An Ontogenesis Model of Word Learning in a Second Language

A recent paper caught my eye, Ontogenesis Model of the L2 Lexical Representation, and despite the immediate mind glazing effect of the word “ontogenesis,” I found the model well worth digging into and sharing here—and it may bear relevance to conversations on orthographic mapping.

How we learn words and all their phonological, morphological, orthographic, and semantic characteristics is a fascinating topic of research—most especially in the areas of written word recognition and in the learning of a new language.

This paper thus struck me as an especially insightful attempt to synthesize much of that research. To be clear: this is a model that has not been directly tested, but it seems well-aligned to other theories like orthographic mapping and the lexical quality hypothesis, as well as explain some of the tension between regularity and irregularity in word forms and frequency.

“In intentional word learning from definitions, L2 words with easily encoded orthographic form are better retained. In incidental word learning, words with unusual form are more salient and more easily detected.”

I enjoyed especially the visualizations of phonological, orthographic, and semantic mapping and how they can develop at different rates and trajectories but with interdependence.

Figure 1a depicts an example ontogenetic curve in one domain. Over time, the degree of acquisition increases while, simultaneously, the degree of fuzziness decreases till the optimum range (shaded green) is reached (asterisks, meeting the optimum’s lower bound).
Figure 1b shows the ontogenetic curves of all three domains in a three-dimensional graph (semantic in front, phonological and orthographic behind). Domains may have different onsets (here, the emergence of the phonological representation starts before the orthographic and semantic representations), different slopes (here the orthographic representation has a steeper slope) and that they may (here: phonological and orthographic) or may not (here: semantic) reach their optima.
Figure 3c shows an ontogenetic domain curve (e.g., semantic) with gradual network integration. Depicted over time, the circles representing network integration yield a cone-like structure around the curve in the three-dimensional space; its radius grows as the representation becomes better integrated in the corresponding network.

A couple of terms that are key to the ontogenesis model (the authors should perhaps come up with a catchier name):

  • Fuzziness: “inexact or ambiguous encoding of different components or dimensions of the lexical representation that can be caused by several linguistic, cognitive, and learning-induced factors. These factors include, among others, changes in neural plasticity, the complexity of mapping L2 semantic representations on the existing L1 semantic representations and of mapping L2 forms on the semantic representations, and problems with L2 phonological encoding”
  • Optimum: “the ultimate attainment of a representation (or its individual components), i.e., the highest level of its acquisition, when the representation is properly encoded and no longer fuzzy”

These concepts give us a way of visualizing, as per the graphs above, how different dimensions of a word may develop over time. Our goal, of course, is to reach optimum encoding across the sounds, spelling, and meaning so that it is anchored in our long-term memory (i.e. fluent, automatic access and retrieval).

“Each lexical entry can comprise representations from the three domains, and each representation is interconnected with other representations of the same type. Each domain representation can thus develop its own, idiosyncratic network of connections to other representations. Together they constitute the phonological, orthographic, and semantic networks in the mental lexicon.

“The model sees a word’s lexical integration as a gradual process, in which connections to other representations grow in number and strength until the optimum is potentially reached. The optimum in this dimension can be described as an adequately rich network of appropriate connections. Fuzziness in this dimension then refers primarily to an inadequate number of connections to other representations (typically too few) and/or to their inadequate strength
(typically too weak), as well as inappropriate connections (e.g., an erroneous connection between the phonological forms of through
and dough due to the influence of orthography).”

The added complexity of learning words in a new language is that there are variable interactions across phonological, orthographic, and semantic dimensions with our native language.

“Depending on the grapheme-phoneme relationship between the L1 and L2 and within L2, simultaneous acquisition of orthographic information may thus move the phonological representation closer to or further away from its optimum (and vice versa). Furthermore, the effect of L1 orthography on spoken word recognition in L2 is modulated by L2 proficiency and word familiarity

…a new L2 form representation is connected not only to other, previously established, L2 form representations, but also to L1 forms. The OM thus differentiates between two subnetworks within the
form network: an IntraNetwork and an InterNetwork. The IntraNetwork refers to the connections between a given L2 form and other L2 forms, as discussed above. The InterNetwork refers to cross-language connections, i.e., the connections between a given L2 form and L1 forms.”

An interesting and insightful model! I look forward to seeing further studies drawing upon it.


2 responses to “An Ontogenesis Model of Word Learning in a Second Language”

  1. This is fascinating–thank you!

    Have you considered sharing on SPELLTalk?


    On Sun, Mar 13, 2022 at 7:13 AM Language & Literacy wrote:

    > manderson posted: ” A recent paper caught my eye, Ontogenesis Model of the > L2 Lexical Representation, and despite the immediate mind glazing effect of > the word “ontogenesis,” I found the model well worth digging into and > sharing here—and it may bear relevance to conversatio” >


    • Thank you, Harriett! I’m a bit intimidated to share anything on SPELLTalk myself — but I definitely enjoy reading the threads and research posted on there.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: