The Ontogenesis Model of the L2 lexical representation by Bordag, Gor and Opitz (2021) is a rich conceptual framework of the way words are stored (i.e., what a lexical representation consists of) and learned (i.e., how the representation changes over time) in the L2 mental lexicon. It models the development of three dimensions of a lexical representation: a) three major linguistic domains of knowledge about the word, b) the mappings between these domains, and c) the network in which this multidimensional representation is embedded. The model's holistic focus on the word – that is, on the multidimensionality of the lexical representation itself – connects several approaches that are often limited to one modality (e.g., reading), or one domain of knowledge (e.g., meaning), underscoring the confluence of factors that impact word learning.

Understanding the internal structure of the L2 lexical representation is an essential component of advancing knowledge about how L2 learners succeed in creating accurate and elaborate lexical entries over time. The exact mechanisms by which this happens are still open – and could involve updating initial representations or removing inaccurate ones. In turn, understanding what L2 representations are like, and how they change, matters because of the key role they play in recognizing spoken words in a conversation. In L1, lexical representations for well-known words contain various types of information – they are elaborate (e.g., Hulstijn, Reference Hulstijn and Robinson2001, p. 259); they are specific and stable; and they are deeply interconnected into larger networks. Their phonological form is precise, or fine-grained, allowing for efficient activation and recognition. Words are accessed quickly and accurately, even with incomplete acoustic information (e.g., Otake & Cutler, Reference Otake and Cutler1999), and listeners are exquisitely sensitive to phonetic mismatches (e.g., Dupoux Sebastián-Gallés, Navarrete & Peperkamp, 2008). In L2, many lexical representations are initially fragmentary, ambiguous, and their phonological form shaped by L1 phonology. As a result, they are more difficult to access with precision and ease (Cook & Gor, Reference Cook and Gor2015).

Herein lies also the import of Bordag and colleagues’ framework (and of the research it will generate) for language instruction, faced with the question of how to help learners establish fine-grained lexical representations. Ultimately, understanding which factors facilitate this development will inform instruction – for instance, by outlining when to provide explicit pronunciation support; or which types of words to learn together or separately (see Dobel, Lagemann & Zwitserlood, Reference Dobel, Lagemann and Zwitserlood2009; Pajak, Creel & Levy, Reference Pajak, Creel and Levy2016) – in order to effectively refine these words’ representations. The unifying force of the model highlights the importance of integrating the various domains of knowledge into vocabulary teaching.

Going forward, one of the challenges for research will be to further define ‘fuzziness’. Bordag and colleagues view it as a central property of the L2 lexicon and define it as inexact or ambiguous encoding of some components or dimensions of the lexical representation. Of note, fuzziness could be viewed as a property of developing lexicons rather than of L2 lexicons only, in continuity with L1 (Gierut, Reference Gierut2016; Lahiri & Reetz, Reference Lahiri, Reetz, Gussenhoven and Warner2002; Pajak et al., Reference Pajak, Creel and Levy2016; Stoel-Gammon, Reference Stoel-Gammon2011; White, Yee, Blumstein & Morgan, Reference White, Yee, Blumstein and Morgan2013). However, it is possible that the nature of this fuzziness differs in L1 and L2. Research is just beginning to uncover the various ways by which L2 representations can be fuzzy, ranging from uncertainty about one feature, an entire syllable (Darcy & Thomas, Reference Darcy and Thomas2019), to a more global form fuzziness (Cook & Gor, Reference Cook and Gor2015). The nature of the fuzziness could also evolve over time for the same word: a word may first be encoded with inexact specifications (not target-like, e.g., the wrong vowel is stored), then be ambiguous (one or more features of the vowel are left underspecified), and finally become accurate. This may lead to varying slopes towards fine-grained optima, within each domain.

The model is certain to also stimulate exciting research on the source of imprecise representations: for instance, the way the word was learned (demanding vs. easy, similar vs. different words, etc., Dobel et al., Reference Dobel, Lagemann and Zwitserlood2009) vs. a processing limitation due to L1 influence resulting in a particular phonological dimension not being encoded in words (Dupoux et al., Reference Dupoux, Sebastián-Gallés, Navarrete and Peperkamp2008). Eventually it will also become possible to characterize the consequences of fuzziness, i.e., patterns of mutual influence among lexical representations, how neighboring as well as more distant words influence the content and the development of a given entry and how representations shift from fuzzy to fine-grained throughout the whole mental lexicon.