot’ instead appear on the least-frequent parts list.

The high degree of congruence in the most-frequent Hebrew and English body parts is more easily visualized with body maps, shown in Figure 5, using the percentages of mention given in Table 8. These maps are constructed based solely on the number of mentions of each body part in the total data set—that is, the depicted size of the body-part term is proportionally larger when a greater number of participants have mentioned it. We followed morphological localization in upper-middle-lower body regions as an important organizational principle, but many other principles are also at work here, including function, shape, perspective, naming and emotions. We tried to render that complexity: The maps display both continuities and discontinuities with multi-perspective overlays of internal and external parts, front and back parts, and first and second order perspectives (e.g., bite [one’s] lip vs. bite [another’s] ear), along with some continuity (torso, whole-body). The maps were drawn by Yangfei Song, a bilingual Chinese-English speaker who does not know Hebrew, in close collaboration with the first authors. The artist was directed to map size onto frequency of mention (Table 8) by using one body part (brain) as the standard unit of measurement to draw the other body parts in the homunculus. The original size of the brain was 3 cm by 1.25 cm. The full homunculus was resized to create Figure 5a. The homunculi in Figures 6 and 7 (shown proportionally to each other in 5b and 5c, respectively, and proportional to 5a) were drawn following a similar process. Details on the construction of all the homunculi are given in Supplementary Material IV. Note that our body maps do not reflect any judgment by our speakers as to the exact boundaries of the terms. Rather, they are meant to provide quick and direct visualization of the proportionate size of the body-part terms based on the number of mentions, as an illustration of the complexity of the associations.

Figure 5: 
Body maps depicting body parts associated with early-learned verbs in Hebrew (always shown on the left) and English (always shown on the right). All maps are shown here at scale with respect to each other. Figure 5a shows body maps depicting the most-frequent body parts, representing 92.3 and 93.7% of the data in Hebrew and English, respectively. Least-frequent body parts are shown in two maps, Figure 5b and c, which are shown much larger in Figures 6 and 7, respectively. Figure 5b shows body maps depicting the non-integrative least-frequent body parts, representing 7 and 6.2% of the data in Hebrew and English, respectively. Figure 5c shows body maps depicting the integrative least-frequent parts, representing 0.6 and 0.04% of the data in Hebrew and English, respectively. See Supplementary Material IV for details on how these body maps were constructed.
Figure 5:

Body maps depicting body parts associated with early-learned verbs in Hebrew (always shown on the left) and English (always shown on the right). All maps are shown here at scale with respect to each other. Figure 5a shows body maps depicting the most-frequent body parts, representing 92.3 and 93.7% of the data in Hebrew and English, respectively. Least-frequent body parts are shown in two maps, Figure 5b and c, which are shown much larger in Figures 6 and 7, respectively. Figure 5b shows body maps depicting the non-integrative least-frequent body parts, representing 7 and 6.2% of the data in Hebrew and English, respectively. Figure 5c shows body maps depicting the integrative least-frequent parts, representing 0.6 and 0.04% of the data in Hebrew and English, respectively. See Supplementary Material IV for details on how these body maps were constructed.

Figure 6: 
Enlarged body maps depicting the non-integrative least-frequent pairings of verbs and body parts in Hebrew (left) and English (right). Figure 6 shows the same maps as Figure 5b, larger here for easier viewing. These maps are proportional with respect to each other but not shown to scale with respect to Figure 5. These figures represent 7 and 6.2% of the data in Hebrew and English, respectively. See Supplementary Material IV for details on how these body maps were constructed.
Figure 6:

Enlarged body maps depicting the non-integrative least-frequent pairings of verbs and body parts in Hebrew (left) and English (right). Figure 6 shows the same maps as Figure 5b, larger here for easier viewing. These maps are proportional with respect to each other but not shown to scale with respect to Figure 5. These figures represent 7 and 6.2% of the data in Hebrew and English, respectively. See Supplementary Material IV for details on how these body maps were constructed.

Figure 7: 
Enlarged body maps depicting the integrative body parts in Hebrew (left) and English (right). Figure 7 shows the same maps as Figure 5c, here larger for easier viewing. These maps are proportional with respect to each other but not shown to scale with respect to Figure 5. These figures represent 0.6 and 0.04% of the data in Hebrew and English, respectively. See Supplementary Material IV for details on how these body maps were constructed.
Figure 7:

Enlarged body maps depicting the integrative body parts in Hebrew (left) and English (right). Figure 7 shows the same maps as Figure 5c, here larger for easier viewing. These maps are proportional with respect to each other but not shown to scale with respect to Figure 5. These figures represent 0.6 and 0.04% of the data in Hebrew and English, respectively. See Supplementary Material IV for details on how these body maps were constructed.

3.3.2 Least-frequent body parts

Most of the body parts in the data were mentioned very infrequently, with the tail of the distribution being longer for Hebrew speakers than English speakers. Hebrew speakers provided more terms overall: 57 body parts (rankings 15–72, mentioned between 50 and 1 times) comprised 7% of the total responses in Hebrew, and 45 body parts (rankings 17–61, mentioned between 38 and 1 times) comprised 6.2% of the total responses in English. These least-frequent parts are discussed in two groups. The majority of the terms, 51 Hebrew and 43 English terms, respectively 7.0 and 6.2% of the data, refer to non-integrative parts. The remaining integrative terms (6 in Hebrew, 2 in English, respectively 0.6 and 0.04% of the data) span multiple body areas or refer to immaterial parts. Below we discuss differences between Hebrew and English in the least-frequent parts offered and depict them in body maps. Discrete areas

Quantitatively, Hebrew speakers provided more terms at comparable percentage levels to English speakers in the head area (Hebrew: 14 terms, 2.3%; English: 10 terms, 2.4%) and in the upper-limbs (Hebrew: 13 terms, 1.4%; English: 8 terms, 1.4%). Hebrew speakers and English speakers are comparable in the number of body-part terms but differ in their percentage of mention for parts of the torso (Hebrew: 19 terms, 2.7%; English: 17 terms, 1.5%) and lower-body (Hebrew: 6 terms, 0.6%; English: 7 terms, 0.9%). Within these areas, Hebrew-speaking participants supplied nearly twice as many internal part terms (Hebrew: 16 terms, 0.8%; English: 9 terms, 0.6%). Hebrew speakers mentioned internal body parts in the head (meytar kol ‘vocal cord’, shablul ‘cochlea’, ishon ‘pupil’, lo’a ‘gullet’) and torso/trunk area (kaved ‘liver’, me’i ‘intestine’, rexem ‘womb’, re’a ‘lung’, txol ‘spleen’, kilya ‘kidney’, agan ‘pelvis’, beten ‘stomach’), as well as skeletal parts (amud shidra ‘spine’, cela ‘rib’, shixma ‘shoulder-blade’, mifrak ha-yarex ‘hip joint’). English speakers mentioned internal parts in three areas, in the head (larynx, tonsil, tastebud), torso/trunk (stomach, gut, liver, lung, abs), and lower-limbs (quads). Integrating parts

Hebrew speakers provided three times as many integrative body-part terms and mentioned them much more frequently (Hebrew: 6 terms, 0.6%; English: 2 terms, 0.04%). These terms span multiple areas (shrir ‘muscle’; skin, bone), are at a superordinate level of linguistic categorization (Rosch et al. 1976; Tversky and Hemenway 1984) (ever min ‘genital’, male and female; gapa, ‘limb’; guf taxton ‘lower-body’), and refer to immaterial parts (neshima ‘breath’, neshama ‘soul’). Body maps of least-frequent parts

For easier visualization of the variation found between the least-frequent body parts, we show two body maps, one for the non-integrative parts which comprise the majority of the least-frequent terms (Figure 6) and one for the integrative parts (Figure 7). As with the body maps for the most-frequent parts, these maps are drawn such that the depicted size of the body-part term is proportionally larger when a greater number of participants have mentioned that particular body part. Overall, the least-frequent body-part maps look more divergent than the most-frequent body maps, with the map resulting from the Hebrew data appearing more continuous. The distribution of infrequent responses shows that the Hebrew speakers used more integrative labels for parts at the surface of the body but used more discriminative labels for internal, hidden parts.

4 Discussion

In this study, we quantified in detail the overall shared way in which early-learned verbs in Hebrew and English adult language pattern with associated body parts, despite constraints that should act as forces of divergence. We found high systematicity and coherence between the two languages as well as meaningful differences. This is a new contribution, as previous work has not quantified the relative proportion of convergent to divergent associations between verbs and body parts. We discuss how these findings support neural and developmental continuity and stability in the verbal system with respect to categorization by body parts. We also consider the possible influence of different organizing principles on verb–body-part associations and their relation to development.

4.1 Convergence

Three results characterize large similarities in the clustering of verbs by body parts in the Hebrew and English associative systems: 1) similar internal organizations, as operationalized by comparable levels of entropy; 2) similarities in clustering for the first four dimensions organizing action verbs, contributing to shape similarities of the overall patterns of associations; and 3) strong agreement for the most-frequent body parts and the central body regions categorizing verbs.

These converging results may be due to similar organizing principles. One important principle comes from the organization of the somatopic maps in the neural organization of adults, where body parts are well-represented components of multiple distributed systems in the cortical and subcortical areas. Somatopic maps are reported in the sensory-motor strips (Marshall et al. 1937; Penfield and Boldrey 1937), in the occipitotemporal cortex for visual representation (Orlov et al. 2010) and in the cerebellum (Holmes 1918). These cortical and subcortical regions are connected with our own actions or the actions of others. From a developmental neural perspective, continuity in the somatosensory map has been reported, together with some developmental remapping in infants (Rigato et al. 2014). Further, recent fMRI evidence in newborn macaques shows that the topographic sensorimotor maps are present at birth and that these body maps are mostly indistinguishable from those in older macaques. Further, the embodied perspectives in cognitive neuroscience claim that the circuits used for functions such as planning, sensing, calibrating and selecting an action (in the supplementary sensorimotor area, SMA and premotor cortex, PMC) are either simulated for semantic grounding (Barsalou 1999; Gallese and Lakoff 2005; Glenberg and Kashak 2002) or are related to the word form that becomes associated with association regions related to perception and action through Hebbian learning (Pulvermüller 2005). Our data does not support the opposite perspective, which separates the body from language and takes the view of a wired brain that takes in information from culture and the environment independently of spatial or temporal activation (for a review and discussion of the many embodied and disembodied accounts of semantics, see Meteyard et al. 2012). By this view, less convergence between verbs and body parts should be found across different languages and cultures which is not what we find in our data.

A second organizing principle that contributes to convergence is latent semantics and distributional cues. Studies suggest a connection between early-learned and concrete words and later-learned abstract words, where early-learned concrete words derive from neural activity in domain-specific sensorimotor brain regions (Hazy et al. 2020). As later-learned verbs enter the semantic system, they form new connections with these early pathways, including figurative and more abstract connections. Additionally, later-learned abstract terms may be partially learned through linguistic cues, including distributional statistics, such as lexical co-occurrences (Hazy et al. 2020) and social and inner experiences (Borghi and Binkofski 2014). For example, the body and its parts are involved in primary metaphors, which emerge from early embodied experiences and are often shared cross-culturally; an illustration of this is the early-learned metaphor happy is up, sad is down, which correlates with a child energetically exploring the world while running upright and feeling happy versus lying down when tired or unhappy (Lakoff 2012). Such distributional cues establish important pathways and early neurological wiring during the sensitive period in the experience-dependent brain. Primary metaphors continue to be active in adult metaphoric associations and are building blocks for complex and general conceptual metaphors where the body and its parts can be understood as containers, objects, conduits, etc. (Lakoff and Johnson 1980). Body parts continue to be found overtly in adult figurative expressions and associations (e.g., help is at hand, don’t mind me, I put my foot in my mouth, I heart ice cream). For example, in our data and all the languages studied to date (see Table 1), the prevalence of the hand response in adults could stem from the early concrete association between verbs and body parts in daily experiences, as well as from more abstract and figurative associations resulting from distributional learning. If developmental continuity is at play, we predict we should see similar internal organization and shape similarities in the overall patterns of associations in later-learned and abstract verbs (e.g., imagine, judge, manipulate, gamble). Additionally, while we would continue to see many verbs associated with hand, we would see an increased importance of more abstract body-part terms, such as mind, eye and heart, and potentially more verbs would span multiple body regions.

In addition to neural-developmental-semantic principles, linguistic and perceptual categorization also contribute to the convergence we find. Cognitively, the interaction between categorization and naming would predict maximal similarities around mid-level body-part terms, as those would be accessed faster in our associative task (Rosch et al. 1976; Tversky and Hemenway 1984). In fact, in our data most of the terms provided are at the mid-level of generality, with very few detailed (e.g., tastebud, vocal cord, abs) or very general (e.g., genital, upper-limb, lower-body) terms. Additionally, perceptual categorization, in particular visual, also pushes for convergence. In our data, the most-frequently named parts are mostly surface ones: one large-size surface (whole-body), a range of medium-size surfaces (e.g., leg, head, hand, eye, finger), and very few smaller surface parts or visual details (nails, but no blemishes, pimples, wrinkles, cuticles, etc.). The upper and lower limits of categorization may be due to a confluence of influences, such as labels of the first body parts learned, importance of surface similarities in children, frequency of usage and attention to certain parts, similar cultural frames of attention where some details are focused on more than others, comparable proprioceptive awareness, and sparse time resources in experimental tasks such as ours.

Another organizing principle driving convergence in verb–body-part associations may be having a shared concept of the body organized in parts (Goddard and Wierzbiecka 2016; Wierzbicka 2007). The convergence in our data may be due to Hebrew and English speakers using the label whole-body and conceiving of the body in terms of a mereology, or part-whole relationships (e.g., a fingernail is part of a finger, which is part of a hand). However, note that talking about the whole-body and conceiving of it as a collection of part-whole relationships does not hold in all languages and cultures, as some languages refer to the body as ‘person’, and a body part is a ‘thing that is located’, such that a nose is not part of the head, but a thing located on the head (Goddard and Wierzbiecka 2016; Wierzbicka 2007). Additionally, some cultures delineate limbs at different points on a body map or do not have terms for limbs (e.g., Majid 2010), suggesting that part-whole relationships may not be a primary means of discussing the body in such languages, and specific cultures may differ in the labeling, levels and relationships between parts, or whether they even think of the body as comprising parts (see Enfield et al. 2006).

4.2 Divergence

Although it is striking to find large convergence in the two adult associative systems, we also see noteworthy differences in the body-part terms and low frequency verb–body-parts pairings. Below we discuss 1) the divergence in the number and type of body-part terms provided and 2) the most significant differences in the body-part terms and verb pairings.

First, we find that Hebrew speakers mention more body parts per verb than English speakers overall, and the responses in Hebrew include a larger proportion of internal and integrating body-part terms. These differences between the two associative systems may stem from individual differences within the sample, such as personality (e.g., being detail-oriented or wanting to stand out), particular experiences (e.g., recent surgery or health-related issues) and tolerance for experimental tasks. Divergence may also arise from cross-cultural differences in knowledge of the body, comfort-levels in talking about certain body parts (e.g., genitals), historical traditions and cultural practices shaping somatic awareness and interoceptive sensitivity and accuracy (Ma-Kellams 2014) to internal organs. Finally, preferences for abstraction may also account for the divergence in the integrative body-part terms (e.g., lower-body, genitals, skin), reflecting small but important linguistic differences in using more superordinate terms referring to general body regions.

Some significant and interesting divergences are found in specific body parts and their verb pairings. The YAD ‘HAND’/HAND region in the two languages shows significant differences, particularly based on cultural variations in everyday actions and coded gestures, for example, in dancing (e.g., with the belly, holding hands in circle, focused on the legs), eating (e.g., with three fingers, with the whole hand, with utensils) and praying (e.g., palms joined, bowing, kneeling on the floor). Kaf ha-yad ‘palm’ is especially salient in Hebrew, occurring in the most-frequent list and associated with 39 verbs, compared with palm in English which only occurs with 2 verbs (push, splash). Among Hebrew speakers, there may be a heightened and shared awareness of kaf yad ‘palm’, which is talked about in religious, sacred practices (e.g., blessing, praying) and might increase its salience even in very mundane activities (e.g., knocking, pushing, putting, writing, brushing, cutting).

We also find differences for terms for genitals, with Hebrew speakers mentioning more types of sexual parts and to a greater proportion than do English speakers. These differences may be due to factors including differences in age, cultural taboos or a greater comfort with talking about sex and sexual parts among Hebrew speakers and individual differences (e.g., humor and fatigue, among others). Although speakers of both languages mention pin ‘penis’/penis, Hebrew speakers mention more types of female parts, perhaps due to 67% of the participants being female. The female genital terms provided are pot ‘vulva’, dagdegan ‘clitoris’ and rexem ‘womb’ and the integrating term ever min ‘genital’ which refers to both male and female parts. The terms pot ‘vulva’ and ever min ‘genital’ are used with lidfok (lidpok) ‘knock’ which in slang means ‘shag, have sex.’ Pot ‘vulva’ is also associated with laredet ‘get down, go down’, lelakek ‘lick’, lirkav ‘ride’, lidxof ‘push’ and ligmor ‘finish’, as well as with lexasot ‘cover’ and lishtof ‘wash’. In sexual parts provided in common between the two languages, we find differences in verb associations, for shad ‘breast’/breast (Hebrew: litpos ‘catch’, le’ehov ‘love’; English: buy) and pin ‘penis’/penis (ligmor ‘finish’/finish in both languages, but also in Hebrew, lidfok (lidpok) ‘knock’, lasim ‘put’ and lexabot ‘turn off’). English speakers but not Hebrew mention nipple (associated with feed).

Another difference we find is that Hebrew, but not English, speakers use integrating terms referring to the immaterial parts neshima ‘breath/breathing’ (associated with le’ehov ‘love’, laruc ‘run’, lashir ‘sing’, la’acor ‘stop’) and neshama ‘soul’ (associated with lacet ‘go out’, la’azor ‘help’); both terms share the same consonantal root n-sh-m. These associations suggest a dynamic interaction in Hebrew between physicality (the respiratory system) and cultural, religious and linguistic conventions. The importance of breath and breathing is found in Hebrew in expressions such as kax neshima amuka ‘take a deep breath, relax’. Neshama ‘soul’ also has figurative meaning, such as referring to a person as good-hearted and to express extra familiarity in order to ask for a favor, or conversely, to refer to a person as bad.

Finally, we also see cultural divergence in the verb–body-part associations involving more internal, less observable states and activities, including cognition, emotion and spirituality, which may reflect differences in how these two cultures conceptualize and talk about these. It is important to note that verbs of cognition and emotion in both languages tend to be learned later as compared with other verbs (English: Fenson et al. 1994; Hebrew: Maital and Dromi 1997; Maital et al. 2000). Three body parts associated with verbs of cognition in the two languages occur with different frequency rankings: moax ‘brain’/brain and rosh ‘head’/head are among the most-frequently mentioned terms in both languages, but with a higher ranking in Hebrew; and mind is in the most-frequent list for English only (with sexel ‘mind’ occurring in the least-frequent list). Within the set of verbs of cognition, only limco ‘find’/find and ligmor ‘finish’/finish are included on both early-learned verb lists. In Hebrew, these verbs are more strongly associated with the ROSH ‘HEAD’ region, particularly moax ‘brain’, sexel ‘mind’ and rosh ‘head’, whereas in English, these cognitive verbs are also associated with eye, hand and other parts. The other verbs of cognition differ between the two languages and also differ in the associated parts, with livxor ‘choose’ and lexpes ‘search, look for’ associated primarily with rosh ‘head’ in Hebrew and pretend, think and wish associated predominantly with mind in English, suggesting a more perceptual-emotional dimension of cognition in English. The two cultures also show divergence in some emotional appraisal verbs. Lisno ‘hate’/hate and lexabev ‘like’/like are associated with rosh ‘head’ in Hebrew, suggesting a cognitive component to the emotion, whereas they are associated with mind and heart in English, suggesting more emotion and feeling. However, note that le’ehov ‘love’/love is associated with lev ‘heart’/heart in both languages (plus mind in English only), suggesting both Semitic and Indo-European cultures have somewhat similar conventions regarding “love” (for a review, see Vanhove 2008), though “hate” and “like” may be understood differently. These patterns of associations suggest that the concepts of sexel ‘mind’/mind and rosh ‘head’/head are quite different in each language. Other studies have also suggested more marked cross-linguistic differences for abstract terms (e.g., Goddard 2010).

4.3 Limitations

One of the well-known limits of the associative method is that it cannot by itself disambiguate between overlapping meanings that have different sources (literal, vicarious, metaphoric, etc.). Future work will need to address the complexity of associative interpretations experimentally and directly compare the proportion of concrete uses of body parts as compared with symbolic, figurative uses. Additionally, the associative method used here might not provide the full range of possibilities found in all languages and may need to be modified to incorporate culture-specific notions and terms regarding the body, for instance using ideas of personhood rather than body, and thing rather than part (Wierzbicka 2007).

It could also be that, in some cultures, the term “body part” is more or less restricted in terms of what it encompasses (e.g., excludes internal organs, bones, or very small parts; see Enfield et al. 2006). The divergences we found in our data may be due to the Hebrew speakers providing more body parts, particularly internal organs and detailed parts, than the English speakers. Comparison between languages that provide similar numbers of parts may result in less divergence. To explore cultural divergence more fully, it is important in future work to ask for more than just one body part, to add organs or bones in the definition of what a body part can refer to, and to use other detailed measures of the real world, such as pictures and questions about function, localization and categorization. Future work should also examine languages from cultures that are significantly more different, as the overall patterns we found here may not always hold.

5 Conclusion

The significant convergence and small divergence we found between Hebrew and English verb–body-part associations require that we consider development as a main factor in our analysis. Interacting developing systems such as sensory, motor, neural, emotional, categorical perceptual, categorical linguistic, metaphoric, historic, factors from the task itself and others discussed in the paper act as a dynamic system of constraints. These dynamic systems push towards convergence and continuity even in adult knowledge, with the modulation of cultural and individual differences.

Data availability statement

Our tabulated data are available in Supplementary Materials I–IV. Our raw data Excel sheets are available via the TROLLing repository at

Corresponding author: Nitya Sethuraman, Department of Behavioral Sciences, University of Michigan-Dearborn, 4901 Evergreen Rd, 4012 CB, Dearborn, MI 48128, USA, E-mail:
Josita Maouene and Nitya Sethuraman contributed equally to this work.


We thank Dr. Sango Otieno and his team () for their contribution to the analyses and we thank Christian Casper for his editorial work on the paper. The body maps were created by Yanfei Song and supported by internal UM Dearborn professional development funds to Nitya Sethuraman.


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Supplementary Material

This article contains supplementary material (

Received: 2022-05-02
Accepted: 2023-03-10
Published Online: 2023-04-03
Published in Print: 2023-02-23

© 2023 the author(s), published by De Gruyter, Berlin/Boston

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