Canada

Overlap-based drug and compound prioritization pipelines are attractive because they are scalable, transparent, and easy to apply across large drug-disease search spaces. However, they are also vulnerable to annotation-breadth effects, whereby broadly annotated genes can inflate apparent support across many disease contexts. We developed a bias-aware computational framework for candidate prioritization in Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis (ALS). The framework combines a normalized raw overlap score, annotation-breadth correction using gene-specificity weights, and a matched-null permutation layer applied to a selected high-priority subset. Across 62,887 overlap-positive pairs, correction substantially changed rank order and reduced the contribution of broadly annotated genes. However, exploratory downstream alignment with ClinicalTrials.gov remained weak, and a small curated therapeutic-history benchmark showed mixed recovery of known positives. These findings support the framework as a transparent hypothesis-generation and triage tool, but not as a predictor of clinical efficacy.

Equitable Health Technologies: Overcoming Biases and False Inferences in AI Diagnostics and Drug Discovery

A Bias-Aware Computational Framework for Drug and Compound Prioritization across Neurodegenerative Diseases

computational framework

neurodegenerative disease

annotation-breadth adjustment

drug and compound prioritization

hypothesis generation

The 46th Annual Meeting of the Cognitive Science Society was an in-person meeting held in Rotterdam, The Netherlands at the Postillion Hotel & Conference Centre.

**ON-DEMAND PROGRAM ACCESS AFTER THE CONFERENCE** 
Recordings of the invited program are now available. You can access them by clicking on the 'Schedule' icon on the left. Select view by week and navigate to the day and time slot of the recording you wish to view. Click on the time slot to access the recording.

Recordings available (click on each recording to view):

**Thursday, July 25** 
0900: [Keynote Speaker Morgan Barense on Enhancing real-world event memory](https://underline.io/events/465/sessions/17997/lecture/99084-dynamics-between-minds-and-the-environment) 
1000: [Gleitman Award Winner Isabelle Dautriche on Language Foundations: Insights from acquisition, communication, cognition, and more](https://underline.io/events/465/sessions/18000/lecture/99085-gleitman-talk) 
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture) 
1700: [Rumelhart Prize Presentation Speaker Alison Gopnik on Exploit, explore, empower: Three ages and three intelligences](https://underline.io/events/465/sessions/18028/lecture/99161-rp1-rumelhart-prize-presentation) 

**Friday, July 26** 
0900: [C.L. de Carvalho-Heineken Prize Keynote Speaker Kia Nobre on Focusing in memory](https://underline.io/events/465/sessions/18035/lecture/99162-hp1-c-l-de-carvalho-heineken-prize-keynote-address) 
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration) 
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture) 
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture) 
1745: [Keynote Speaker Andrea E. Martin on Neural dynamics encode the structure and statistics of language](https://underline.io/events/465/sessions/18077/lecture/99271-neural-dynamics-encode-the-structure-and-statistics-of-language) 

**Saturday, July 27** 
0900: [Keynote Speaker Gregor Schöner on How higher cognition emerges from the dynamics of strongly interacting neural populations](https://underline.io/events/465/sessions/18083/lecture/99273-dynamics-within-the-mind) 
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture) 
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture) 


#### **CogSci 2024 Program Book**
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**The Program Book can be downloaded [here](https://drive.google.com/file/d/1yRVp1cBqRnAbdYi4GXWV_pZeyWNsRquv/view?usp=sharing).**

#### **Letter from the President**

Welcome to the 2024 Meeting of the Cognitive Science Society in Rotterdam! CogSci 2024 brings together a large community of cognitive scientists who have traveled here from around the world, as well as a smaller group of remote presenters. I would like to extend an especially warm welcome to our first-time CogSci meeting attendees, and hope that 
their experience inspires them to join our future meetings for many years to come.

I want to thank this year’s conference co-chairs, Larissa K Samuelson, Stefan Frank, Mariya Toneva, Allyson Mackey, and Eliot Hazeltine, for putting together an amazing series of invited talks and symposia around the theme of Dynamics of Cognition. The co-chairs also deserve credit for handling a record-breaking number of submissions and producing a truly exciting conference program 

Beyond the invited talks and symposia dedicated to this year’s conference theme, I would like to highlight the presentations and events honoring the 
Rumelhart, Elman, Gleitman and C .L . de Carvalho Heineken prize winners, including the Rumelhart reception on Thursday. As you may know, we are recording all of the invited talks and symposia, and all prize-winners’ talks, and will be making this content available after the conference. 

The Cognitive Science Society relies on the volunteer efforts of our Governing Board members who work on matters related to our membership; conference policies; diversity and inclusion, international, and outreach initiatives; prizes and much more. We welcome interest from Society members who would like to become more involved in what we do. The 
Society is deeply grateful to Rick Dale and Andrea Bender, who serve as editors of the two Society journals, Cognitive Science and Topics in Cognitive Science respectively. We especially acknowledge our editors’ generosity in donating their stipends each year to D&I and outreach efforts. 

This year’s Annual Meeting depends critically on the support of Marischal De Armond and his team at Podium Conference Specialists: Sharon Zwack, Cendrine De Vis, Sarah-Kate Burke, and Rachel Waller worked tirelessly to secure the venue and handled a multitude of logistical and organizational issues, from arranging coffee breaks and receptions to making sure that a huge number of posters fits comfortably into the available space. Finally, and importantly, I would like to acknowledge the Cognitive Science Society Executive Officer, Erica Wojcik, for managing the complex and ever expanding sphere of our Society’s activities.

I hope you enjoy this year’s meeting and the many different opportunities to engage with the vibrant community of cognitive scientists gathered here. I also hope you get a chance to explore the great city of Rotterdam with its many attractions, museums, restaurants and scenic views. If you’d like to talk more about our Society, or simply want to say hello, please stop by!

![](https://assets.underline.io/markdown_image/1/image/3c66d0e21fff30e204df1138ffd15173.jpeg)

**Anna Papafragou** 
President, Cognitive Science 
Society 2023-2024

**CODE OF CONDUCT** 
By attending the CogSci 2024 Conference, you are required to adhere to the society’s [Code of Conduct](https://drive.google.com/file/d/16-6KkptF0Gn3ZYGJDlpqpwTdImw45Ng0/view?usp=drive_link).

**ABOUT THE COGNITIVE SCIENCE SOCIETY** 
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.

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CogSci 2024

The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

technical paper

As AI transforms healthcare, ensuring reliability is critical. However, systems learning from existing data often reflect gaps or inconsistencies, leading to false inferences, diagnostic errors, and unequal performance across patient groups.

Open Data is pivotal for transparency and accountability. Biases emerging from unrepresentative datasets or flawed assumptions can perpetuate health disparities.

Leveraging public data on genomics, clinical trials, and social determinants allows researchers to audit and mitigate these risks effectively.

We task undergraduate and graduate students to harness data science to:

* Detect, reduce, and prevent bias in diagnostics and drug discovery;

* Develop data-driven tools that increase fairness and inclusivity;

* Innovate bias-aware model evaluation techniques to build trustworthy AI systems.

As AI transforms healthcare, ensuring reliability is critical. However, systems learning from existing data often reflect gaps or inconsistencies, leading to false inferences, diagnostic errors, and unequal performance across patient groups.

Artificial intelligence (AI) frameworks combining 3D Computed Tomography (CT) scans with patient metadata hold promise for advancing lung cancer staging. However, these systems face vulnerabilities from shortcut learning, where models exploit superficial data correlations rather than authentic biological features. This study evaluates and quantifies the structural and optimization risks of metadata-driven algorithmic bias during the multi-class classification of primary lung tumor stages (T1 to T4). Utilizing a multi-institutional dataset of 354 patients, we constructed and compared two parallel neural network frameworks: a unimodal 3D EfficientNet-B3 image-only baseline and a multimodal late-fusion network (FusioNet-B3) that combined 3D chest CT tensors with tabular clinical metadata, including smoking history. Across experimental training runs, both architectures failed to generalize beyond the dataset’s majority class distribution (~42% validation accuracy). Notably, under an accelerated learning rate (η = 1x10-4), the multimodal network experienced complete feature suppression. It bypassed high-dimensional visual anatomy entirely, utilizing the binary smoking history token as an algorithmic shortcut to minimize mathematical loss. This resulted in a zero-variance, frozen validation accuracy flatline for 199 consecutive epochs. These findings provide empirical proof that naive integration of highly correlated clinical priors creates competitive optimization dynamics that can render image-processing branches obsolete. This failure underscores that small cohort sizes and severe class imbalances force networks to rely on demographic biases rather than genuine oncological features. To ensure robust, safe, and equitable clinical deployment, future multimodal architectures must shift away from basic concatenation toward explicit debiasing frameworks like adversarial training to force model grounding in true visual pathology.

Shortcut Learning Risks in Multimodal Deep Learning for Lung Cancer Staging

Clinical artificial intelligence models trained on imbalanced health data systematically underperform for minority subpopulations, raising concerns about whether synthetic data genuinely improve fairness or merely hide underlying bias. This study investigates whether synthetic data augmentation via the Synthetic Minority Oversampling Technique (SMOTE) and Bayesian hierarchical modelling produce meaningful fairness improvements in a diabetes prediction task using the National Health and Nutrition Examination Survey (NHANES) 2019–2020 and 2021–2023 cycles. Three models are developed: a logistic regression baseline trained on imbalanced data, a SMOTE- augmented logistic regression, and a Bayesian hierarchical GLM with per-race intercepts estimated via MCMC sampling. All models are assessed on AUC, F1, precision, and recall stratified by race/ethnicity and sex, with SHAP explainability analyses applied to both logistic regression models and posterior convergence diagnostics reported for the Bayesian model. The baseline model achieves an overall AUC of 0.897 but a recall of 0.524. SMOTE increases recall across all subgroups (overall recall 0.749) but produces asymmetric precision losses and fails to improve AUC, suggesting the model is more willing to predict the positive class, rather than being more accurate. The Bayesian hierarchical model achieves the most balanced precision- recall tradeoff for several subgroups, while posterior diagnostics confirm well-converged chains and modest but non-negligible between-group variance (sigma_alpha ≈ 0.2). This suggests that SMOTE improves sensitivity at the cost of precision without reducing subgroup discrimination gaps, while Bayesian hierarchical modelling helps by estimating uncertainty and capturing explicit group-level differences. Subgroup- stratified evaluation is essential for honest clinical AI assessment.

Does Synthetic Data Fix or Hide Bias?

Breast cancer detection relies heavily on mammographic imaging, but dense tissue can make malignant lesions harder to identify as both dense tissue and tumours appear radiographically similar. With increasing use of AI-based diagnostic tools in medical imaging, it is important to determine whether these models are affected by the same limitations that challenge human interpretation. The purpose of this research is to emphasize the need to train AI models on a more representative dataset that includes high-noise samples to ensure equitable care across demographics. This study investigates whether variability in breast tissue density acts as a confounding factor in AI-driven tumour detection thus leading to diagnostic inaccuracies. Mammographic images from the CBIS-DDSM dataset were analyzed using Mammo-CLIP, a pretrained model for mammography-based classification. Cases were grouped by BI-RADS breast density categories, and model performance was evaluated using sensitivity, specificity, accuracy, FNR, and chi-squared testing between fatty and dense breast tissue groups. Model sensitivity decreased from 64% to 34% and FNR increased from 36.1% to 66.1% with increasing breast density. Classification accuracy was significantly higher in fatty tissue than dense tissue, and cancer detection amongst malignant cases was also significantly reduced in dense tissue. These findings suggest that denser tissue negatively influences Mammo-CLIP’s ability to detect malignant lesions and increases likelihood of missed cancers. This has important clinical implications, as false negatives may delay diagnosis and treatment, particularly for patients with dense breast tissue.

Effect of breast tissue density variability on AI-based tumor detection

Healthcare systems generate vast quantities of clinical data, yet much of this information remains locked within unstructured free-text records, including physician notes and doctor-patient transcripts, making it inaccessible for large-scale analysis. Manually extracting structured information from this data is time-consuming, expensive, and error-prone. Large language models (LLMs) have demonstrated promising performance in a variety of medical fields, including radiology report summary, doctor-patient dialogue transcription, clinical question answering, and diagnostic reasoning. This study evaluates four LLMs (GPT-5.4 mini, Qwen3 32B, Llama 3.1 8B, and Gemini 3.1 Flash-Lite Preview) on their ability to label radiology diagnostic conclusions and clinical assessment summaries from unstructured medical text using MedVAL-Bench, a 2025 Stanford MIMI benchmark with physician-validated reference outputs. The models were tested on two tasks: doctor-patient dialogue summarization (dialogue2note, n=85) and radiology report impression extraction (report2impression, n=190), which were assessed using ROUGE-1, BERTScore F1, and BLEU. The results suggest that proprietary models like GPT-5.4 mini outperformed all models with a ROUGE-1 of 0.376 and BERTScore F1 of 0.860, while Qwen3 32B, Llama 3.1 8B, and Gemini 3.1 Flash-Lite Preview produced comparable results. ROUGE-1 and BLEU scores were consistently low across all models, reflecting the limitations of n-gram metrics in clinical contexts, where a single-word difference can carry critical medical significance. BERTScore F1 emerged as the most reliable indicator of semantic similarity. These findings suggest LLMs can significantly automate clinical text labelling, offering a scalable, low-cost alternative to manual data extraction in healthcare research.

A Comparative Analysis of LLM-Assisted Labelling of Unstructured Clinical Data

General-purpose artificial intelligence chatbots are increasingly used for health information, despite not being validated as medical devices. In 2026, ECRI ranked misuse of AI chatbots as the leading health technology hazard, noting that human-sounding responses can mislead users when information affects patient care. Dermatology is a particularly important domain for AI safety because patients can easily photograph visible skin concerns and seek advice before entering formal care. Although specialized dermatology models such as SkinGPT-4 demonstrate the feasibility of image-based AI analysis, widely available general-purpose tools such as ChatGPT, Claude, and Gemini may be more accessible to ordinary users. This paper proposes SAFER-Skin, a reproducible safety-audit framework for evaluating the off-label use of general-purpose multimodal chatbots when responding to consumer-style dermatology image queries. Rather than diagnostic accuracy, the framework is designed to evaluate safety behavior: whether chatbots provide appropriate referral advice, communicate uncertainty, avoid false reassurance, and refrain from prioritizing skincare products when medical evaluation may be needed. The proposed methodology uses publicly available dermatology image datasets including the Diverse Dermatology Images dataset and the Skin Condition Image Network, paired with neutral, reassurance/product-seeking, and safety-first prompt templates. Responses would be coded using a structured rubric with a primary safety classification and seven binary safety features, then stratified by model, prompt style, risk tier, dataset, and available skin-tone metadata. No empirical results are reported; this manuscript presents the proposed framework and protocol. SAFER-Skin is intended for AI safety researchers, health AI developers, and clinical governance teams. It does not validate AI for clinical diagnosis or replace dermatologists; it provides a practical method for identifying unsafe response patterns before off-label AI use in dermatology becomes entrenched.

SAFER-Skin: Auditing AI Skin Advice Across Skin Tones

Traumatic brain injury (TBI) is an important public health concern in Canada and is frequently associated with depression – although often underdiagnosed in this population. Large-scale population health datasets, like ones provided by Statistics Canada, typically rely on clinical diagnoses as outcome variables. As these datasets are increasingly used to develop artificial intelligence (AI) diagnostic models, systematic disparities in diagnosis may become embedded within algorithmic systems – contributing to existing health inequities. This study examined the relationship between TBI and diagnosed mood disorders in Canada – exploring demographic differences that may influence diagnosis and healthcare representation. Data were obtained from the Canadian Community Health Survey (CCHS) 2017–2018 Public Use Microdata File. Descriptive analyses, chi-square tests, and multivariable logistic regression compared mood disorder prevalence between respondents reporting TBI and those reporting other injuries, controlling for sex, age, and immigrant status. The final sample included 15,884 respondents, of whom 611 reported a TBI. Mood disorders were more prevalent among TBI survivors (18.6%) than among those reporting other injuries (14.3%). TBI remained significantly associated with increased odds of reporting a mood disorder (OR = 1.33, p = .008) after adjustment for demographic variables. Women with TBI reported higher rates of mood disorders than men (22.8% vs. 13.3%), and non-immigrants demonstrated greater odds of diagnosis than immigrants. These findings suggest that depression remains a significant consequence of TBI, highlighting how demographic differences in diagnosis may influence datasets used to develop AI technologies; underscoring the importance of equitable data representation in future healthcare innovation.

Bias in Depression Diagnosis Following Traumatic Brain Injury: Implications for Equitable AI Diagnostics in Canada

Clinical trials provide the evidence used to guide treatment recommendations, regulatory decisions, and healthcare practice. As clinical evidence increasingly supports precision medicine and AI-assisted healthcare, the completeness and representativeness of clinical trial data have become important considerations. This study examined demographic representation and reporting practices across cardiovascular, respiratory, and diabetes/metabolic clinical trials identified through ClinicalTrials.gov. A cross-sectional analysis of 54 completed studies was conducted, including 18 cardiovascular studies, 18 respiratory studies, and 18 diabetes/metabolic studies. Data were collected on sex distribution, race and ethnicity reporting, enrollment, and study design. Descriptive analyses compared demographic representation and reporting across disease areas. Differences were observed across all three disease categories. Cardiovascular studies contained the most missing demographic information, with 11,997 participants lacking sex-specific reporting and 11,088 participants classified as Unknown/Not Specified for race or ethnicity. Respiratory studies generally demonstrated more complete demographic reporting, while diabetes/metabolic studies reported the highest number of female participants. Interventional studies comprised 45 of the 54 included studies. These findings suggest that demographic representation and reporting practices remain inconsistent across clinical research. Incomplete demographic reporting may contribute to evidence gaps that limit the interpretation and generalizability of clinical findings. As clinical trial datasets increasingly inform precision medicine, predictive analytics, and AI-assisted healthcare systems, improving demographic inclusion and reporting standards may strengthen the quality, reliability, and applicability of future healthcare evidence and technologies.

Demographic representation and reporting in clinical trials across major disease areas

Breast cancer screening relies heavily on detailed mammographic interpretation to detect and classify lesions. Advancements in multimodal artificial intelligence (AI) systems have demonstrated potential capabilities in cancer imaging analysis. However, the accuracy and reliability of AI interpretation of mammographic lesions remain understudied. This study evaluates the diagnostic accuracy of commercial AI models for detecting and classifying mammographic lesions, compared with physician annotations. Using breast cancer patient imaging data, from the CBIS-DDSM, hosted by the TCIA. Each image case was assessed on five diagnostic criteria: lesion presence, lesion count, shape, margin, and pathology, compared against expert radiologist annotations. Across 121 individual mammograms, Gemini achieved the highest overall diagnostic accuracy (> 60%), outperforming Claude and ChatGPT. All models demonstrated high correctness for simple features ; lesion presence (≥ 90%) and lesion count (85–95%), but exhibited marked declines in complex morphological characterization, with accuracy for margin and shape ranging from 5–30%. Pathology classification achieved moderate accuracy (50–60%), reflecting difficulty in integrating interdependent visual and semantic cues. These findings reveal that while LMMs can reliably detect lesions, their interpretive precision diminishes when constrained by BI‑RADS‑defined morphological taxonomy. To address these limitations, we developed TerraComrad, a prototype human‑in‑the‑loop web application that integrates clinician‑guided region selection to improve lesion characterisation. Overall, this study establishes a benchmark for evaluating general‑purpose multimodal AI in breast imaging, demonstrating potential for preliminary screening while emphasizing the necessity of expert oversight for complex diagnostic interpretation.

Performance of commercial large multimodal models in mammographic lesion interpretation

Cardiovascular diseases remain a leading cause of morbidity, making timely and accurate diagnosis essential for improving patient outcomes. However, sex-based disparities in the diagnosis of cardiovascular disease lead to increased risk of misdiagnosis, and subsequent undertreatment, in females. As artificial intelligence (AI), machine learning (ML), and large language models (LLMs) become increasingly integrated into clinical decision-making. It is important to determine whether these tools reduce or reproduce existing biases in existing medical data. This manuscript reviews evidence on sex-based bias in AI-assisted cardiovascular diagnosis. A two-phase search strategy was used to identify relevant literature from OMNI McMaster, Google Scholarly, PubMed, and Web of Science. Initial keywords produced fifty articles, which were screened using an inclusion and exclusion criteria related to human participants, clinical cardiovascular populations, AI or ML use, and sex-stratified evaluation. Fifteen articles were selected for detailed analysis. Across the reviewed literature, findings suggest sex-based disparities exist in both traditional clinical and AI-supported diagnosis. These findings highlight the need for further sex-stratified model evaluation, more representative data, and bias-mitigation strategies before AI tools are more widely implemented. Overall, AI has the potential to improve diagnosis, but without careful training and bias control, it may exaggerate existing gender-based biases.

Biases In LLMs for Clinical Diagnosis of General Cardiovascular Disease

Accurate demographic representation in clinical trials is crucial for data generalizability, yet reporting for specific ethnicities remains under-examined. Using a Python-based algorithm, this study evaluated the visibility of South Asian participants across 178 global ribociclib HER2-negative breast cancer trial records extracted from ClinicalTrials.gov. A novel, four-level rule-based framework, the South Asian Visibility (SAV) score, was developed to quantify reporting granularity, ranging from 0 (no demographic reporting) to 3 (explicit subgroup reporting). Natural language processing (NLP) evaluated target keyword frequencies, while non-parametric tests assessed relationships between study scale, design, and visibility. The analysis revealed a severe reporting deficit: 161 studies (90.4%) provided zero public demographic data (SAV score 0), whereas only six (3.4%) explicitly isolated South Asian subgroups (SAV score 3). Furthermore, sample size demonstrated a negligible, non-significant correlation with visibility scores (r_s = 0. 072, p = 0. 3491) , confirming that larger research scales do not inherently improve data transparency. NLP analysis supported this observation, yielding zero text mentions of "Asian" or "South Asian" despite frequent references to "race" and "ethnicity." Ultimately, reliance on broad, combined racial categories severely masks South Asian representation in clinical data. This reporting gap may limit the assessment of generalizability of oncological findings and threatens the equity of downstream data-driven technologies, such as precision oncology models. Mandating subgroup-level demographic reporting standards across global clinical registries is vital to achieving equitable health outcomes.

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