Canada

The present study assessed both implicit and explicit math self-concept, math-gender stereotype, math achievement, and math anxiety in 260 Swiss children from 1st, 3rd, and 5th grade. In line with prior research, explicit math self-concepts were found to decrease as a function of participant age cohort. Furthermore, already children in the first grade were found to implicitly endorse the math-gender stereotype. In line with these findings, math anxiety was found to be higher in older cohorts than in younger, and it was non-trivial in all age groups. Lastly, as expected, math achievement was higher for older children. Taken together, the present study found patterns of stereotypes increasing as a function of age, with simultaneous increases in math anxiety, and simultaneous decreases in math self-concept. These findings underline the importance of studying math-related constructs in children. Understanding these patterns will contribute to sustaining STEM interests in young children and adolescents.


CogSci 2022

Math Self-Concept, Stereotypes, Achievement and Anxiety – A Cross-Sectional Study

### 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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CogSci 2025

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

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Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
<br>[**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)
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This conference requires a registration to attend. Please use the link below to secure your ticket. The website will be available to all registered attendees on July 25th. 

[Register to Attend](https://cognitivesciencesociety.org/registration/)

Registration Is Required

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Quantifying the meaning of a word is a complex challenge. Humans can encode semantic information along a large and diverse range of semantic dimensions for any given word. Whilst a number of studies have applied a range of techniques to quantify word meaning along specific dimensions, little work has focussed on the socio-semantic dimensions of meaning. Here, we present data that quantifies the socio-semantic representations of 2,700 Czech words along the dimensions of gender, location, political, valence and age. We also demonstrate the utility of the data set by calculating an estimate of socio-semantic similarity between all words, which can be used to identify words that are either proximally close or distant in socio-semantic space.

Quantifying the Socio-semantic Representations of Words

Math anxiety (MA) is negatively related to math performance. One proposed intervention with potential to disrupt the MA-math performance link is expressive writing. The current study aimed to conceptually replicate Park and colleagues (2014). In that study, the authors concluded that expressive writing effectively boosted math anxious students’ performance. In our current sample of 168 college students, participants randomly assigned to the expressive writing condition were no more accurate at posttest than were other participants assigned to a math self-concept intervention, active control, or passive control. Additionally, participants in the math self-concept and active control conditions reported lower state MA immediately following the intervention; participants in the expressive writing and passive control conditions reported no differences between pretest and posttest state MA. The current study provides boundary conditions for the effectiveness of expressive writing interventions in ameliorating MA during difficult math tasks and illuminates potential mechanisms underlying MA.

Does Expressive Writing Blunt the Effects of Math Anxiety on Math Performance? A Conceptual Replication and Extension of Park et al. (2014)

Learning to write is characterized by bottom-up mimicking of characters and top-down writing from memory. We introduce a CNN-RNN model that implements both pathways: It can (i) directly write a letter by generating a motion trajectory given an image, (ii) first classify the character in the image and then determine its motion trajectory `from memory', or (iii) use a combination of both pathways. The results show that, in one-shot and few-shot learning, the model profits from different combinations of the pathways: The generation of different character variants works best when the top-down is supported by the bottom-up pathway. Refilling occluded images of efficiently learned characters works best when using the top-down pathway alone. Overall, the architecture implies that a weighted merge of bottom-up and top-down information into a latent, generative code fosters the development of compositional encodings, which can be reused in efficient learning tasks.

Efficient learning through compositionality in a CNN-RNN model consisting of a bottom-up and a top-down pathway

Intelligent agents are capable of transfer and generalization. This flexibility in adapting to new tasks and environments often relies on representation learning and replay. Among these algorithms, successor representation learning and memory replay offer biologically plausible solutions. However, replay prioritization algorithms remain largely limited to value prediction errors. Here we propose PARSR, Priority-Adjusted Replay for Successor Representations, to address this caveat. Decoupling learning of the environment dynamics and rewards, PARSR can use prediction errors from either representation learning or values to prioritize memory replay. We compare PARSR to SR-Dyna, Dyna-Q, and a number of state of the art algorithms using replay and successor representations in cognitive neuroscience. We find that PARSR is able to reproduce human behavior in a number of revaluation tasks while also representing a performance improvement over SR-Dyna, its closest counterpart.

Priority-Adjusted Replay for Successor Representations

This paper describes an initial cognitive framework that captures the reasoning involved in scientific data analyses, drawing from close collaborations with scientists in different domains over many years. The framework aims to automate data analysis for science. In doing so, existing large repositories of data could be continuously and systematically analyzed by machines, updating findings and potentially making new discoveries as new data becomes available. The framework consists of a cycle with six phases: formulating an investigation, initiating the investigation, getting data, analyzing data, aggregating results, and integrating findings. The paper also describes our implementation of this framework and illustrates it with examples from different science domains.

Towards Capturing Scientific Reasoning to Automate Data Analysis

The introduction of deep learning and CNNs to image recognition problems has led to state-of-the-art classification accuracy. However, CNNs exacerbate the issue of algorithm explainability due to deep learning’s black box nature. Numerous explainable AI (XAI) algorithms have been developed that provide developers insight into the operations of deep learning. We aim to make XAI explanations more user-centric by introducing modifications to existing XAI algorithms based on cognitive theory. The goal of this research is to yield intuitive XAI explanations that more closely resemble explanations given by experts in the domain of bird watching. Using an existing base XAI algorithm, we conducted two user studies with expert bird watchers and found that our novel averaged and contrasting XAI algorithms are significantly preferred over the base XAI algorithm for bird identification.

User-Centric Enhancements to Explainable AI Algorithms for Image Classification

We demonstrate how medical-image classification decisions can be denoised by aggregating decisions on similar images. In our algorithm, the final decision on a target image is cancerous if a percentage t of the k most similar images are cancerous, else it is not cancerous. Similarity between images is calculated as the distance between representations from an artificial neural network. We vary k and t for novice and expert participants using data from Trueblood et al. (2018) and Trueblood et al. (2021). We show that increasing k improves performance for novices, with their performance approaching that of experts. We also show that the algorithm is biased towards identifying cancerous cells, which is reflected in the representational space. The percentage t allows greater control over sensitivity and specificity and can be used to debias decisions. This algorithm is less effective for experts, partially explained by them giving similar responses on similar images.

Representational Smoothing to Improve Medical Image Decision Making

Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday language and how do we reason with them. A widely studied topic is whether cognitive theories can account for typical reasoning tasks and be confirmed by own empirical experiments. This paper takes a different view and we do not propose a theory, but instead take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning, which we call plausibility. For this purpose, we employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP). Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.

A Quantitative Symbolic Approach to Individual Human Reasoning

A central component of the predictive coding theoretical framework concerns the comparison between predictions and sensory decoding. In the probabilistic setting, this takes the form of assessing the similarity or distance between probability distributions. However, such similarity or distance measures are not associated with explicit probabilistic models, making their assumptions implicit. In this paper, we explore an original variation on probabilistic coherence variables; we define a probabilistic component, that we call a "Bayesian comparator", that mathematically yields a particular similarity measure. A geometrical analogy suggests two variants of this measure. We apply these similarity measures to simulate the comparison of known, predicted patterns to patterns from sensory decoding, first in a simple, illustrative model, and second, in a previous model of visual word recognition. Experimental results suggest that the variant that is scaled by the norms of both predicted and perceived probability distributions yields better robustness and more desirable dynamics.

Bayesian comparators: a probabilistic modeling tool for similarity evaluation between predicted and perceived patterns

The ability to infer general characteristics of populations from specific instances is critical for reasoning. While there is evidence of this capacity in infancy, prior work has not examined children’s ability to use these second-order inferences to make predictions about future outcomes. In the current study, 3-year-olds observed balls drawn at random from two containers. In one sample each ball was a different color. The other sample consisted of balls of only one (Experiment 1) or two (Experiment 2) colors. Children were asked which of the containers was more likely to contain a novel colored ball. A significant majority of children chose the more variable sample’s container. This suggests that 3-year-olds are not only able to make inferences about hidden populations from the variability of observed samples, but also use those inferences to reason beyond their direct experience.

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Quantifying the Socio-semantic Representations of Words

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