Recent work has raised a question about whether adult language learners take advantage of indirect negative evidence (here, statistical preemption) while learning a new language. Statistical preemption predicts that exposure to conventional formulations results in better recognition that unconventional formulations are unacceptable. In a preregistered study, 61 undergraduates enrolled in Spanish classes were exposed to instances of conventional constructions in Spanish for 3 days to determine whether the exposure would bring their responses to unconventional formulations into closer alignment with those of native Spanish speakers. Judgment data confirms an effect of statistical preemption: students showed an increased recognition of the fact that unconventional (unwitnessed) formulations were unacceptable. These results are consistent with the idea that learning a new language is, in large part, learning which formulations to avoid: learning what not to say.

Notably, while English tends to prefer shorter before longercomplements (explained to us a very clear effect), Japanesedisplays the opposite tendency. Far less cross-linguistic workhas investigated possible differences in the ordering of nounswithin conjunctions (“binomials’), although a corpus studysuggests that the same factors predict binomial ordering inJapanese and English. To investigate the issue experimentally,we report Japanese and English speakers’ productions of namesof the members of couples that they knew personally. Resultsconfirm that conceptual accessibility is the most importantfactor in the ordering of familiar name binomials in bothlanguages. That is, both groups tended to name the memberthey felt closer to first. Length (syllables/mora) was not asignificant predictor in either language. Differences in thepreferred order of verbs’ complements are then attributable toother factors, possibly a very general preference to minimizethe average distance between semantically related elements.

How are exceptions to a generalization learned? 20 participants were exposed to a mini-artificial language in whicheach word (prefix + wordstem) was associated with a unique image. One of two prefixes generalized probabilistically:it appeared with 40 stems associated with faces and 8 exceptions, which were associated with scenes. The other prefixoccurred with 8 faces and 8 scenes. The prefixes and the image categories (faces vs. scenes) were counterbalanced acrossparticipants. Participants performed a 2 alternative-forced choice task on all items, with feedback, over 6 repeating blocks.Results show that image-word pairs that included the generalizable prefix were learned better than those which appearedwith the other prefix, despite having 48 items in the first class and 16 in the other (d = 1.28, d = 0.80, p = 0.023). Weinvestigate the neural representation of these words and how they change over the course of learning.

How are exceptions to a probabilistic generalization learned? The present results suggest exceptions are learned in partby selectively suppressing the competing category, as opposed to only increasing knowledge of exceptions. Participantswere exposed to a mini-artificial language with a probabilistic generalization (80-20%) that mapped labels to categories ofimages (faces and scenes). Mouse-tracking trajectories determined the degree to which the generalization served as a lureto exceptions, compared to a separate baseline condition. Over time, the generalization became suppressed in a context-sensitive way: for exception items only. This extends retrieval induced forgetting, in which a particular item is suppresseddue to competition from partial retrieval, to include the entire conceptual category. Post-test revealed high item-specificaccuracy, even though category recognition was sufficient for the task.

There has been a great deal of interest in howgeneralizations and exceptions are learned, withparticular interest in how speakers learn to avoidovergeneralizations. Do overgeneralizations disappearonly because exceptions become more stronglyrepresented or does the generalization itself becomesuppressed? Novel labels were constructed bycombining 56 syllables with one of two prefixes, andeach label was assigned a unique image. Most labels withthe first prefix were paired with images from ageneralization category, whereas exceptional labels werepaired with images from a different semantic category.All labels with the second prefix appeared with a thirdcategory (“baseline”). Participants used a computermouse to choose one of two images for each label.Mouse-tracking results show that the generalizationitself became suppressed over time in the context ofexceptional labels. A post-test demonstrated thatexceptions were learned with item-specific precision.

Recent work suggests that speakers’ lexical networks in theirnative and secondary languages are organized somewhatdifferently, with native languages showing greatersystematicity. We here test this claim in a new way, bymaking use of the “Retrieval-induced forgetting” effect(RIF). Specifically, practicing previously encodedinformation through rehearsal is expected to result in bettermemory for that information, regardless of which languagethe information is encoded. The RIF effect involves thesuppression of information that is associated with thepracticed information but is itself unpracticed. Since RIF isunderstood to rely on the association between the practicedand unpracticed memories, we predict it will be weaker whenapplied in a language with weaker or less systematicallyorganized lexical associations. Results confirm that while theexpected practice effect was evident in participants’ nativeand second languages, the RIF effect was only significant inparticipants’ native language. We discuss the relevance andimplications of this finding for second language speakers.