Netherlands

Pronoun interpretation seems to be driven by structural factors, but also by factors related to meaning. In a forced-choice pronoun interpretation experiment, we compare the impact of the next-mention bias associated with transfer-of-possession-verbs on the interpretation of three Dutch pronominal forms that differ in the strength of their structural biases: reduced personal pronoun ze ‘she_reduced ’, full personal pronoun zij ‘she_full’, and demonstrative pronoun die ‘that’. In addition to replicating the common Goal-bias associated with transfer-of-possession verbs, results show significant differences in the proportion of pronoun resolved to the preceding subject between all three pronominal forms. However, the effect of the next-mention manipulation did not differ between pronominal forms. These findings are in line with a model of pronoun interpretation that combines structural and meaning-related factors, and present particularly strong evidence against models that posit that pronoun interpretation is the mirror image of pronoun production.

**Authors:**

Jet Hoek: Radboud University; Hans Wilke: University of Groningen

CogSci 2024

The effect of meaning-related cues on pronoun resolution in Dutch

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The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Recent work shows that participants make asymmetric causal inferences from apparently symmetric correlational statements (e.g., “A is associated with B”). Can we make sense of this behavior in terms of rational language use? Experiment 1 investigates these interpretive preferences—what we call “PACE effects”—in light of theoretical and experimental pragmatics and psycholinguistics. We uncover several linguistic factors that influence them, suggesting that a pragmatic explanation is possible. Yet, since PACE effects do not show that correlational language leads to causal implicatures strong enough to influence action choice in practical decision contexts, Experiment 2 offers new evidence from an experiment that explicitly compares the effects of causal vs. correlational claims on decision-making. Our results suggest that causal inferences from correlation language are an intricate, but possibly

**Authors:**

Daniel Lassiter: University of Edinburgh; Michael Franke: University of Tübingen

The rationality of inferring causation from correlational language

Are abstract sentences associated with specific constructs in dialogue, i.e., higher uncertainty, more curiosity and willingness to continue a conversation, and more causal questions? In three preregistered experiments we address these questions asking participants to evaluate the plausibility of linguistic exchanges referred to concrete and abstract concepts. Results support theories proposing that abstract concepts involve more inner monitoring and social dynamics compared to concrete concepts, and suggest that reaching alignment in dialogue is more effortful with abstract than with concrete concepts.

**Authors:**

Claudia Mazzuca: Sapienza University of Rome; Caterina Villani: University of Bologna; Tommaso Lamarra: University of Bologna; Marianna M Bolognesi: Modern Languages Dep; Anna Borghi: Sapienza University of Rome

Abstract Sentences elicit more Uncertainty and Curiosity than Concrete Sentences

This paper introduces a novel perspective to significantly mitigate catastrophic forgetting in continuous learning (CL), which emphasizes models' capacity to preserve existing knowledge and assimilate new information. Current replay-based methods treat every task and data sample equally and thus can not fully exploit the potential of the replay buffer.
In response, we propose COgnitive REplay (CORE), which draws inspiration from human cognitive review processes. CORE includes two key strategies: Adaptive Quantity Allocation and Quality-Focused Data Selection. The former adaptively modulates the replay buffer allocation for each task based on its forgetting rate, while the latter guarantees the inclusion of representative data that best encapsulates the characteristics of each task within the buffer. Our approach achieves an average accuracy of 37.95\% on split-CIFAR10, surpassing the best baseline method by 6.52\%. Additionally, it significantly enhances the accuracy of the poorest-performing task by 6.30\% compared to the top baseline. Code is available at https://github.com/sterzhang/CORE.

**Authors:**

Jianshu Zhang: School of Cyber Science and Engineering; Yankai Fu: School of Cyber Science and Engineering; Ziheng Peng: School of Cyber Science and Engineering; Dongyu Yao: Wuhan University; Kun He: Wuhan University

CORE: Mitigating Catastrophic Forgetting in Continual Learning through Cognitive Replay

Human learning is sensitive to rule-like structure and the curriculum of examples used for training. In tasks governed by succinct rules, learning is more robust when related examples are blocked across trials, but in the absence of such rules, interleaving is more effective. To date, no neural model has simultaneously captured these seemingly contradictory effects. Here we show that this same tradeoff spontaneously emerges with “in-context learning” (ICL) both in neural networks trained with metalearning and in large language models (LLMs). ICL is the ability to learn new tasks “in context” — without weight changes — via an inner-loop algorithm implemented in activation dynamics. Experiments with pretrained LLMs and metalearning transformers show that ICL exhibits the blocking advantage demonstrated in humans on a task involving rule-like structure, and conversely, that concurrent in-weight learning reproduces the interleaving advantage observed in humans on tasks lacking such structure.

**Authors:**

Jacob Russin: Brown; Ellie Pavlick: Brown University; Michael J. Frank: Brown University

Human Curriculum Effects Emerge with In-Context Learning in Neural Networks

This study investigates cue-based memory retrieval during sentence processing. Cue-based retrieval theories argue that the parser uses lexical and structural information as retrieval cues to retrieve items from memory. Evidence for cue-based memory retrieval comes from research showing that non-target representations matching retrieval cues interfere with target retrieval. However, the degree of susceptibility to this similarity-based interference has been debated, having led to the development of different computational models. This study focuses on two cue-based models and tests their predictions in two experiments. The results suggested similarity-based interference, but its patterns were not fully compatible with these models. To reconcile these findings within the framework of cue-based memory retrieval, this paper presents a model that assigns substantial weight to the structure-based cue and incorporates the notions of initial retrieval and revision. Results from simulations indicate that the model incorporating these assumptions provides a better fit to the observed data.

**Authors:**

Hiroki Fujita: University of Potsdam

Memory Retrieval Processes during Real-Time Language Comprehension: Empirical Evidence and Computational Modelling

One hallmark of human language is its combinatoriality---reusing a relatively small inventory of building blocks to create a far larger inventory of increasingly complex structures. 
In this paper, we explore the idea that combinatoriality in language reflects a human inductive bias toward representational efficiency in symbol systems. We develop a computational framework for discovering structure in a writing system. Built on top of state-of-the-art library learning and program synthesis techniques, our computational framework discovers known linguistic structures in the Chinese writing system and reveals how the system evolves towards simplification under pressures for representational efficiency. We demonstrate how a library learning approach, utilizing learned abstractions and compression, may help reveal the fundamental computational principles that underlie the creation of combinatorial structures in human cognition, and offer broader insights into the evolution of efficient communication systems.

**Authors:**

Guangyuan Jiang: Massachusetts Institute of Technology; Matthias Hofer: MIT; Jiayuan Mao: Massachusetts Institute of Technology; Lionel Wong: Massachusetts Institute of Technology; Josh Tenenbaum: MIT; Roger Levy: Massachusetts Institute of Technology

Finding structure in logographic writing with library learning

We examine the relation between event segmentation in language and cognition in the domain of motion events, focusing on Turkish, a verb-framed language that segments motion paths in separate linguistic units (verb clauses). We compare motion events that have a path change to those that did not have a path change. In the linguistic task, participants were more likely to use multiple verb phrases when describing events that had a path change compared to those that did not have a path change. In the non-linguistic Dwell Time task, participants viewed self-paced slideshows of still images sampled from the motion event videos in the linguistic task. Dwell times for slides corresponding to path changes were not significantly longer than those for temporally similar slides in the events without a path change. These findings suggest that event units in language may not have strong and stable influences on event segmentation in cognition.

**Authors:**

Bilge Tınaz: Özyeğin University; Ercenur Ünal: Özyeğin University

Event Segmentation in Language and Cognition

Eating onion ice cream is improbable, and levitating ice cream is impossible. But scooping ice cream using sadness is not just impossible: it is inconceivable. While prior work has examined the distinction between improbable and impossible events, there has been little empirical research on inconceivability. Here, we report a behavioral and computational study of inconceivability in three parts. First, we find that humans reliably categorize events as inconceivable, separate from probable, improbable, and impossible. Second, we find that we can decode the modal category of a sentence using language-model-derived estimates of subjective event probabilities. Third, we reproduce a recent finding that improbable events yield slowest response times in a possibility judgment task, and show that inconceivable events are faster to judge than impossible and improbable events. Overall, our results suggest that people distinguish the impossible from the inconceivable, and such distinctions may be based on graded rather than discrete judgments.

**Authors:**

Jennifer Hu: Harvard University; Felix Anthony Sosa: Harvard University; Tomer D. Ullman: Harvard University

Shades of Zero: Distinguishing impossibility from inconceivability

Telicity is an important semantic feature pointing to event construal: telic verb phrases denote bounded events with an inherent endpoint while atelic verb phrases denote unbounded events without such an endpoint. Languages encode telicity in different ways. Unlike English, Mandarin lacks an overt count-mass distinction and allows bare noun objects to form verb phrases. Would this cross-linguistic difference influence event perception? Experiment 1 elicited descriptions of bounded vs. unbounded events from English and Mandarin native speakers. A clear cross-linguistic difference was found: English speakers mostly used telic predicates for bounded events and atelic predicates for unbounded events while Mandarin speakers gave atelic predicates with bare noun objects for both event types. Experiment 2 explored how English and Mandarin speakers tracked the temporal structure of bounded vs. unbounded events. The two language groups performed similarly. The way people describe events may not affect the way they track event temporal profiles.

**Authors:**

Yue Ji: Beijing Institute of Technology; Anna Papafragou: Unversity of Pennsylvania

Do Cross-Linguistic Differences Influence Event Perception?

Daily experiences are conceptualized as events involving multiple participants and their relations (i.e., thematic roles). When describing events, speakers often do not include all event participants involved. Here, we explore how underlying conceptual requirements and language-specific encoding options influence the content of event descriptions in speech and gesture in two typologically different languages (English, Turkish). Focusing on conceptually peripheral instruments whose status is highly debated, we manipulated the conceptual status of event participants by including events that ‘require’ or ‘allow’ otherwise syntactically optional instruments. Results showed that the require-allow distinction did not manifest uniformly in Turkish and English in speech, gesture, or when both modalities were considered. However, mention of highly optional event participants (e.g., allowed instruments) was affected by language-specific syntactic encoding options. We conclude that, under more naturalistic elicitation conditions, planning descriptions of instrument events is more heavily affected by language-specific encoding than conceptual prominence of the roles.

**Authors:**

Christiana Moser: University of Toronto; Bahar Tarakçı: Özyeğin University; Ercenur Ünal: Özyeğin University; Myrto Grigoroglou: University of Toronto

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