United States

Number-line estimation tasks (NLETs) have been used to assess symbolic numerical skills (SNS; Booth &amp; Siegler, 2008; Lyons &amp; Ansari, 2015) and have also been associated with the approximate number system (ANS; Khanum et al., 2016; Wong et al., 2016). A recent study with 6–7-year-old children in Sweden (Morell-Ruiz et al., 2025) provided evidence that training NLE abilities can help bridge these two numerical systems, suggesting that the ANS may actively scaffold the development of the SNS. Building on this, we designed a novel two-choice NLET compatible with Drift Diffusion Model (DDM) fitting, allowing us to decompose children’s estimation processes into interpretable parameters. Our results show that DDM parameters significantly correlate with performance in both symbolic and nonsymbolic tasks, and that performance on the two-choice and standard NLETs is strongly correlated. These findings validate our paradigm, offering new insights into the cognitive mechanisms linking numerical representations via number-line estimation.

CogSci 2025

On the Role of Nonsymbolic and Symbolic Numeracy Skills in Number-Line Estimation Processes

development

decision making

learning

psychology

education

Number-line estimation tasks (NLETs) have been used to assess symbolic numerical skills (SNS; Booth & Siegler, 2008; Lyons & Ansari, 2015) and have also been associated with the approximate number system (ANS; Khanum et al., 2016; Wong et al., 2016). A recent study with 6–7-year-old children in Sweden (Morell-Ruiz et al., 2025) provided evidence that training NLE abilities can help bridge these two numerical systems, suggesting that the ANS may actively scaffold the development of the SNS. Building on this, we designed a novel two-choice NLET compatible with Drift Diffusion Model (DDM) fitting, allowing us to decompose children’s estimation processes into interpretable parameters. Our results show that DDM parameters significantly correlate with performance in both symbolic and nonsymbolic tasks, and that performance on the two-choice and standard NLETs is strongly correlated. These findings validate our paradigm, offering new insights into the cognitive mechanisms linking numerical representations via number-line estimation.

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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* **Society Creation**<br>
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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.

Building on Shin (2022), the present study examines how Korean monolingual children comprehend suffixal passive constructions by employing a webcam eye-tracking method, aiming to test two theoretical accounts of grammatical generalisation (gradual vs. early abstraction). Twenty-eight children aged three to six, alongside 20 adults, joined picture-selection experiments paired with eye-gaze measurements. The findings indicate that children’s utilisation of passive-voice heuristics remains limited yet developing, overshadowed by well-entrenched active-voice knowledge. In particular, the eye-gaze data reveal processing challenges related to the passive voice, mainly interpretive difficulties arising from passive morphology. These results replicate those of Shin (2022), offering further support for a moderate version of each account that emphasises the pivotal role of linguistic exposure in mastering linguistic knowledge. From a methodological standpoint, this study enhances the accessibility of webcam eye-tracking research for understudied languages in the field.

Korean monolingual children’s comprehension of suffixal passive construction:  A webcam eye-tracking study

Functional category induction with theory-neutral cognitive biases

Manipulating Predictive Focus Improves the Taste Appreciation of Coffee

The approximate number system (ANS) enables humans to rapidly estimate numerical quantities without relying on counting, yet the computational processes underlying the ANS remain mysterious. Here, we propose a mechanistic model for the ANS, built on the core idea that when presented with a stimulus array, observers first form a template representation through ensemble averaging and subsequently engage in a template-matching process, whereby each array item is compared to the template. With a limited set of plausible assumptions about the inherently noisy computational processes, our model naturally accounts for Weber’s law, the main hallmark of number psychophysics, systematic numerosity underestimation and the coherence illusion. We further test two novel predictions about stimulus factors that modulate the strength of the coherence illusion and demonstrate high fidelity between predicted and obtained data. This model offers new insight into the computational mechanisms by which ANS representations are derived from visual input.

Noisy template matching: A mechanistic model of approximate number perception

Research in experimental syntax typically assumes that the five-point Likert scale offers an ordinal probe that maps monotonically onto a latent degree of sentence acceptability. We challenge that assumption by showing that, when the stimulus space is densely populated, speakers repurpose the scale into an anchor-and-baseline device. Two large-N experiments (Russian and Serbo-Croatian; N=237; 120 permutational word-order variants per language) elicited over 28000 sentence acceptability ratings. Plotting Shannon entropy of the response distribution against the mean rating reveals a robust ’entropy arch’: uncertainty climbs to a sharp peak at the midpoint and collapses toward both ends. We interpret the arch as the quantitative fingerprint of constraint competition: the scale extremes serve as categorical anchors (’completely acceptable’ vs. ’completely unacceptable’), while the center functions as a floating baseline against which speakers register maximally uncertain, cue-balanced configurations for which grammatical, information-structural and frequency-based cues pull in opposite directions. Our findings reframe Likert data as the outcome of dynamic calibration rather than static preference strength and provide a simple diagnostic, entropy profiling, for locating linguistic ’tipping-point’ constructions. Beyond sentence acceptability, the approach offers a principled way to map regions of maximal competition in any domain where categorical anchors and graded uncertainty coexist.

Dense Sentence Sets Induce an Anchor-and-Baseline Strategy in Likert Scale Acceptability Judgments

It has been argued that when scientists employ algorithmic tools to assist in problem-solving, epistemic monocultures may emerge in which research tools, topics, findings, etc. are homogenized. As a result, fertile areas of research might be left unexplored, impeding scientific progress. To explore the nature of these epistemic monocultures, we develop an agent-based model where agents have the ability to query an AI system to assist in their search of an epistemic (NK) landscape. In general, we find that AI use negatively affects the community of researchers by reducing heterogeneity, but both the rate of AI queries and how AI is used impact the ultimate success of the community. We then implement a potential solution suggested in the literature, AI personalization, and find somewhat mixed results on its potential for mitigating homogenization in research communities.

Epistemic Monocultures and the Effect of AI Personalization

Do children believe AI deserves to be treated morally as humans, and if so, why? In this study, eighty children aged four to eight were randomly assigned to interact with a human or AI partner in a naturalistic storytelling activity, after which their moral considerations and rationale were elicited. The results indicated that although children believed they should treat both AI and humans morally, the motives behind their beliefs differed. Children's moral considerations for humans were driven by empathy for others' needs and feelings, whereas their considerations for AI were motivated by the desire to preserve its functional utility. These motives were corroborated by children's denial of AI’s capacity to have feelings or basic physical needs. This study contributes to the literature on children's moral development by demonstrating that while their moral reasoning extends to non-human entities, their justifications reflect a distinct, domain-specific understanding rather than an anthropocentric confusion.

Empathy or Utility: Children's Reasoning for Moral Consideration of AI

Literature indicates that lingua franca use in clinical settings impacts bilinguals communicating in their second language (L2)—but what drives these changes? This study examined how language, proficiency, and cognitive load influence emotionality during traumatic autobiographical memory (AM) retrieval. Building on evidence that L2 use diminishes emotional resonance, we hypothesized that proficiency would moderate this effect. Additionally, we expected lower emotionality in first-language (L1) recall under cognitively demanding conditions, mirroring second-language effects. Greek native speakers (N=107) recalled a traumatic AM in L1, L2 (English), or L1 under cognitive load (visuospatial working memory task) while undergoing physiological EDA assessment. Self-reported arousal, distress, and traumatic symptoms were measured. Notably, L2 alone did not lower emotional intensity—only lower L2 proficiency did, highlighting its key moderating role. Cognitive load during L1 retrieval significantly decreased emotionality, supporting resource-dependent emotional processing models. These findings reveal proficiency and cognitive demands’ interplay in shaping bilingual emotional processing.

Same memory, different words: The Role of Language, Proficiency and Cognitive Load in Traumatic Autobiographical Memory Recall

Stimulating language learners’ engagement is essential to successful second language acquisition, but it can be hard to translate this intuition into effective learning resources. In the first large scale investigation into the linguistic and pragmatic features that make an educational conversation interesting, we collected interest ratings for 64 conversations between teachers and second language learners of English. We provide proof of concept that - despite the high degree of subjectivity involved in perceptions of interest - it is possible to extract features that make a conversation interesting for the average learner. Specifically, concreteness, comprehensibility, and uptake (i.e., the degree to which a teacher and a student’s turn build on one another) all had unique relations to interest in our data. These findings lay the foundations for future work on the optimization of AI tutors for more engaging language learning interactions.

What makes a conversation interesting? Linguistic features predictive of interest in educational conversations between teachers and learners of English

The current study examines 120 5 to 10-year-old children’s beliefs about adults’ abilities to use and fix tablet technology when those adults belong to varying gender (man, woman) and age (young, old) categories. The results indicate that, overall, children appear to prioritize age over gender when judging adults’ technological knowledge, with children choosing younger adults as more competent at using and fixing tablets than older adults. In addition, when evaluating adults of the same age category (e.g., a young man and a young woman), children show in-group gender-based preferences where girls choose women and boys choose men. This in-group preference is more pronounced in children’s selections of adults when determining who would be better at fixing tablets than who would be better at using these devices. Implications for children’s developing ability to consider intersectional identities based on gender and age, and for their STEM learning, are discussed.

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Korean monolingual children’s comprehension of suffixal passive construction: A webcam eye-tracking study

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