An Embodied Account of Argument Structure Development

In lieu of an abstract, here is a brief excerpt:All information enters the cognitive system through the body. Thus, it is possible that the body, and its morphology, may play a role in structurng knowledge and acquisition. This idea is particularly cogent in the case of verbs, since early learned verbs are about bodily actions and since recent advanc-es in cognitive neuroscience (Pulvermueller, 2005; James and Maouene, 2009) indicate that the neural processing of common verbs activates the brain regions responsible for the specific body parts that perform those actions. Here we provide initial evidence these body-part verb relations may also be related to the argument structures associated with specific verbs. We will conclude that in the same way that verb meaning and argument structure develop out of correlations in linguistic experiences, they may also develop out of correlations in body experiences

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Letting clusters and paths emerge from early semantic hypernetwork structure of features and their nouns

Recently, Hills and colleagues (2009 a,b) have described the potential categorical information contained in the features of early-learned basic level nouns by examining the binary graph-theoretic properties of developing noun-feature networks with a deterministic method: the clique percolation. The networks were built from the overlap of words normatively acquired by children at three different ages: 20 months --21 nouns--, 25 months --56 nouns-- and 30 months -- 130 nouns-- and their perceptual and functional features from adult feature generation norms (1394 token features). The resulting networks had small-world structure, indicative of a high degree of feature overlap in local clusters. The results also suggested that overlapping features among these early-learned nouns created higher-order groupings common to adult taxonomic designations as well as ad hoc categories. However, these methods are limited as they are only descriptive and yield minimal semantic information such as the degree of connectivity of local structures, whether a link of similarity (unspecified) exist or not between two neighbors or identify cliques of connectivity with unspecified semantics. To account for these limitations, we present a different type of representation, the hypernetwork (Berge, 1956) that keeps the semantic in the system, and a different formalism the Formal concept analysis (FCA), a non deterministic method, that builds relationships of containment (Wille, 1984). Furhte, machine-learning algoritms automatically cluster and build inclusions for the features and their nouns ( 21, 56 and 130 object names) at the 3 different ages mentionned above. We compare our results to the percolation method results obtained by Hills and colleagues. The power of the system lies in its automaticity and its ability to form many intermediate clusters at all stages of the growth of the network as well as showing the emerging paths from that structure

Body Parts Correlates of Early-Learned Verbs in Children

Literature on verb acquisition has mainly focused on abstraction and concreteness for verb acquisition whereas the analysis presented here points to an embodied perspective on word learning, examining whether early-learned verbs are associated with distinct parts of the body by children. Former results described how adults systematically and coherently correlate early learned verbs with five main body regions. However, the question remains whether 3-to 5- year-olds have the same systematic and coherent associations for the same verbs. We examined 102 verbs that are typically used by 3-to 5-years-old speakers of English. We asked an average of 15 children to give us which body part they use when performing an action for 21 verbs at a time. From these judgments, we created a body part vector for each verb. Nested body parts (e.g., lip, mouth, teeth, head) were treated separately. For example, the meaning vector for bite is: 13 mouth, 7 teeth, as these are the answers of the 20 children who each gave one body part when asked about bite. A correspondence analysis indicated that four dimensions of the correlations children made, as these correspond to ear-verbs=dimension 1, mouth-verbs=dimension 2, eye-and-brain-verbs=dimension 3, arm-and-leg-verbs=dimension 4. The adult data and the children data correlate. Developmentally, the earliest verbs acquire are mainly correlated with mouth- and leg- verbs, then, hand-verbs dominate, and finally, the latest verbs acquired have multiple body part correlations. Further, verbs that are learned the earliest have the strongest body part associations in children, a result also found in adults

Correlating Body Experiences in Argument Structure Development

In complement to the growing literature suggesting that verb meaning and argument structure develops out of correlations in linguistic experience, we suggest that correlations with body experiences may also matter. Study 1 examines what body parts are most commonly associated with early learned verbs. 50 adults and 60 children (36-60 months) were asked to name the main body part suggested by each of 101 early-learned verbs (MCDI). Results indicate that adults and children form similar patterns of associations, wherein three major regions body regions organize verbs: HEAD, ARMS, and LEGS. Study 2 is an fMRI study presenting preschoolers with an oral list of verbs associated with LEGS, verbs associated with HANDS, and neutral adjectives. Results indicate that only verbs recruit motor regions in the developing brain and that LEG and HAND verbs activate different regions in the motor cortex. Study 3 examines transcripts of speech from 28 20-month-olds and their mothers and 28 28-month-olds and their mothers in the Bates Corpus on CHILDES (MacWhinney, 2000). The speech was examined for all uses of the 101 MCDI verbs in five sentential frame categories: [V], [V loc], [V NP], [V NP NP], [V NP loc], [V-S]. Verbs were coded for body part associations data according to results from Study 1. The results indicate strong correlations between particular body parts and specific syntactic frames. Taken together these studies suggest that it might be worth looking at the realtionship between body experiences and syntactic frames

Longitudinal analysis of early semantic networks : preferential attachment or preferential acquisition?

Analyses of adult semantic networks suggest a learning mechanism involving preferential attachment: A word is more likely to enter the lexicon the more connected the known words to which it is related. We introduce and test two alternative growth principles: preferential acquisition—words enter the lexicon not because they are related to well-connected words, but because they connect well to other words in the learning environment—and the lure of the associates—new words are favored in proportion to their connections with known words. We tested these alternative principles using longitudinal analyses of developing networks of 130 nouns children learn prior to the age of 30 months. We tested both networks with links between words represented by features and networks with links represented by associations. The feature networks did not predict age of acquisition using any growth model. The associative networks grew by preferential acquisition, with the best model incorporating word frequency, number of phonological neighbors, and connectedness of the new word to words in the learning environment, as operationalized by connectedness to words typically acquired by the age of 30 months