Search

Pronouns serve a critical referential function, yet the cognitive processes engaged during pronoun comprehension remain incompletely understood. One view is that encountering a pronoun leads the comprehender to reactivate the semantic features of its antecedent. We examined this by manipulating the concreteness of a noun antecedent and assessing whether an Event Related Potential (ERP) concreteness effect was elicited at a downstream pronoun. We observed a robust concreteness effect at the noun, but no similar effect at the pronoun. We also examined whether N400 semantic priming from the antecedent would increase on content words shortly following the pronoun, relative to those preceding it. Again, although we observed semantic priming following the noun, it did not increase following the pronoun. Our data suggest that pronouns do not induce the activation of (much) new semantic information in long-term memory, perhaps instead triggering an attentional shift towards their antecedents’ extant representations within working memory. [ABSTRACT FROM AUTHOR]

Our environment contains temporal information unfolding simultaneously at multiple timescales. How do we learn and represent these dynamic and overlapping information streams? We investigated these processes in a statistical learning paradigm with simultaneous short and long timescale contingencies. Human participants (n = 96) played a game where they learned to quickly click on a target image when it appeared in one of nine locations, in eight different contexts. Across contexts, we manipulated the order of target locations: at a short timescale, the order of pairs of sequential locations in which the target appeared; at a longer timescale, the set of locations that appeared in the first versus the second half of the game. Participants periodically predicted the upcoming target location, and later performed similarity judgments comparing the games based on their order properties. Participants showed context-dependent sensitivity to order information at both short and long timescales, with evidence of stronger learning for short timescales. We modeled the learning paradigm using a gated recurrent network trained to make immediate predictions, which demonstrated multilevel learning timecourses and patterns of sensitivity to the similarity structure of the games that mirrored human participants. The model grouped games with matching rule structure and dissociated games based on low-level order information more so than high-level order information. The work shows how humans and models can rapidly and concurrently acquire order information at different timescales., (© 2024 Massachusetts Institute of Technology.)