United States

Detecting dynamic spatial relationships, such as pursuit, can be cognitively demanding (Scholl &amp; Gao, 2013). Research has identified visual cues that influence pursuit detection, including the distance between the pursuer and the target (Meyerhoff, Schwan &amp; Huff, 2014) and the number of objects in a scene (Gao, Baker, et al., 2019; Kon, Khemlani &amp; Lovett, 2024). We turn to data visualization research to explore techniques to improve pursuit detection in dynamic scenes. For example, displaying trajectory histories can aid processing by reducing cognitive load (Heer &amp; Robertson, 2007). We conducted a study in which participants viewed visualizations of six moving dots, either with or without trace lines, and determined whether one dot was chasing another. Preliminary findings suggest that trace lines improve the speed and accuracy of pursuit detection. Our results bridge visualization and vision science, suggesting that trace lines might enhance pursuit detection by providing less transient shape-based visual cues.

CogSci 2025

Visualizing Motion Traces Enhances Pursuit Detection in Dynamic Scenes

psychophysics

spatial cognition

computer science

psychology

perception

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

<iframe width=580 height=700 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

Please log in to explore this event. You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

Registration for this workshop has now closed. We appreciate your interest and hope to welcome you at a future event. If you’d like to hear about similar upcoming events, please check our website or join our mailing list.

Registration for this workshop has now closed.

NAML 2021

The annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


Detecting dynamic spatial relationships, such as pursuit, can be cognitively demanding (Scholl & Gao, 2013). Research has identified visual cues that influence pursuit detection, including the distance between the pursuer and the target (Meyerhoff, Schwan & Huff, 2014) and the number of objects in a scene (Gao, Baker, et al., 2019; Kon, Khemlani & Lovett, 2024). We turn to data visualization research to explore techniques to improve pursuit detection in dynamic scenes. For example, displaying trajectory histories can aid processing by reducing cognitive load (Heer & Robertson, 2007). We conducted a study in which participants viewed visualizations of six moving dots, either with or without trace lines, and determined whether one dot was chasing another. Preliminary findings suggest that trace lines improve the speed and accuracy of pursuit detection. Our results bridge visualization and vision science, suggesting that trace lines might enhance pursuit detection by providing less transient shape-based visual cues.

poster

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/amvr/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>

#### 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. 
<br><br>
* **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.
<br><br>
* **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.
<br><br>

#### 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)**.
<br><br>


You need to log in with the email address you registered with. 

Login credentials were sent to you from Underline -  subject line "Welcome to the CogSci 2025 Conference". Please be sure to check your spam/promotional inbox  if you do not see an email confirmation right away.





Please log in to join this event.

To access the site, please register [**here**](https://cognitivesciencesociety.org/registration/).

If you are registered and feel like you are seeing this message by mistake, please make sure you are logged in with the same email that you registered with. 

Please register!

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

Children with Developmental Language Disorder (DLD) exhibit longer reaction times (RTs) than age-matched neurotypical children. Drift diffusion models estimate parameters influencing RT distributions: drift-rate represents speed of information accumulation, non-decision time represents other factors contributing to longer RTs, e.g., poor attention or motor coordination. Using a hierarchical Bayesian framework, we modeled RT data from visual search and mental rotation tasks completed by 3rd graders (N = 248). Children with impaired verbal abilities without accompanying nonverbal impairment (DLD) and those with global verbal/nonverbal impairments were compared to neurotypical children. Across tasks, children with DLD exhibited a lower drift rate than neurotypical children, indicating slower information accumulation, and no difference in non-decision time. Children with global impairments showed lower drift rates and higher non-decision times than neurotypical children. Results suggest directions for nuanced tests of the generalized slowing hypothesis of DLD. Clinical implications for diagnosis and treatment of DLD are discussed.

Modeling Processing Speed in Developmental Language Disorder using Drift Diffusion Modeling

The Transformer architecture used in LLMs has garnered
widespread attention due to these model’s human-like
conceptual knowledge and language understanding, yet
understanding how these models’ capabilities result from
experience-guided learning, and connecting this learning
process with the structure in their training data, can seem
intractable. Here we present preliminary steps to
characterizing the developmental trajectory of a minimal
Transformer trained on a next-token prediction task, using a
simple dataset with quantifiable uncertainty and a simple,
intuitively characterizable structure that captures some aspects
of natural semantic structure learned by LLMs from large
datasets. We show how the dynamic learning process of this
model is a predictable consequence of the structure of the
training data, exhibiting attested features of human semantic
development, as captured in a theory of neural network
learning dynamics (Saxe et. al. 2019) previously used to
capture such dynamics in a network originally introduced by
Rumelhart & Todd (1993).

Extending a Mathematical Theory of the Emergence of Knowledge from the Experience to Capture Learning Dynamics in Transformers 

This study explores the role of coherence-based reasoning in belief updating within uncertain environments. We develop a novel computational model where agents update their beliefs based on observed evidence, with some evaluating the coherence of their belief set before accepting new evidence. Our results show that coherence-based evidence filtering improves belief accuracy in noisy environments and when agents' prior beliefs are accurate. However, when agents encounter systematically misleading evidence, coherence considerations lead to less accurate beliefs. These findings shed light on how coherence interacts with evidence quality and belief accuracy.

Coherence-Based Evidence Filtering: A Computational Exploration

In the studies of aesthetic appreciation, poetry has received less attention than other art forms.Furthermore, although a number of recent studies have focused on the factors that influence poetry appreciation, very few attempts have been made to explain the appreciation process.In this study, to explore the cognitive process of poetry appreciation, we propose a computational model of poetry appreciation based on the incongruity resolution theory, which has been proposed to explain the process of evoking poetic effects.Assuming that poetry reading comprehension is a process of evoking mental imagery from words in a poem, we model it as spreading activation in a semantic network.The notion of incongruity resolution is then modeled as the difference of the diversity of activation in the network. The computational model was tested using multiple regression analysis with simulation results as independent variables and human ratings for aesthetic appeal as a dependent variable. The analysis demonstrated that the degree of incongruity resolution computed by the model accounted for a significant portion of the variance in aesthetic appeal. This finding provides evidence for the validity of the model and suggests its potential for a more comprehensive model of poetic appreciation in literary works.

A computational model of poetry appreciation based on a spreading activation network and the incongruity resolution theory

Bilinguals perceive events differently in their native (L1) and second languages (L2; e.g., Dylman & Bjärtå, 2019). A possible explanation for this “foreign language effect” is that L2 makes the described event feel less personal or emotional, leading to more analytical thinking. Prior research has (1) focused predominantly on negative emotions, and (2) not considered individual differences in how relatable an event feels, potentially modulating emotional intensities. This study tests both positive and negative events and how relatable each event feels. Participants, randomly assigned to the L1 or L2 condition, read 8 happy and 8 sad stories, and rated the emotion they felt for each story and its similarity to their personal experience. Preliminary descriptive results (N=17) suggest that emotion is felt more intensely in L1, regardless of valence. With the final dataset, we will examine whether Language, Valence, and Relatability independently and interactively predict the felt emotion.

Experiencing Positive and Negative Emotions in L1 vs. L2

This study investigates how stress and anxiety influence the interplay between visual working memory and decision-making in human participants. Using the Socially Evaluated Cold Pressor Test (SECPT) to induce acute stress, we examined cognitive performance on a computerized behavioral paradigm (the Marble Jar Task) requiring the storage, manipulation, and utilization of visual information. Results revealed that while experimentally induced stress did not significantly affect overall accuracy, higher self-reported state anxiety was correlated with both lower decision-making accuracy and poorer visual memory performance. Interestingly, higher state anxiety was also correlated with increased attention towards high-value outcomes in decision-making. This work highlights the importance of understanding how stress and anxiety affect the interaction between interconnected cognitive functions, rather than studying isolated cognitive phenomena.

Examining the Influence of Stress and Anxiety on Visual Working Memory and Decision-Making

Grammaticalization is the process by which a lexical item (e.g., noun) acquires a more functional role (e.g., preposition) over time. Grammaticalization is considered largely unidirectional, that is, change from functional to lexical is far less common (Hopper & Traugott, 2003). What is the cause of this unidirectionality? Our experiment tests whether individuals have a preference in the direction of grammaticalization when performing semantic extension in communication. We focus on the phenomenon of using body part nouns as a source of spatial prepositions. We predicted that participants extending body parts to use as prepositions would find the task easier and more intuitive than participants extending prepositions to use as body parts. However, our results show no directional bias, indicating that the historical unidirectional tendency for body parts to be used as spatial relations does not originate in a bias that individuals have for using one to refer to the other.

No directional preference for grammaticalization in semantic extension game

Primates’ social interactions are grounded in temporal and spatial
relationships, with physical proximity commonly used to assess
affiliation, dominance, and tolerance. Yet proximity is often treated
as a static, categorical measure rather than a dynamic, continuous
process. Here, we combine computer vision and environmental
markers to precisely quantify short-range social distances in two
groups of Japanese macaques housed in large outdoor enclosures.
Our social tolerance test results show that, when entering a
food-baited circle, macaques positioned themselves at
greater-than-chance distances from conspecifics, particularly to
dominants. Furthermore, lower-ranking individuals tended to
follow more indirect paths before approaching the food resource,
suggesting they weigh social risks alongside physical positioning.
By treating social proximity as a dynamic process, our study
provides new insights into how primates navigate social and
physical environments. This illustrates the potential of our method
for more nuanced measures of group organization, tolerance, and
decision-making.

Socially Situated Navigation: Social Rank and Sex Influence Spatial Navigation Strategies in Japanese Macaques

Expectations, or the prior probability of a choice outcome, are powerful sources of evidence for improving decision making under uncertainty. Most expectations in the real world are learned implicitly on the basis of statistical properties of observers’ environments. However, most studies investigating effects of expectations on perceptual decisions explicitly instruct observers on prior probabilities within the experiment, and thus fail to capture the experience-dependent uncertainty of real-world expectation learning. Here, we report data from a novel expectation-guided perceptual decision making task specifically designed to address this gap. Human observers (n=21) learned, through experience, probabilistic relationships between cues and images. Then, they explicitly reported both an estimate of each cue’s prediction and a confidence rating in that estimate before performing a cued perceptual decision task. We find that, although these measurements are highly correlated, confidence in an explicit report is the primary factor that interacts with implicit expectations to shape perceptual decisions.

Investigating implicit and explicit expectations in perceptual decision making

Statistical learning (SL) mechanisms drive learning across several domains, including language acquisition. Can SL mechanisms like cross-situational word learning, which rely on the accumulation of statistical evidence, also account for the rapid acceleration in children’s word learning? This study examined whether learners could track and integrate several statistical regularities concurrently and use this information to map words to objects that never co-occurred during training—a behavior known as zero-shot learning. Experiment 1 showed that learners leveraged their acquired knowledge of word and object categories to map words to objects that did not co-occur during training. Experiment 2 extended this finding by demonstrating that learners integrated their newly acquired statistics with prior knowledge to map novel words to perceptually similar, yet previously unseen, objects. These findings suggest that integrating several types of statistical relationships between words and objects can accelerate new learning, making SL an efficient mechanism for early word learning.

Downloads

Next from CogSci 2025

Modeling Processing Speed in Developmental Language Disorder using Drift Diffusion Modeling

Similar lecture

Reinforcement Learning Approach to Speed-Overmatched Maritime Targets with Uncertain Information

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from CogSci 2025

Modeling Processing Speed in Developmental Language Disorder using Drift Diffusion Modeling

Similar lecture

Reinforcement Learning Approach to Speed-Overmatched Maritime Targets with Uncertain Information

Downloads