United States

This study investigates how heritage Spanish-English bilinguals process sentences with canonical and non-canonical word orders, focusing on inanimate (IA) subjects and objects in subject-verb-object (SVO) and object-verb-subject (OVS) structures. By examining whether participants rely on sentential cues or semantic processing, we aim to test predictions from the Competition Model, which emphasizes cue reliability and validity, and the Good-Enough Processing Model, which suggests reliance on heuristics in challenging syntactic contexts.

Using the Tobii Pro Fusion eye tracker, we are collecting eye movement data from 50 bilingual participants (Spanish AoA: 0-3 years; English AoA: 0-8 years) as they read 80 sentences (40 per language), balanced for verb agreement and randomized to control for order effects. Participants will identify the subject after each sentence and complete tasks assessing language dominance (BLP), vocabulary (LexTALE, LexTALE-ESP), and literacy skills in English and Spanish.

Results will advance our understanding of current theories of sentence processing.

CogSci 2025

Sentence cues or semantics? Using eye tracking to study sentence processing in heritage Spanish-English bilinguals.

semantics of language

language comprehension

statistics

eye tracking

reading

linguistics

syntax

poster

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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#### Code of Conduct

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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

With the rise of Large Language Models (LLMs), interest in simulating interaction dynamics has grown, raising questions about their validity as cognitive models of human discourse. While extensive research focuses on their performance in various applications, we aim to quantify LLM conversational processes akin to traditional human studies. By analyzing how convergence entropy evolves across different conversational tasks, we propose a framework for quantitatively assessing LLMs’ ability to exhibit specific features. This approach offers a pathway to characterizing LLMs for agent-based modeling and broader discourse analysis.

Large Language Model Discourse Dynamics

Effective social navigation requires individuals to infer others' intentions and adjust their movements accordingly to avoid collisions. A key aspect of this process is recursive reasoning, where individuals anticipate that others are also inferring their intentions. In this study, we quantitatively measured whether humans exhibit spontaneous mentalizing during navigation and developed a computational model to demonstrate the basic principle. Then, we introduced a novel framework for quantifying the recursive depth of human mentalizing during social navigation. Using a Doors-choosing task within a VR environment, participants navigated between two doors while avoiding a virtual human. Analyzing choice patterns, confidence levels and walking trajectories, we found that participants engaged in one or two levels of recursion, with respective probabilities of 80% and 20%. This study provides a quantitative estimation of the recursive depth of mentalizing in navigation and establishes a foundation for integrating human recursive reasoning into socially intelligent agents.

Quantifying Recursive Mentalizing Depth for Social Navigation

Humans do not just follow rules and solve problems created by others: we modify those rules, set new goals, and create new problems—so can we be inventors and innovators. Creating a good rule or a good problem, however, depends not just on the ideas you come up with but on how you evaluate such proposals. Here, we study invention through the lens of game design. We focus particularly on the early stages of novice, “everyday” game creation, where the stakes are low. We draw on a dataset of over 450 human created games and conduct a model-based analysis of how people invented new games based on prior experience. We consider two different cognitive mechanisms that may be at work during the early processes of intuitive game invention: an associative proposal based on previous games one has seen, and evaluation based on simulations of play. In particular, we aim to understand two possible evaluation schemes (model-free and model-based) that a commonsense-based game creator may use to refine their initial draft proposals. We find that the generated games are best described by a model which incorporates both rapid model-free evaluations and slower, model-based estimates of game quality at a population level. Our work serves as a step forward towards the proposal and evaluation process in human invention. See https://sites.google.com/view/gen-eval-game-creation for additional details and preprint.

Generation and Evaluation in the Human Invention Process through the Lens of Game Design

How do people determine whether non-human entities have thoughts and feelings — an inner mental life? Prior work has proposed that people use compact sets of dimensions (e.g., body-heart-mind) to form beliefs about familiar kinds, but how do they generalize to novel entities? Here we investigate emerging beliefs about the mental capacities of large language models (LLMs) and how those beliefs are shaped by how LLMs are portrayed. Participants (N = 470) watched brief videos that encouraged them to view LLMs as either machines, tools, or companions then took a survey measuring mental capacity attributions. We found that the companion group more strongly endorsed statements regarding a broad array of mental capacities that LLMs might possess relative to the machine and tool groups, suggesting that people’s beliefs can be rapidly shaped by context. Our study highlights the need to explore the factors shaping people’s beliefs about emerging technologies to promote accurate public understanding.

Portraying Large Language Models as Machines, Tools, or Companions Affects What Mental Capacities People Attribute to Them

The cognitive processes underlying Go/No-Go performance may be explained by two plausible evidence accumulation models: Two-Boundary (2-B) and One-Boundary (1-B) decision drift models (DDMs). While both embed a Go decision, the 2-B DDM embeds a definitive No-Go decision, whereas the 1-B DDM embeds a response window for Go. Using simulations, we found that model comparison methods like leave-one-out cross-validation (LOO), coupled with Bayesian hierarchical modeling, can correctly identify the underlying model. Additionally, using the correct model reduces the risk of missing true effects or detecting spurious findings. Therefore, we recommend researchers implement and compare both models for Go/No-Go studies to reduce misleading results. Lastly, we implemented these models to investigate race effects in the decision to shoot during police training. We found that the accumulated evidence needed to reach the Shoot decision is lower for Black suspects, which explains the heightened error rates for shooting unarmed Black suspects in data.

Comparing One-Boundary and Two-Boundary Evidence Accumulation Models for Go/No-Go Processes: An Application to the Decision to Shoot

While Chinese null pronouns consistently demonstrate a subject preference, overt pronouns exhibit a subject preference in forward reference but not in backward reference. Previous studies have attributed this pattern to syntactically imposed constraints. However, Su (2020) proposed that this pattern arises from language-specific discourse constraints prohibiting subject referents for overt backward pronouns in temporal clauses but allowing them in non-temporal clauses. To examine Su’s account, we administered an offline sentence judgment experiment with 100 Chinese-speaking adults. The experiment employed contextually ambiguous sentences, involving manipulations of pronoun form (null vs. overt), anaphora type (forward vs. backward) and clause/connective (deshihou ‘when’, zhiqian/zhihou ‘before/after’ and ruguo ‘if’). Our results partly confirmed Su’s account: Chinese overt backward pronouns showed a subject preference in non-temporal ruguo ‘if’ clauses and temporal zhiqian/zhihou ‘before/after’ clauses, but not in temporal deshihou ‘when’ clauses. These findings suggest potential influences of event structure on Chinese pronoun resolution.

Connectives and clause order modulate Chinese pronoun resolution

Spatial abilities are relevant to scientific achievement, yet little is known regarding development of spatial abilities among adolescence. This study thus examines the development of spatial abilities during adolescence, and how different spatial abilities differentially predict science achievement.
Over 1,006 students from grades 4, 6, and 8 were assessed with four different spatial abilities tasks, a science achievement test (TIMSS), and assessment of control variables.
Significant grade differences in spatial abilities were observed. Spatial abilities account for about 14%, 13%, and 13% of variance in science achievement in Grades 4, 6, and 8. Extrinsic-dynamic abilities emerged as the strongest predictor of science achievement.
The current findings showcased the development of different spatial abilities across Grades 4 to 8 and confirmed the significance of spatial abilities, particularly extrinsic-dynamic spatial abilities, in science learning. Interventions that target spatial abilities may be a potential way to prepare students for the science curriculum.  
Keywords: Spatial Abilities, Science Achievement, Cognitive Development, Spatial Cognition, STEM

The Developmental Role of Spatial Abilities in Predicting Science Achievement in Elementary and Middle School: A Cross-Sectional Study

Understanding how adults and children remember dynamic events is important for cognition. In S1, adults(N= 177) heard no words, verbs, or nouns while Tobiix30 eyetracker recorded looking to 34 familiar events. Test included 40 events: Old, New Action(old agent,new action), New Agent(new agent,old action), and Conjunction(new combination). Tracking shows more attention to hands (50%) than head (25%); looking affected memory. Results show better memory for actions than agents (e.g.,Trial type:F(3,522)=660.28,p< .001). In an adapted procedure, 19 3-year-olds and 19 4-year-olds were shown 2 events while hearing verbs; tested on memory with the same types of trials.  Again, actions were remembered better than agents (e.g.,Trial type(F(4, 72)=25.66,p< 0.001). Conclusions can be drawn regarding the development of event memory with implications for multiple areas (eyewitness testimony,verb learning).

How Well Do Adults and Children Remember Agents and Actions in Dynamic Events?

Language co-activation can strengthen associations between unrelated concepts. We tested whether cross-linguistic phonological overlap impacts semantic processing of non-overlapping written inputs. English monolinguals and Korean-English bilinguals were presented with an interlingual homophone (e.g., “moon”) and a word that is either semantically related (e.g., “lock” – “moon,” the sound /mu:n/ means "door" in Korean, which is semantically related with “lock”) or unrelated across languages (e.g., “fork” – “moon”). While their EEG was recorded, participants had to judge whether the word pairs were semantically related. A smaller N400 effect (difference in ERP amplitude between related and unrelated word pairs) was found in bilinguals than monolinguals, especially for word pairs related in meaning across languages. We conclude that phonological links across languages can connect unrelated concepts, reshaping the lexico-semantic network.

Phonological Overlap Connects Semantically-Unrelated Concepts: Evidence from Neural Correlates of Language Co-activation

Graphical representations of data are pervasive in modern communication and are often used to convey socio-economic, scientific and medical information. Despite their popularity, it is still unknown whether they can enhance the long-term retention of their content. We conducted an incidental delayed recall task with psychology undergraduates (N=92), in which participants read about the evolution of a socio-economic phenomenon, with few datapoints presented either as a graphics, a text, or a table. We found that graphics facilitated the remembering of the general trends of the data after a two-hour interval. No advantage was found on immediate recall of numerical values in another sample of participants (N=80). Thus, even for equal initial encoding of numerical information, and even for very concise materials, graphics facilitate long-term retention. Overall, the study reveals the potential of graphics as effective tools for enhancing memory retention and therefore highlights their valuable role in educational settings.

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