United States

This study examines the plasticity of color discrimination and the speed-accuracy trade-off in color discrimination using the 100-hue test. To prevent ceiling effects from task simplicity, an unprecedently short time limit of 75 seconds was implemented, a novelty of this study. Unlike conventional 100-hue tests, accuracy rate—rather than total error score—was used to assess color discrimination ability due to its preciseness. The results showed that practice enhances color discrimination accuracy by 40% to 59%, with significant improvements observed after only three trials. Additionally, longer response times correlate with higher color discrimination accuracy. These findings suggest that color vision is highly plastic, with color discrimination ability improving significantly after three practice trials, and that a speed-accuracy trade-off exists in color discrimination.

CogSci 2025

Plasticity and Speed-Accuracy Trade-Off in Color Discrimination: Insights from the 100-Hue Test

psychophysics

cognitive architectures

learning

psychology

perception

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

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.

Iconic words are those whose sounds share properties in common with their referents, such as “clatter” or “hiccup.” Research shows that children learn iconic words earlier than arbitrary words and that iconicity may help children form these connections. However, another factor to consider is that iconic words have forms that are easier to produce. To gain further insight into the link between iconicity and acquisition we studied homophones. This allowed us to hold the form of each word constant and examine whether iconic meanings are acquired earlier. Participants provided iconicity ratings on 1668 total meanings for 390 word forms. We ran a mixed effects linear regression and found an effect of iconicity on test-based age-of-acquisition, controlling for word form, length, frequency, phonological neighbourhood, and meaning-specific familiarity. These findings suggest that children learn iconic meanings earlier than arbitrary ones and support iconicity as an important factor in word-learning.

Iconic Meanings Are Learned Earlier: Homophones Provide Insight on Iconicity's Role in the Acquisition of Words

Computational models of purposeful behavior comprise both descriptive and prescriptive aspects, used respectively to ascertain and evaluate situations in the world. In reinforcement learning, prescriptive reward functions are assumed to depend on predefined and fixed descriptive state representations. Alternatively, these two aspects may emerge interdependently: goals can shape the acquired state representations and vice versa. Here, we present a computational framework for state representation learning in bounded agents, where descriptive and prescriptive aspects are coupled through the notion of goal-directed, or telic, states. We introduce the concept of telic-controllability to characterize the tradeoff between the granularity of a telic state representation and the policy complexity required to reach all telic states. We propose an algorithm for learning telic-controllable state representations, illustrating it using a simulated navigation task. Our framework highlights the role of deliberate ignorance -- knowing what to ignore -- for learning state representations that balance goal flexibility and cognitive complexity.

Learning telic-controllable state representations

In classifier languages, a sequence consisting of a noun (N), a numeral (Num), and a numeral classifier (CL) could in principle occur in one of six possible word orders. However, the cross-linguistic distribution of these word orders is highly uneven. Specifically, classifier languages tend to use Num-CL orders and, furthermore, N-medial orders are completely unattested in the world’s languages. We use an artificial language learning paradigm (Experiment 1) and a silent gesture paradigm (Experiment 2) to test the hypothesis that typological patterns arise from cognitive biases at the level of individual speakers. In contrast to studies that examined coarser grained ordering effects, our results do not align with typological preferences. We consider the possibility that cognitive biases might not play a role in “finer grained” ordering phenomena involving units such as classifiers, whose role in an utterance is more about grammatical well-formedness than a strong contribution to meaning.

Numbers and counting: Silent gesture and artificial language learning do not always reflect typological patterns

Formal models in social epistemology explore why rational agents might polarize. While paradigmatic models focus on binary topics, e.g., ”Is H true or false?”, many real-world issues involve multi-option topics: ”Which of n > 2 options is true/best?” This paper introduces a model of rational deliberation on multi-option topics to address the following question: As a group discusses more options, should we expect their beliefs to polarize more or less? We find a dual effect: as the number of options increases, agents are more likely to disagree on which option is most likely correct. This makes it harder to reach consensus on a single position. At the same time, their beliefs—and thus their disagreements—become less extreme. Hence, while agents are more likely to disagree, these disagreements are less intense. Since each trend aligns with a familiar concept of polarization, more options can increase or decrease polarization, depending on one’s measurement.

Multi-Option Polarization: How Deliberating More Options Both Increases and Decreases Polarization

The present study aimed to examine the relationship between rumination and two core executive functions, inhibition and switching, through an experimental design. Undergraduate participants (N=153) were randomly assigned to a rumination induction, a negative mood induction, or an abstract distraction control. Participants completed a task-switching paradigm before and after induction, providing inhibition and switch-cost indices. The Ruminative Response Scale (RRS) and the Leiden Index of Depression Sensitivity (LEIDS-R) were administered to measure trait rumination and cognitive reactivity, respectively. Significant three-way interactions were observed, with participants low in trait rumination and high in cognitive reactivity in the negative mood induction group demonstrating the least switching costs. Interestingly, no significant differences emerged among those with either high or low levels of both traits across all conditions. The study showed a complex picture between rumination and cognitive reactivity, suggesting that the impact of rumination on executive function performance depends on reactivity levels.

Investigating the Relationship Between Rumination and Executive Functions: The case of Inhibition and Switching

The prepositions “in” and “on” are used in a wide variety of concrete, spatial contexts as well as abstract, non-spatial contexts. Previous research suggests that the meanings of these prepositions when used in abstract, non-spatial contexts might be grounded in the meanings these same prepositions have when they are used in concrete, spatial contexts; however, few studies have attempted to empirically test for evidence of these connections directly. The current study attempted to conceptually replicate Feist and Breaux (2013): No evidence of priming was observed. It could be that our prime stimuli did not adequately activate the meanings of “in” and “on” or that our participants’ linguistic habits obscured any impact of the prime stimuli. Further research is needed to truly understand the role that grounded connections might play in the lexical semantics of words that are used in both abstract and spatial contexts.

Resolving the Ambiguity of “In” and “On” Across Spatial and Abstract Contexts

Large Language Models (LLMs) demonstrate high performance across various tasks, yet they struggle with those requiring complex comprehension and reasoning. LLMs are not solely reliant on memorization: responses can be generated to novel prompts by interpolating between learned data points in a continuous vector space. However, they exhibit limitations in their inherent reasoning capabilities.
Despite efforts to enhance their reasoning abilities, such as Chain-of-Thought prompting and test-time inference techniques, LLMs still face challenges in this domain. In contrast, humans utilize mental models—internal representations of situations and concepts—to adapt and solve novel situations.
Integrating external modules that emulate the construction and utilization of mental models could offer a promising avenue for enhancing the reasoning abilities of LLMs. This approach could bridge the gap between current LLM capabilities and human-like reasoning, potentially leading to more robust and reliable LLMs.

Beyond Interpolation: Enhancing Large Language Models (LLMs) with Mental Models

Mutual exclusivity (ME), the tendency to map novel words to unfamiliar referents, can support children’s word learning (e.g., Markman & Wachtel, 1988). A recent study (in prep) demonstrated that 4-year-old English-speaking children show a weaker ME effect for novel verbs than nouns, consistent with evidence that verbs are harder to learn (e.g., Gentner, 1982). Here, we replicated this study in adults. Adults viewed videos (verb trials) or static images (noun trials), one familiar and one unfamiliar, and selected the best match for a novel verb or noun. Adults applied ME for both verbs and nouns, but significantly less for verbs (z = 4.073, p = 0.0003). Adults also had longer reaction times (β = 471.25, p < 0.0001) and lower confidence ratings (β = -7.035, p < 0.0001) for verbs than nouns. Thus, less use of ME for verbs stems from something about event conceptualization rather than child development.

Mutual Exclusivity in Noun and Verb Learning in Adults

Emerging Morphosyntactic Prediction in Early Childhood: A Visual Tracking Study
Linguistic prediction, a key mechanism in language acquisition (Dell & Chang, 2014), enables anticipation of upcoming linguistic input based on contextual cues (Mani & Huettig, 2012; Angulo-Chavira et al., in review). This eye-tracking study investigated gender-based morphosyntactic prediction in Spanish-speaking toddlers aged 30 and 36 months. Participants heard highly constraining sentences (e.g., La gallina puso su... [The hen laid its…]) while viewing images of a gender-matching competitor (cuchillo [knife]), which shared only grammatical gender with the target (huevo [egg]), and a distractor (gorra [cap]). Results revealed that 36-month-olds, but not 30-month-olds, shifted their gaze toward the gender-matching competitor before hearing the target noun, indicating developmental differences in morphosyntactic processing. These findings suggest that the ability to integrate morphosyntactic cues into predictive language processing emerges by 36 months, providing empirical evidence on the developmental trajectory of linguistic prediction in early childhood.

Emerging Morphosyntactic Prediction in Early Childhood: A Visual Tracking Study

Human reasoning and decision-making under uncertainty often deviate from normative standards of rationality. Over the past decades, cognitive scientists have extensively investigated heuristics and cognitive biases, such as overconfidence—the tendency to overestimate the probability that one’s judgments are correct. Meanwhile, philosophers have explored different “cognitive utilities” that guide both scientific and everyday reasoning, including the concept of truthlikeness, i.e., how well an hypothesis, be it a statement or a numerical interval, approximates the whole truth about a target domain. In this paper, we integrate empirical findings with philosophical perspectives, showing how formal models of truthlikeness offer valuable insights for empirical research on overconfidence. In particular, by conceptualizing overconfidence through the lens of expected truthlikeness maximization, we argue that many instances of this phenomenon may be construed not as cognitive biases, but rather as rational strategies for approaching the truth under conditions of uncertainty.

Downloads

Next from CogSci 2025

Iconic Meanings Are Learned Earlier: Homophones Provide Insight on Iconicity's Role in the Acquisition of Words

Similar lecture

Color Perception: A Comparison between Computer and Human Color Labelling

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES