United States

Misleading visualizations are increasingly prevalent, with a surge in their negative impact on data interpretation, often leading to misunderstandings and poor decision-making. To address this issue, we investigate the impact of informative feedback within perceptual training targeting misleading visualizations. Our results show that informative feedback significantly enhances viewers’ perceptual skills, improving both accuracy and efficiency in interpreting misleading data visualizations. Additionally, participants demonstrated a transfer effect, applying their developed perceptual skills to novel misleading visualizations beyond those encountered during training. These findings highlight the potential of perceptual training with informative feedback to strengthen viewers’ resistance to misleading data visualizations, offering valuable insights for educational practices aimed at fostering viewers’ resistance to misleading data visualizations.

CogSci 2025

Perceptually Training Viewers against Misleading Data Visualizations with Informative feedback

instruction and teaching

learning

education

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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### 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

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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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.

The aim of this study is to analyse the auditory scene of musical works and to demonstrate that different compositions may prompt the emergence of distinctly interpreted perceptual streams in the listener’s mind. The research focuses on selected excerpts from works by Alexandre Guilmant, Ludwig van Beethoven, and Antonio Vivaldi, which, due to their unique characteristics, elicit diverse auditory impressions. By combining score analysis with auditory scene analysis, this paper seeks to explain how different interpretations of the same sounds result in dissimilar auditory impressions. The auditory scene analysis presented here provides deeper insight into the process of stream formation and its implications for musical performance and aesthetic perception. The findings indicate that perceptual stream formation in music is considerably more complex and context-dependent than previously assumed, with implications for how listeners interpret auditory scenes.

Variable Properties of Auditory Image Analysis: A Case Study of Selected Musical Works

Recent research has suggested that some children, after they learn the meaning of “four,” proceed to learn the next few numbers one at a time (Krajcsi & Fintor, 2023). This claim is in direct contrast with previous theories that argued for an inductive leap after learning “four” (Carey, 2009). We assessed children’s number knowledge using an adapted Give-N method that captured higher set sizes, and tests of executive function and working memory as an exploratory window onto the changes across stages. First, results support the claim that children exhibit partial-number knowledge beyond “four”. Second, executive function was associated with children’s jump from knowing numbers up to “four” (subset-knower stages) to knowing numbers above four, while working memory was associated with the change from partial to putatively full knowledge of number word meanings.  These findings offer new insight into the conceptual change process and suggest a potential two-fold sequence in children’s induction of cardinality.

Continuity and discontinuity in children’s number acquisition 

Considerable debate exists over the kinds of numbers the Approximate Number System (ANS) can represent and compute over. Across three experiments (N = 218), we show that children can represent and add large mixed sets (i.e., large collections that include two types of items) with the ANS. In Experiment 1, 5-7-year-olds completed a replication of a large non-symbolic number addition task using an online asynchronous format. In Experiments 2 and 3, 5-7-year-olds completed a variation of that addition task with mixed sets of stimuli either area-controlled or area-correlated and again performed above chance level. Taken together, these findings are a crucial first step in examining whether the ANS can represent all positive rational numbers (i.e., fractions or ratios), as opposed to exclusively integers. In sum though, our findings suggest that children can represent and compute over large mixed sets of stimuli with the ANS.

Can children represent and compute over mixed sets with the Approximate Number System?

How are complex words recognized during the early moments of visual word recognition? What roles do full word and constituent frequency play in semantic processing? The present study addressed these questions by employing a word-picture relatedness task with brief stimuli presentations designed to tap the early mapping of orthographic input onto semantic representations. The main manipulation involved first presenting a picture depicting the target word’s constituent (200 ms), followed by the presentation of the target word (56 ms). We compared the rate of positive relatedness judgements elicited by picture-word pairs between suffixed (SKI-skier), pseudo-suffixed (MOTH-mother), and non-suffixed words (CAN-canoe). Results suggest that the “constituents” of all three word types are semantically accessed, although with a suffixed word advantage. Regression analyses did not corroborate behavioral findings as no full word and constituent frequency effects were obtained. We discuss the implications of these findings for models of the visual word recognition system.

Accessing the meanings of sublexical forms during visual word recognition

Sign languages are perceived visually and externalized using a signer’s hands, face, and upper body. During sign language comprehension, deaf signers primarily focus their gaze on the face, while hearing non-signers attend more to the hands of a signer. Little is known about whether deaf signers adapt their gaze behaviour when processing unknown signs. Here, we report eye-tracking data from 15 deaf native signers of German Sign Language (DGS) and 15 hearing non-signers who were presented with videos in either DGS or an unknown sign language, all containing no linguistic mouth actions. Our data confirm that deaf signers generally fixate more on the face of a signer than hearing non-signers who attend to the hands in sign space. Moreover, only deaf signers increase their attention to the hands when processing video stimuli consisting of unknown signs compared to familiar signs, suggesting similar adjustment behaviours as observed in spoken languages.

Deaf Signers Adapt Their Eye Gaze Behaviour When Processing an Unknown Sign Language 

Facial expressions are essential to human interaction, providing emotional cues that influence how we perceive and respond to others. Advances in artificial intelligence (AI) now enable real-time manipulation of these expressions, raising ethical concerns about AI-mediated communication (AI-MC). This project investigates the effects of AI-based facial expression alterations on prosocial behavior and empathy using a choice-exposure counterfactual paradigm. It addresses two key questions: (1) Do facial AI filters influence empathy and prosocial behavior? (2) Do individuals strategically use AI filters to justify self-serving actions? Participants chose between authentic and AI-filtered videos before making donation decisions after exposure. In an initial study, neutralizing AI filters showed no significant effects on empathy or donations. Building on these findings, we will explore stronger filters and more emotionally expressive portrayals to better understand their impact. This research aims to advance knowledge of the psychological and ethical implications of AI-MC.

Filtering out Empathy: The Impact of Other-Alteration through AI-Filters on Prosocial Behavior

Recent research has compared representation models of word meaning (Brown et al., 2023, Cognitive Science 47:e13291), however, less research has compared representation models of words’ perceptual features. Thus, we compared vector-space word representation models that can be used to quantify words’ phonological similarity. The main structure of the model was adapted from Cox et al.’s orthographic representation model (Behavior Research Methods 43:602-15, 2011). Variations of the model included phonetic mapping scheme, encoding scheme, the inclusion of lexical stress, and the combination of orthographic and phonological representations. We tested the model variants against human-rated phonological similarity and both phonological and orthographic Damerau-Levenshtein distance. Open n-gram encoding (1 ≤ n ≤ 2) performed better overall than terminal relative encoding across all phonological similarity metrics.  Concatenated orthographic-phonological vectors improved the prediction of human ratings with terminal-relative encoding only. Using more fine-grained phonetic mapping or including lexical stress had minimal effects.

Exploring Vector Representations for Phonological Similarity

Interactions between agents are supported through a continuous process of detecting and responding to behaviors that are contingent upon the other agent's behavior. Here, we explore the temporal dependence of these mechanisms, focusing on the role of timescale compatibility in inter-agent interactions. Using continuous-time recurrent neural networks (CTRNNs) to control embodied agents in a minimal social interaction task, we demonstrate that effective interactions require agents to operate on compatible timescales. Our results indicate that time scale mismatches disrupt agents’ ability to distinguish other agents from non-social entities, revealing a timescale threshold beyond which agents begin misclassifying slower agents as static objects and faster agents as non-social animate objects.

The Effect of Timescale Dependence on Dyadic Interactions

Visuo-spatial abilities contribute to successful athletic performance (Millard et al., 2021), and the results of visio-spatial tests have a predictive and diagnostic value (Moreau et al., 2012). In our study, we test participants from grassroots sports (n=186) and adapted sports (n=30) with tests covering allocentric and egocentric cognition (Mental rotation (Shepard & Metzler, 1988), Perspective taking (Kozhevnikov & Hegarty, 2001), Santa Barbara Sense of Direction Scale (SBSOD) (Hegarty et al., 2002)) using a web application. We compare differences in spatial skills in both groups depending on demography (age, gender) and sports training characteristics (type of sport, training regularity, experience). Preliminary results suggest that (1) mental rotation performance is similar across groups; (2) participants from the grassroots sports perform better in the perspective-taking test; (3) SBSOD measurements are slightly better in the adapted sports.

Spatial skills in grassroots athletes and athletes with functional impairments: A screening study in Latvia

This study examines how instructional framing influences probability distortions in decision-making scenarios. With 136 participants, we explored four instruction conditions: direct calculation guidance, estimative averaging, and narrative framing from first- and third-person perspectives. Our findings indicate that skewness, rather than variance, significantly impacts estimation errors in expected value tasks. The estimative instruction condition notably reduced probability neglect, whereas direct calculation instructions unexpectedly introduced bias. Both narrative conditions amplified probability neglect, with no significant difference between perspectives. These outcomes challenge traditional assumptions in decision-making models, emphasizing the central role of skewness and the substantial effect of instructional framing on probability distortions. The results suggest that employing estimative instructions could effectively minimize biases in contexts where accurate evaluation of expected value is crucial. This research underscores the importance of instructional design in decision-making tasks and provides insights into minimizing probability neglect.

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Variable Properties of Auditory Image Analysis: A Case Study of Selected Musical Works

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Protecting multimodal large language models against misleading visualizations

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