United States

Many deaf children first encounter a language in deaf schools, often experiencing adverse effects of late exposure on several aspects of language. Little is known about how late acquisition of sign language affects lexical sign acquisition and how the structural architecture of signs modulates this process. Here, we investigate the lexical development of late-signing children (N=11; MeanAge=84.7 months) through 8-week intervention using mobile-compatible web-app teaching lexical signs in Turkish Sign Language to investigate whether (1) children acquire basic signs during intervention and (2) phonological complexity influences this process. Preliminary results revealed a significant improvement in sign learning (β=1.7545, p&lt;0.001) regardless of phonological complexity (ps&gt;0.05). Findings underline the importance of accessible platforms to support language development for this population and extend previous work on the effect of phonological complexity on articulation accuracy during learning in hearing adults by showing its lack of influence in deaf children on 2-choice recognition without articulation.

CogSci 2025

An Intervention Program: Helping Deaf Children with Hearing Parents in Acquiring Turkish Sign Language

field studies

psychology

language acquisition

linguistics

### Welcome to CogSci Conference 2025!

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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.

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#### 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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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.

Many deaf children first encounter a language in deaf schools, often experiencing adverse effects of late exposure on several aspects of language. Little is known about how late acquisition of sign language affects lexical sign acquisition and how the structural architecture of signs modulates this process. Here, we investigate the lexical development of late-signing children (N=11; MeanAge=84.7 months) through 8-week intervention using mobile-compatible web-app teaching lexical signs in Turkish Sign Language to investigate whether (1) children acquire basic signs during intervention and (2) phonological complexity influences this process. Preliminary results revealed a significant improvement in sign learning (β=1.7545, p<0.001) regardless of phonological complexity (ps>0.05). Findings underline the importance of accessible platforms to support language development for this population and extend previous work on the effect of phonological complexity on articulation accuracy during learning in hearing adults by showing its lack of influence in deaf children on 2-choice recognition without articulation.

poster

Light verb constructions (LVCs) are grammatical structures where a verb lacking semantic content combines with another structure (a noun phrase) to provide semantic value (e.g., take a photo). Previous cross-linguistic research shows LVCs are distinct from other verb types (Butt 2010), and that distinct representations are activated during verb comprehension (Richardson et al., 2003), but it remains unclear what representations, particularly of time, are activated during comprehension of LVCs. The present study manipulates the specific verb comprising the LVC (e.g., ‘turn attention to’ versus ‘dedicate attention to’) to probe whether one’s perception of the duration of that action is affected. We test different LVCs cross-linguistically, and in both bilingual second-language speakers and monolingual speakers of English. Preliminary findings indicate that the specific verb used in the LVC influences one’s perception of the duration of that action, with specific verbs generating interpretations of shorter durations while others much longer.

Investigating representations of time in light verb constructions during verb comprehension

Stopping decisions are frequently modeled as decisions to switch to alternative activities once the current activity stops being adequately rewarding, such as in optimal foraging theory, as well as more recent metacognitive models. However, the sense of stopping and making decisions in such frameworks is highly platonic, with both decisions and stopping actions occurring instantaneously. In contrast, the phenomenology of quitting actions that one is undertaking appears to be temporally extended and metacognitively challenging. We study the metacognitive covariates of quitting decisions made by chess players using a large database of chess games sourced from an online chess portal. Our analysis reveals that players tend to persevere when they are playing against stronger opponents and after having played poor moves. We also find that a history of quitting games makes players more likely to quit in future games, but that having recently quit in a game offers some protective effect against quitting. Finally, we find that quitting a game makes it more likely that a player will play a game again soon. We place these results in the context of modeling quitting as a metacognitive choice affected by multiple competing goals.

A metacognitive appraisal of quitting

A widely studied cognitive bias in decision-making is the attraction effect, in which introducing a clearly inferior decoy option into a binary choice set increases the likelihood of selecting the dominating option (target) over the competitor. While this effect is robust with numerical stimuli, findings with perceptual stimuli have been inconsistent, with some recent studies even reporting a negative attraction effect. We argue that this distinction between numerical and perceptual stimuli is superficial, and that choice behavior is better explained by the inter-attribute relationships. In two experiments, we demonstrated positive attraction effects using combined perceptual-numerical stimuli and both positive and null effects with numerical stimuli by manipulating the asymmetry in pairwise comparison difficulty. The numerical stimuli used in Experiment 2 leveraged the findings from fraction research. Together, our results challenge the numerical-perceptual distinction and support a universal cognitive mechanism underlying the attraction effect.

Re-evaluating the Numerical-Perceptual Distinction in the Attraction Effect

Mild cognitive impairment (MCI) detection using eye-tracking (ET) signals in virtual reality (VR)-based cognitive tasks shows great promise, as it can capture rich temporal and behavioral information. Therefore, we build four VR-based tasks based on Stroop test and construct a dataset for MCI detection using ET signals. However, ET signals often suffer from non-stationarity,variability, and redundancy, challenging accurate MCI detection.To address these issues, we propose a novel lightweight network KWS-TA-CNN with three key components: 1) Kymatio Wavelet scattering transform (KWS), which generates time-robust features and reduces memory usage through a depth-first traversal strategy; 2) Temporal Attention (TA) to dynamically weight critical time steps for MCI detection; and 3) 1D Convolutional Neural Network (CNN) to capture local temporal patterns and reduce feature redundancy. Experimental results from leave-one-subject-out cross-validation show high performance, with subject-level accuracies of 0.8158, 0.9211, 0.8158, and 0.8421 across the four tasks, demonstrating its strong clinical potential.

KWS-TA-CNN Network: Towards Lightweight Mild Cognitive Impairment Detection Using Eye-Tracking Signals From Virtual Reality Stroop Test

This study investigates the role of memory mechanisms in the emergence of communication by comparing two instance-based learning models: one implemented using the ACT-R cognitive architecture and the other using PyIBL, a lightweight framework based on Instance-Based Learning Theory. Both models were tested on a simulated communication task requiring agents to coordinate actions message exchange using abstract symbols. The ACT-R model, featuring an explicit goal-representation module and precise memory structure, led to faster formation of communication system and more successful task performance. In contrast, the PyIBL model showed delayed emergence of communication system, attributed to its simplified memory representation and difficulty in imitation during the task. These results suggest that detailed goal representation and mechanisms for self-other distinction play a critical role in communication development. The study also demonstrates the potential of cognitive modeling for connecting individual-level processes with large-scale simulations of social behavior.

Emergence of Communication: A Comparative Study of Instance-Based Learning in ACT-R and PyIBL

Approximately 31% of the global population is insufficiently active, leading to 3.2 million deaths annually. Sedentary behavior increases the risk of cancer, heart disease, and metabolic disorders. To improve adherence to behavior change interventions, this study introduces personalized humorous nudging for walking behavior. Following a Randomized Control Trial (RCT) design, participants (N=100) received either humorous or non-humorous nudges via a Telegram-based chatbot, “WalkMore,” encouraging daily walks for 7 days. We used the AICTP smart nudge framework (Activity, Influence, Content, Time frame, Presentation) and added humor to the presentation component. Results showed a significant relationship between walking and humor (F=6.48, p<0.05). Manipulation check items reported the nudges’ average funniness rating as 3.48 in experimental group and average personalization rating as 3.64 in control group. The findings indicate efficacy of the proposed augmented nudge framework for behavior change.

WalkMore: Exploring the Role of a Personalized Humorous Nudge Architecture on People’s Walking Behavior

A key challenge in cognitive modeling is capturing how emotional states modulate internal cognitive parameters. While cognitive architectures such as ACT-R (Adaptive Control of Thought-Rational) provide a principled framework for simulating memory and decision-making, their emotional components remain underexplored. This study examines how individual differences in emotional states, particularly anxiety and affective valence, are reflected in core memory-related parameters of ACT-R. Across two experiments using a digits recall task, we introduced emotional variation via affective stimuli and applied a model-fitting procedure to estimate individual values for the mismatch penalty and activation threshold. Results from the second experiment revealed significant correlations between state anxiety and both parameters, suggesting that emotional traits systematically shift memory retrieval dynamics. Our findings offer empirical support for integrating emotion into cognitive architectures without introducing ad hoc modules, and contribute to broader efforts to align the Common Model of Cognition with affective science. This work highlights the potential of inverse modeling as a tool for understanding the emotion–cognition interface and opens new avenues for modeling individual differences in affect-sensitive cognitive systems.

Emotional Parameters in Cognitive Architecture: Examination Through Simple Memory Performance

Considering the effect of collaborative learning based on the Interactive-Constructive-Active-Passive (ICAP) theory, learners can deepen their understanding by engaging in interactive activities in which they elaborate their knowledge by integrating others’ elaborated knowledge. However, it is unclear from the comparison between worked-out and erroneous worked-out examples whether they facilitate interactive and deepen understanding. Therefore, this study examines the effects of erroneous worked-out examples in collaborative learning. We employed a cognitive model based on Adaptive Control of Thought-Rational (ACT-R) to present concept map examples based on learners' relevant knowledge. Errors were adopted link error because links are important for understanding knowledge. The results showed that the worked-out example improved learning performance, but did not facilitate the collaborative learning process. Moreover, the erroneous worked-out example enhanced learning performance by facilitating interactive. These results provide insights into the effect of an erroneous worked-out example and a strategy for presenting it.

Using Erroneous Worked-out Examples for Supporting Collaborative Learning: An Investigation Based on the Cognitive Model of Link Errors using ACT-R

The Optimal Viewing Position (Nazir, Heller &
Sussman, 1992; Brysbaert & Nazir, 2005) for reading
visual words is well studied and documented in most
of alphabetic languages (Grainger, 2022). The
current study investigates the optimal viewing
position in Hindi, written using the Devanagari
script in attempt to understand reading universals
across scripts as suggested by Frost (20120. We
carried out two experiments using the lexical
decision task, one each on words with maatraa (3, 4
and 5 varna) and without maatraa (2, 3, and 4 varna).
Maatras are diacritics used to manifest vowel
pronunciations with aksharas in Hindi and can be be
marked all around the base akshara, adding to the
graphemic complexity of a Hindi word (for a detail
review see, Share et al., 2015; Rimzhim et al., 2021;
Verma et al., 2021). The results demonstrate robust
optimal viewing position effects in terms of reaction
times with a U-shaped curve in relation to initial
fixation position and the optimal viewing position
has been found to be slightly left to the centre of the
word. These seem to be similar to the OVP findings
reported for European languages (Brysbaert & Nazir,
2005) and may add information about reading
universals across scripts and writing systems.
Keywords: Optimal Viewing Position, Hindi,
Lexical Decision Task

Universals in Visual Word Recognition: Investigating the Optimal Viewing Position for Visual Words in Hindi

Emotion recognition from electroencephalogram (EEG) signals is critical for applications in mental health, human-computer interaction, and adaptive systems. However, existing methods struggle with modeling long-term dependencies and addressing the ambiguity between emotion classes. To address these challenges, we propose Mamba-CCA, a novel framework that combines Selective State Space Modeling (SSM) with a Class Confusion-Aware Attention (CCA) mechanism. Mamba-CCA leverages the efficiency of Mamba’s linear-time modeling to capture both local and global temporal features in EEG signals while significantly reducing computational costs. The CCA mechanism further enhances classification by dynamically resolving ambiguities between emotional classes. Experimental results on the SEED and SEED-V datasets demonstrate that Mamba-CCA achieves state-of-the-art classification accuracies of 96.02% and 83.54%, respectively, surpassing the previous best model, CSET-CCA, by 0.84% and 1.48%. Additionally, Mamba-CCA reduces inference time by 20.12% and computational cost by 21%, making it highly suitable for real-time applications.

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Investigating representations of time in light verb constructions during verb comprehension

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