An associative account of the development of word learning

Abstract

Word learning is a notoriously difficult induction problem because meaning is underdetermined by positive examples. How do children solve this problem? Some have argued that word learning is achieved by means of inference: young word learners rely on a number of assumptions that reduce the overall hypothesis space by favoring some meanings over others. However, these approaches have difficulty explaining how words are learned from conversations or text, without pointing or explicit instruction. In this research, we propose an associative mechanism that can account for such learning. In a series of experiments, 4-year-olds and adults were presented with sets of words that included a single nonsense word (e.g. dax). Some lists were taxonomic (i.,e., all items were members of a given category), some were associative (i.e., all items were associates of a given category, but not members), and some were mixed. Participants were asked to indicate whether the nonsense word was an animal or an artifact. Adults exhibited evidence of learning when lists consisted of either associatively or taxonomically related items. In contrast, children exhibited evidence of word learning only when lists consisted of associatively related items. These results present challenges to several extant models of word learning, and a new model based on the distinction between syntagmatic and paradigmatic associations is proposed.

Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions

“…These shortcomings of traditional DSMs have already been recognized and acknowledged (e.g. Sloutsky, Yim, Yao, & Dennis, 2017), also by proponents of DSMs (Lemaire & Denhière, 2004). Traditional DSMs are based on batchlearning algorithms, where a matrix storing all information is processed "at once", using computationally demanding techniques.…”

Statistical regularities shape semantic organization throughout development

“…Finally, the potential contributions of co-occurrence regularities are highlighted by a mechanistic account and corroborating behavioral evidence presented by Sloutsky et al (2017). This account was inspired by computational modeling evidence that everyday language input, including input to children (Asr, Willits, & Jones, 2016;Frermann & Lapata, 2015;Huebner & Willits, 2018), is rich in statistical co-occurrence regularities that capture links between concepts in semantic organization (see Jones et al, 2015 for a review).…”

“…In contrast, shared patterns of co-occurrence must be integrated across separate instances of co-occurrence. Therefore, learning taxonomic relations from shared patterns of co-occurrence may require more time, such that taxonomic relations may emerge more gradually in the course of development (Sloutsky, Yim, Yao, & Dennis, 2017). Importantly, according to this view, taxonomic relations supplement rather than replace co-occurrence relations.…”

“…Moreover, regularities with which words share each other's patterns of co-occurrence with other words, such as spaghetti and pie (both co-occur with fork), link members of the same taxonomic category. Therefore, according to Sloutsky et al's (2017) account, exposure to co-occurrence regularities in language fosters both the learning of associations between concepts whose labels directly co-occur, and between taxonomically related concepts whose labels share patterns of co-occurrence. However, whereas direct co-occurrence can be gleaned straight from the input and therefore rapidly foster links between concepts, shared patterns of co-occurrence should foster links between members of the same taxonomic category more slowly because they can only be derived by integrating across multiple instances of direct co-occurrence.…”

“…Initial support for this account comes from a series of word learning and lexical extension experiments (Sloutsky et al, 2017) in which both children and adults had to infer a meaning for a novel word embedded in a list of familiar words. When the novel word appeared in a list of words that are associated (and therefore likely to co-occur) with the word "animal", such as "furry" and "zoo", both children and adults inferred that the novel word meant "animal".…”