United States

Abstract
Previous research has been inconsistent in approaching the exclusion criterion of nonverbal IQ when investigating developmental language disorder (DLD) in monolingual and multilingual children. The present study investigates the influence of the controversial low nonverbal IQ range (between one and two standard deviations (SD) below the mean) on lexical and morpho-syntactical abilities. 91 multilingual children, aged 4-8;11, were tested on Crosslinguistic Lexical Task and Sentence Repetition Task in Germany. Data were analyzed using generalized linear mixed models, considering the factors nonverbal IQ, DLD status, age, gender, and length of exposure (LoE) to German. Results show that children with typical language development (TLD) outperformed those with DLD on the LITMUS tests, independent of their nonverbal IQ, supporting the validity of these tools. Language status (TLD/DLD) and LoE had the strongest impact on test performance, exceeding the effect of nonverbal IQ. Regardless of language status, nonverbal IQ affected only receptive vocabulary but not productive vocabulary or morpho-syntax. However, when applying the one SD threshold, its influence shifted from receptive vocabulary to morpho-syntactic abilities. No significant differences were found between average and low nonverbal IQ groups across most tests within the TLD and DLD groups.

CogSci 2025

The Role of Nonverbal IQ in Diagnosing Developmental Language Disorder in Multilingual Children

cognitive development

language comprehension

language production

statistics

linguistics

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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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.

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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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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Emotion categories are complex and fuzzy concepts that children must learn to identify and differentiate in themselves and others. While prior research has shown that children’s emotion-related vocabulary evolves from broad to narrow as they age, the role of metrics such as word specificity within the development of emotion vocabulary remains under-explored. We use WordNet, a hierarchically-organized lexical database, to study word specificity in interview data collected from children on emotion labeling. We show that as children's age increases, they tend to use increasingly specific emotion words and we also analyze this in the context of concept learning. Further, we show that young children sometimes use words that are typically thought of as not being acquired until an older age, which are selected strategically for the given context. These findings provide new insights into understanding vocabulary and concept learning changes over age that contribute to the learning of fine-grained emotion category labels.

Children's emotion vocabulary learning discloses a growing understanding of specific concepts

Human sensory development unfolds in a consistent temporal sequence, with early visual inputs initially degraded. Rather than mere biological constraints, we propose these developmental “limitations” may act as inductive biases that foster more global and robust sensory cognition. Evidence derives from children born blind who later gained sight, effectively bypassing this early degraded period. Despite many otherwise intact visual abilities, they exhibit specific deficits in generalization and extended spatial integration. Simulations with deep neural networks confirm that these deficits can arise from a lack of early degraded inputs. Conversely, training with developmentally-inspired input trajectories yields more robust representations and superior generalization. These findings help illuminate the development of typical and atypical sensory cognition, inform clinical interventions, and inspire more robust computational training procedures. Comparable results from auditory development suggest a broader phenomenon, demonstrating how what may appear to be “limitations” can adaptively shape perception and cognition over time.

From Degraded Inputs to Robust Sensory Cognition: A Computational Perspective on Early Perceptual Development

Scientific representations and their constituent concepts change over time to reflect improvements in our understanding of the world. Similar improvements in understanding lead to changes in DNN-procured representations and their features. In this paper, we investigate whether useful methodological practices in concept change and in feature change carry across the two types of representations. We argue that there is indeed considerable potential for methodological cross-pollination and offer some examples of how such benefit may be derived.

Concept and Feature Change in Scientific and Deep Neural Net Representations

Proponents of the Symbol Grounding Problem have claimed that unimodal text-based AI systems can never develop meaningful representations of the world since they lack the capacity to perceive it. Perception is a relation between an agent and their environment which is grounded in perceptual processing. The earliest stages of perceptual processing involve receptivity to sources of perceptual signal in the environment: light waves, pressure waves, and volatile airborne chemicals are all sources of perceptual signal, insofar as agents appropriately receptive to their properties can (with further processing) perceive the world through them. I argue that (1) human-generated text carries sufficient information about the world to be a possible source of perceptual signal for appropriately receptive agents, and that (2) recent generations of Large Language Models (LLMs) are such agents. Although (1) and (2) do not entail that LLMs are perceivers, they do entail that symbol grounding is achievable without multimodality.

Text as a source of perceptual signal

Despite the prevalence of social learning in humans, the cognitive mechanisms underlying social transmission of behavior are not fully understood. We refine and expand a recently-developed paradigm, the Social Multi-Armed Bandit (SMAB) task, to systematically manipulate social information and measure its effects on human decision-making during predictive learning. We compared a dyadic task, in which social influence is maladaptive, to a control task in which participants receive no task-related social information. We found that misleading social information resulted in more maladaptive choices in the Dyadic than the Control condition, confirming findings from a prior study (Adrian, Siddharth, Baquar, Jung, & Deák, 2019). Although the maladaptive Dyadic social effect attenuated across trials and between two "games" (100 trials each), correlations between partners’ higher-order response patterns persisted games. These correlated response patterns between individuals within dyads suggest a tendency to emulate higher-order patterns (i.e., heuristics or strategies). The results imply that adults sometimes emulate decision strategies even when the outcome is disadvantageous. They also suggest that social learning may be reflected in higher-order response patterns even after people learn that imitating specific actions is maladaptive.

Joint Action and Reward-Seeking in a Social Probability-Learning Task

Background: Delta-beta coupling (DBC) is a neural marker of emotion regulation (ER), with elevated DBC linked to cortical over-processing of emotional stimuli. This study investigates the effects of the Be Healthy in Pregnancy (BHIP) intervention, combining a high-protein, energy-controlled diet, nutrition counseling, and physical activity, on offspring DBC.
Methods: Pregnant individuals received either the BHIP intervention or usual care. Twenty-four offspring at follow-up completed resting-state EEG at age two using a 128-channel system. DBC was quantified as the correlation coefficient between delta (2–4Hz) and beta (13–30Hz) power across epochs. Group differences were analyzed using Fisher’s Z-tests.
Results: BHIP offspring exhibited significantly lower DBC in frontal (p=.017), central (p=.014), and parietal (p=.009) regions compared to controls.
Conclusion: Reduced DBC reflects a neural profile linked to efficient ER, enabling context-appropriate cognitive resource allocation. These findings suggest prenatal diet and exercise potentially modulate neurodevelopment, warranting validation in larger, more diverse cohorts.

EEG Delta-Beta Coupling in 2-year-old Offspring of Pregnant Persons Receiving a Diet-and-Exercise Intervention: A Randomized Controlled Trial Follow-up

Research in the cognitive sciences has demonstrated infants’ remarkable ability to mentalize, such as their capacity for goal attribution (Woodward, 1998). However, failed replications of seminal findings have brought into question the strength of such capacities. One possibility is that infants are likely to mentalize in socially relevant/evaluative contexts (Woo et al., 2023). In two pre-registered studies, the present work investigated this possibility by testing whether 3-month-olds (via VoE; Woodward, 1998) and 11-month-olds (via anticipatory looking; Cannon & Woodward, 2012) will show stronger evidence of goal attribution for social versus nonsocial goals. Preliminary analyses revealed surprising results: Three-month-olds (N=52) show evidence of attributing location goals only in the social condition. By contrast, 11-month-olds (N=36) showed the reverse: Their anticipatory eye-movements showed evidence of attributing location goals in the nonsocial condition. The poster will present data from the full target sample (N=64) and further interpret these novel findings.

Exploring 3- and 11-month-olds' understanding of social versus nonsocial goals

Despite the popularity of MOOC, only a small percentage of course participants complete the course. Learners' confusion is one of the factors that impacts the overall learning process and ultimately leads to course attrition. However, extensive exchange of reviews often creates chaos, resulting in 'confusion' posts that can easily be ignored. To this end, we create a labeled dataset for post-type classification and use the public Stanford MOOCPosts dataset to detect learning confusion. We propose a hierarchical multi-task learning framework that combines post-type and confusion degree classification using fine-tuned BERT models and virtual adversarial training. Our model performs with an accuracy of 89\% and an F1 score of 88\%. Additionally, we integrate interpretability techniques to further enhance model transparency. This framework equips instructors with tools to identify and address learning confusion effectively.

Enhancing Educator Support in MOOC Forums: A Multi-Task Model for Detecting Learning Confusion

This paper examines the role of cognitive biases in the decision-making processes of large language models (LLMs), challenging the conventional goal of eliminating all biases. When properly balanced, we show that certain cognitive biases can enhance decision-making efficiency through rational deviations and heuristic shortcuts. By introducing heuristic moderation and an abstention option, which allows LLMs to withhold responses when uncertain, we reduce error rates, improve decision accuracy, and optimize decision rates. Using the Balance Rigor and Utility (BRU) dataset, developed through expert collaboration, our findings demonstrate that targeted inspection of cognitive biases aligns LLM decisions more closely with human reasoning, enhancing reliability and suggesting strategies for future improvements. This approach offers a novel way to leverage cognitive biases to improve the practical utility of LLMs across various applications.

Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions

Designing effective social and policy systems is a vital and forbidding challenge made more difficult because, in real-world settings, individuals don’t just passively accept the static environments imposed upon them: they act both within and upon the social systems that structure their interactions. Should we expect player-driven changes to the ”rules of the game” to benefit cooperation, as agents tweak their environment toward non-zero-sum games — or hinder it because of the challenges of constant change? We introduce a laboratory setting to test whether groups can guide themselves to cooperative outcomes by manipulating the strategic environment that structures their interactions. By offering players “first-order” choices within an economic game (agency over behavior) along with “second-order” choices between games (agency over the rules of the game), we understand emergent cooperation in naturalistic settings in which the rules of the game are themselves dynamic and subject to choice.

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Children's emotion vocabulary learning discloses a growing understanding of specific concepts