Presentations

Abstract

Nearly all human languages have grammars with complex recursive structures. These structures pose notable learning challenges. Two distributional properties of the input may facilitate learning: the presence of semantic biases (e.g. p(barks|dog) > p(talks|dog)) and the Zipf-distribution, with short sentences being extremely more frequent than longer ones. This project tested the effect of these sources of information on statistical learning of a hierarchical center-embedding grammar, using an artificial grammar learning paradigm. Semantic biases were represented by variations in transitional probabilities between words, with a biased input (p(barks|dog) > p(talks|dog)) compared to a non-biased input (p(barks|dog) = p(talks|dog)). The Zipf distribution was compared to a flat distribution, with sentences of different lengths occurring equally often. In a 2×2 factorial design, we tested for effects of biased transitional probabilities (biased/non-biased) and the distribution of sequences with varying length (Zipf distribution/flat distribution) on implicit learning and explicit ratings of grammaticality. Preliminary results show that a Zipf-shaped and semantically biased input facilitates grammar learnability. Thus, this project contributes to understanding how we learn complex structures with long-distance dependencies: learning may be sensitive to the specific distributional properties of the linguistic input, mirroring meaningful aspects of the world and favoring short utterances.

Abstract

Effective communication involves recognizing the disparity between our own perspective and that of the recipient, influenced by factors such as stereotypes and accents. The extent to which Theory of Mind (ToM), the ability to ascribe beliefs to others, plays a role in this process is uncertain. We anticipate individuals to speak in line with their beliefs, but what if their words conflict with our expectations? To investigate, we devised a virtual perspective-taking experiment where we manipulated a virtual agent's beliefs. Electroencephalography data were collected as participants listened to statements from the agent that either aligned or clashed with their true or false beliefs. We focused on the N400, an event-related brain component linked to word unexpectedness. As hypothesized, statements inconsistent with the agent's true beliefs triggered more pronounced N400 responses compared to matching statements. Furthermore, we anticipated that when the agent held a false belief, this knowledge would factor into interpreting their statements. Neither statements aligned with nor those diverging from the agent's false beliefs evoked N400 responses. This can be taken as evidence that participants did take the agents perspective into account. These results strongly support the role of Theory of Mind in language comprehension.