Regular polysemes are sets of ambiguous words that all share the same relationship between their meanings, such as CHICKEN and LOBSTER both referring to an animal or its meat. To probe how a context embedding model, here exemplified by BERT, represents regular polysemy, we analyzed whether its embeddings support answering sense analogy questions similar to “is the mapping be- tween CHICKEN (as an animal) and CHICKEN (as a meat) the same as that which maps between LOBSTER (as an animal) to LOBSTER (as a meat)?” We found that (1) the model was sensitive to the shared structure within a regularity type; (2) the shared structure varies across regularity types, potentially reflective of a “regularity continuum;” (3) some high-order latent structure may be shared across regularity types, suggestive of a similar la- tent structure across types; and (4) there is equivocal ev- idence that the aforementioned effects are explained by meaning overlap.

We conducted a case study on how unreliable and/or unrepresentative stimuli in psycholinguistics research may impact the generalizability of experimental findings. Using the domain of lexical ambiguity as a foil, we analyzed 2033 unique words (6481 tokens) from 214 studies. Specifically, we examined how often studies agreed on the ambiguity types assigned to a word (i.e., homonymy, polysemy, and monosemy), and how well the words represented the populations underlying each ambiguity type. We observed far from perfect agreement in terms of how words are assigned to ambiguity types. We also observed that coverage of the populations is relatively poor and biased, leading to the use of a narrower set of words and associated properties. This raises concerns about the degree to which prior theoretical claims have strong empirical support, and offers targeted directions to improve research practices that are relevant to a broad set of domains.

Are different amounts of semantic processing associated with different semantic ambiguity effects? Could this explain some discrepant ambiguity effects observed between and across tasks? Armstrong and Plaut (2016) provided an initial set of neural network simulations indicating this is indeed the case. However, their empirical findings using a lexical decision task were not clear-cut. Here, we use improved methods and five different experimental manipulations to slow responding- --and the presumed amount of semantic processing---to evaluate their account more rigorously. We also expanded the empirical horizon to another language: Spanish. The results are partially consistent with the predictions of the neural network and differ in several important ways from English data. Potential causes of these discrepancies are discussed in relation to theories of ambiguity resolution and cross-linguistic differences.

Most statistical learning studies focus on the learning oftransitional probabilities between adjacent elements in asequence, however, other statistical regularities may un-derpin different aspects of processing language and regu-larities in other domains. Here, we investigate how con-junctive statistical regularities (of the form A and B to-gether predict C) can be learned, and how this learningis impacted by similarity in representations analogousto that in unambiguous words, homonyms with mul-tiple unrelated meanings, and polysemes with multiplerelated meanings. We observed that provided the stimu-lus structure is relatively simple, participants are readilyable to learn conjunctive probabilities and display sen-sitivity to relatedness among representations. These re-sults open new theoretical possibilities for exploring thedomain-generality of how the learning and processingsystems merge conjunctive information in simple labo-ratory tasks and in natural language.