PEER2025 Program

In Russian, an interaction between aspect and negation is reported in the theoretical semantics and pragmatics literature (Zinova & Filip, 2014a,b). When an imperfective verb is negated, the entire event (both subparts) is negated; however, when the perfective aspect is under negation in Russian, only a subpart of the event is denied (the result) as a result of pragmatic implicature. This study aims to investigate this hypothesized interaction using the Visual World Paradigm (VWP). We examine aspect (perfective vs. imperfective) and negation (affirmative vs. negated sentences) in Russian and Polish. This study additionally introduces a novel psycholinguistic task: a visual variant of a Likert scale. The visual Likert scale will display images corresponding to Ramchand (2008)’s syntactic decomposition of an event. Using a visual depiction of the full event structure will allow us to collect more fine-grained information in real-time about the interpretations of grammatical aspect, as well as about the processing of events more generally.

One of the foundational theories of the syntax-semantics interface is that theta roles are projected from the verb (Jackendoff 1987). However the granularity of these roles (Dowty 1991) and the origin of these roles within the sentence (Husband 2023) are both questions that are still relevant today. We follow the likelihood rating paradigm for proto-roles of Kako (2006) and Reisinger et al. (2015) as well as using phonotactically realistic nonsense words (Berko 1958) in order to address aspects of these questions. Our results show that not only are proto-role properties influenced by elements beyond verbs, like nouns and their modifiers, but also that syntactic positions themselves modulate specific proto-role properties.

Humans judge creativity differently, but the structural and perceptual mechanisms remain unclear. By prioritizing high inter-rater reliability, many previous studies undervalued the importance of individual differences, including expertise. Furthermore, in the domain of storytelling, creativity is often studied separately from other narrative dimensions (e.g., cohesiveness). Moreover, while there are quantitative linguistic features that are predictive of creativity ratings of stories, it is unclear how those features predict distinct aspects of story quality. Here, we addressed these limitations by examining how expertise influences ratings of creativity and related dimensions of stories, as well as the link between diverse linguistic features and these ratings. Novices (N=309) and creative writing/storytelling experts (N=15) rated transcripts of improvised spoken stories (N=108) on five story quality dimensions. Both groups had similar average ratings and showed two distinct latent rating dimensions–innovativeness and cohesiveness–which were predicted by distinct sets of linguistic features. Notably, however, experts perceived the five dimensions as more differentiated, and their ratings were less driven by linguistic features than novices’. These findings indicate that experts’ creativity perceptions are more nuanced and less reliant on fine-grained narrative structures, highlighting the importance of capturing individual differences and multidimensionality in creativity research.

Telicity (whether a described event or state has an explicit endpoint) is a crucial part of sentence understanding and reasoning. To investigate whether transformer models can grasp the concept of telicity, we conducted a prompt-priming experiment on RoBERTa, GPT2, and Llama2. We also fine-tuned RoBERTa on a masked token prediction task for telicity. The data consists of minimal sentence pairs that differ in their telic or atelic readings by one token (e.g., I am going to run for/in 2 hours). Finetuning RoBERTa suggests its sensitivity to telicity. However, models' performances varied in the prompt-priming experiment, which reveals that telicity is difficult to stably prime and may not be actively used by incremental models as a feature.

In event semantics, there are conflicting theories of whether thematic roles are projected from predicates onto their arguments or arise from the arguments themselves (thematic separation). Psycholinguistic research has only found evidence for thematic separation in pre-verbal NPs, which can be explained by incremental processing. In this work, we probe the event representations of LLMs on a large dataset for Semantic Proto-Role Labeling augmented to contain diverse syntactic structures. To quantify the degree to which the models use arguments and predicates during thematic interpretation, we propose a novel Transformer interpretation method that decomposes classification logits into independent contributions from input tokens. Our results provide evidence for event separation in both incremental and bidirectional LLMs, with arguments contributing an equal or greater amount of thematic information as predicates, especially when identifying features of agentiveness.

Representations for extra-linguistic modalities are becoming more common in work on group dynamics. There is less attention on the impact of real-world events in dialogue interpretation. Events significantly contribute to the multimodal context for speech, and can aid in automatic understanding of dialogues, from the perspective of the Common Ground. Additionally, 'grunts', or non-lexical utterances, play a key role in conversations. An annotation scheme for the classification of conversational grunts is proposed, designed to classify pragmatic functions of grunts, considering phonetic and prosodic form. The approach is inspired by work on laughter, which is extended by identifying the gruntable as the event or utterance that proceeds a grunt. By representing both speech and events as communicative acts in the sequence of task-based interactions, the situational content resulting from each move can be tracked. These events can modify the Common Ground in ways that speech and gesture cannot, by adding information about objects in the environment. A shared task video dataset is annotated with the new schema. The characteristics of the conversational grunts can give insight into the state of the task sequence, and how participants process information gained from observed events.