Canada

Autism Spectrum Disorder (ASD) is a neurological development condition that affects approximately 1 in every 54 children and youth in Canada. Individuals diagnosed with ASD may experience challenges in communication and behaviour, which may vary depending on the individual. Often, such challenges are coupled with comorbidities like mental health conditions, which are often more prominent in individuals on the spectrum. As such, the detection of early signs of mental health issues and the proper diagnosis of ASD is crucial. This project explores the overlap between ASD and mental health conditions by utilizing a mixed-methods analysis. By analyzing survey-based and omics data on ASD, the authors hope to conduct a comprehensive examination of Autism Spectrum Disorder from both a psychological and biological perspective. For the first part of this project, questionnaire-based datasets from Thabtah F. et al. on individuals sharing ASD traits were retrieved for analysis. The datasets consist of 10 questions related to ASD traits and demographic information across developmental stages. The concurrence of ASD and mental health conditions will be analyzed through machine learning, data analysis, and visualization. Subsequent stages will involve omics data to deepen our understanding of ASD.

Leveraging Open Data Analytics and Machine Learning to Improve Mental Health Research and Innovation

Exploring the Intersection of Autism Spectrum Disorder and Mental Health Through a Mixed-Method Analysis

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**
<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/cble/" seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>


**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

The University Big Data Challenge (IUBDC) successfully concluded its latest edition, bringing together national health industry leaders, policymakers, and dignitaries for a conference centered on mental health research and innovation. Participants had the opportunity to showcase their projects in a poster session, engaging with representatives from industry and academia. The event provided a rich network of industry and academic professionals, offering pathways for scholarly publication, access to curated datasets, learning materials materials, and workshops.

Held at the Microsoft Headquarters in Toronto on July 26, 2024, the challenge featured hands-on data science and AI research projects focused on mental health. For over seven years, IUBDC has fostered collaboration between data science and health professionals and motivated university students. This year’s projects, which spanned topics from dementia to addictions, aimed to enhance our understanding of mental health, improve patient lifestyles, and reduce disease risk. Abstracts from teams were published in the NRC Research Press STEM Fellowship Journal, with all team's works featured by Underline publications.

2024 Inter-University Big Data And AI Challenge -

Thee challenge featured hands-on data science and AI research projects focused on mental health. 

Substance treatment programs are essential for addressing substance addiction issues. Several considerations may explain why completion rates are lower than predicted. Previous studies have emphasized the impact of demographic and socioeconomic factors on treatment outcomes. However, there is insufficient research on utilizing advanced machine learning algorithms to predict adherence and discover trends in large datasets, particularly considering mental health concerns. The dataset was obtained from SAMHSA which contains extensive information on demographics, geography, education, employment, drug history, mental health status, and treatment outcomes. The study uses sophisticated machine learning algorithms to predict adherence to drug therapies, employing Random Forest and XGBoost models to identify significant characteristics influencing patient treatment completion. These models are quite accurate and could improve treatment outcomes through personalized methods. We investigated Random Forest and XGBoost models using cross-validation and several metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The Random Forest model achieved an accuracy of 85%, with a precision of 80%, recall of 82%, an F1-score of 81%, and an AUC-ROC score of 0.88. The XGBoost model performed even better, with an accuracy of 87%, precision of 82%, recall of 84%, an F1-score of 83%, and an AUC-ROC score of 0.90. Key predictors of treatment adherence included age, marital status, employment status, mental health status, and the number of previous treatment episodes. We also observed significant disparities in adherence rates among ethnic minorities and individuals with lower educational levels. The research shows that machine learning algorithms may accurately predict adherence to drug treatment programs. The identified predictors provide useful information for creating focused, individualized treatment methods. Future studies should use longitudinal data to evaluate post-treatment results and investigate other socioeconomic and psychological aspects. Healthcare practitioners may employ machine learning to enhance intervention efficacy, improve patient outcomes, and reduce the social impact of substance misuse.


Improving Prediction Adherence to Mental Health Treatment Programs Using Machine Learning Algorithms

The Internet provides a platform for individuals to share their thoughts and interact with others, enabling a dynamic exchange of information. In fact, one's online activities can reflect one's mental state, moreover, it may even influence other's mental state by their digital interactions. In this paper, we aim to leverage machine learning to monitor societal mental health by tracking users’ online activities. Using social media as a case study, we employ sentiment analysis to evaluate comments and assess users' online behaviors. Our analysis focuses on the frequent words used, the tone of the comments, and their impact on the audience. The NLP model is designed to monitor societal mental health trend by examining the correlation between online speech and mental health. This enables us to intervene in the content that negatively impacts social mental health, fostering constructive discussions and solutions. Our primary objective is to see a strong connection between hate speech and mental health, demonstrating its influence on the targeted individuals and the surrounding audience. However, our model has limitations. Social media users, particularly those on platforms like YouTube, represent only a small fraction of the overall internet population. Additionally, our model is designed to analyze English-language content, which excludes non-English-speaking internet users.

Social Media: The Influencer of Mental Health

We utilized the Behavioral Risk Factor Surveillance System (BRFSS) dataset for the following observational machine learning (ML) study. In contrast to recent literature using ML to predict specific outcomes, we used causal machine learning (CML) algorithms (LRSRegressors, DRLearner, Rlearner, Xregressor, and Tregressor) to quantify the cause-and-effect relationships between adverse childhood experiences (ACEs) and two different mental health targets (number of bad mental health days and whether the respondent had a depressive disorder). Specifically, we report each ACE’s average treatment effect (ATE) on each target. We also used Uplift Random Forest trees to calculate uplift scores. We found that growing up in a household where one parent had a depressive disorder and being forced into unwanted sex had the most significant effects on our chosen mental health targets. Growing up in a household where one parent had a depressive disorder increased the likelihood of being diagnosed with a depressive disorder by 30% and added 5.07 days of bad mental health in the last month. Repeatedly being forced to have sex added 5.77 days of poor mental health and increased the likelihood of being diagnosed with a depressive disorder by 33%. Uplift modeling indicated that individuals over 35 years of age earning under $100,000 USD annually were most susceptible to the effects of a parent diagnosed with depression by a factor of 1.1 compared to the average population. Male college graduates over 44 years of age earning under $100,000 USD were found to be most susceptible to the effects of childhood sexual abuse by a factor of 1.38. Future research should refine these models and employ more complicated algorithms to gain accurate, interpretable measurements and to understand the relationship between ACEs and mental health.

Quantifying Childhood Trauma: Causal Machine Learning Approaches to Mental Health Outcomes

The increasing prevalence of mental health disorders necessitates innovative approaches to early detection and intervention. Social media platforms like Reddit provide a rich source of textual data that can be leveraged to identify mental health issues based on user-generated content. This study aims to classify mental health disorders by analyzing Reddit comments. The dataset used is Reddit SuicideWatch and Mental Health Collection that includes 54,412 posts, which are classified into several mental health disorders. After that, preprocessing steps were done, which includes tokenization, padding, word embeddings, and handling imbalance data. Then, a Custom-CNN (Convolutional Neural Network) and a Custom- RNN (Recurrent Neural Network) were used to classify the data. These evaluation metrics were then used to evaluate the models: accuracy, precision, recall, F1-score, and ROC AUC Score. Notable results are that the RNN has a higher accuracy of 0.65 compared to 0.64 by CNN. In addition, both RNN and CNN also have high ROC AUC scores of 0.88 and 0.87 respectively. In conclusion, the results show that the models are good at differentiating one class from another, however, both models have problems accurately classifying the correct mental health disorder from the texts. This shows promise as the models can definitely be improved with better and more balanced data, and exploration on LLMs (Large Language Models) can also be valuable for this study and issue. In addition to that, those improvements can be applied to the act of intervention by integrating it to technology such as a conversational AI or wearable actuators that have the ability to predict and prevent through real-time monitoring.

Utilizing Deep Learning Techniques for Mental Disorder Prediction and Support Using Reddit Data

The creation and application of a machine learning model to categorise an individual’s degree of depression is presented in this research. The model predicts the depression state (no depression, mild depression, moderate depression, severe depression) based on other characteristics using the Depres- sion.csv dataset which comprises 16 features. Preprocessing the data, scaling the features, and applying strong machine learning methods—such as Support Vector Classifier (SVC), Random Forest, K-Nearest Neighbors, and Gradient Boosting—are all part of the research. To maximise the model’s performance, hyperparameter adjustment is also carried out. The model’s ability to accurately categorise depression levels is demonstrated by the results which may improve mental health support and monitoring.
Index Terms—Machine learning, support vector classifiers, random forests, gradient boosting, K-nearest neighbours, hyper- parameter tuning, mental health monitoring, depression prediction.			


Prediction of Level of Depression of Individuals  Using Machine Learning

Depression and suicidal thoughts are significant mental health concerns frequently expressed on social media platforms like Reddit. This study uses advanced machine learning techniques to analyze and classify posts from the Reddit dataset to identify and categorize high-risk content. By employing Latent Dirichlet Allocation (LDA) for topic modeling, we identify common themes in the posts, such as expressions of despair and cries for help [1]. We then apply clustering algorithms to group similar posts, uncovering underlying patterns and providing insights into frequent triggers and shared experiences [2]. We use classification models to accurately categorize posts and detect those indicating severe distress, including Logistic Regression, Support Vector Machines (SVM), and Neural Networks [3]. This methodology is implemented in a web application designed to provide real-time categorization of new posts, aiding mental health professionals and social media moderators in quickly identifying and responding to high-risk content. The application invites users to interact, processing their inputs via a text box. Visualization tools, such as 2D and 3D scatter plots, clearly display the data, allowing users to understand how posts are categorized and fit into broader trends [4]. This research improves our understanding of how mental health issues are expressed on social media, highlighting the importance of advanced data analytics in creating practical intervention tools. Our approach demonstrates the potential of combining psychological assessment with sophisticated computational methods to develop a comprehensive system that benefits research and individual users.

Advanced Machine Learning Techniques for Early Detection and Classification of Depression and Suicidal Tendencies on Social Media

Mental health issues are increasingly being addressed through machine learning techniques, which offer new possibilities for analyzing and understanding psychological conditions. This study focuses on detecting depression severity using advanced natural language processing (NLP) and sentiment analysis algorithms. Leveraging the capabilities of machine learning, we aim to provide a robust framework for assessing mental health by analyzing social media data. Our approach involves the use of three distinct models: EMOTION ENGLISH DISTILROBERTA-BASE for extracting emotional features, MULTIWD for assessing various dimensions of wellness, and Vader for sentiment analysis. These models generate a comprehensive feature set, which is then used to compute depression severity, categorized into five levels—Minimal, Mild, Moderate, Moderately Severe, and Severe—based on the PHQ-9 questionnaire. To evaluate the effectiveness of our approach, we compared the calculated severity classes with results from Latent Dirichlet Allocation (LDA) for topic modeling and K-Means clustering for numerical feature-based classification. Our findings indicate that K-Means clustering achieved a 76% accuracy in aligning severity classes, which surpasses the LDA accuracy 63%. This suggests that numerical features may offer more reliable clustering for mental health severity than topic-based methods. This study highlights the potential of combining NLP and sentiment analysis techniques in mental health assessments and suggests directions for further refinement and research.


Model Based Severity Analysis of Mental Health Issues Using NLP and Sentiment Analysis On Social Media Data (SENTI-SEV)

Depression is popularly known as major depressive disorder (MDD), which is a common but serious medical sickness that negatively affects how an individual feels, the way they think, and possibly how they act. It potentially leads to many emotional and physical breakdowns and can reduce a person’s capability to perform at work as well as at home. The project employs a robust methodology to analyze the relationships between depression and various risk factors using the National Health and Nutrition Examination Survey (NHANES) dataset. The project involves data acquisition and preprocessing, depression score calculation using the PHQ-9 questionnaire, and technical infrastructure setup. Data aggregation, preprocessing, and exploratory data analysis (EDA) are performed using Python and its libraries. Hypotheses are formulated and tested using statistical methods, and machine learning techniques are applied for predictive modeling. Feature importance and hyperparameter tuning are used to improve the models.The project identifies key factors associated with depression, including socioeconomic features, alcohol and drug consumption, and mental health measures. Predictive models are being developed to predict depression levels based on multiple variables. A web interface with interactive dashboards is being designed to visualize key insights and model predictions. The findings have the potential to inform and shape public health policies and interventions. Also, successfully identifying and analyzing these complex associations will provide better insights into the whole picture of mental health treatment, particularly depression, which ultimately improves the effectiveness of public health strategies and clinical practices. Every finding in this research project might be beneficial not only to patients with depression but also to the academic community and public health policymakers.

Deciphering Depression: A Multivariate Analysis of Influential Factors and Their Relationships

Depression is a widespread mental health disorder affecting millions worldwide. In the age of social media, it is crucial to leverage technology to detect, help, and support individuals displaying symptoms online. Our investigation revealed that social media, particularly Reddit, has a wide demographic of people and hosts a variety of discussion topics, including a significant number of posts on mental health. Using the large amounts of data on Reddit, the goal is to train and develop various machine learning models to recognize manifestations of depression and evaluating their performance. These models include machine learning and deep learning methods, such as Support Vector Machines (SVMs), transformers, and various Recurrent Neural Network (RNN) architectures — simple RNNs, Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) units — for sentiment analysis and depression detection on Reddit. The objective is to evaluate and enhance the models’ accuracy. Early detection facilitated by such models could potentially mitigate the impact of mental health disorders through timely intervention and support mechanisms.

Using Machine Learning and Deep Learning for Depression Detection on Social Media

Anxiety disorder (AD) is a condition associated with feelings of dread and an incapability to perform daily tasks. Symptoms can be physical, such as a rapid pulse and sweating, and emotional, including constant stress and difficulty concentrating. Anxiety disorders are subjective, variable and often overlap with underlying conditions, complicating detection and treatment. This study focuses on three factors, implicit memory, reaction time, and reaction to a perceived threat—all measured in previous studies and correlated with anxiety levels. Our goal is to determine if anxiety presence can be identified, using three games and one simulation. The first game centers around implicit memory, enabling us to perform tasks subconsciously. Anxious individuals exhibit an implicit memory bias for threat-related information. The second game will associate a level of anxiety to reaction times. The third game first makes the user complete a regular task and progressively introduces a threat to the successful completion of the task. Attentional bias shows that anxious people are more likely to respond to threat-related stimuli rather than stick to their task. Finally, the user will take part in an artificial anxious situation. In the first stage, the user plays a regular game. In the second, distracting stimuli appear. In the third, the user’s available resources decrease. Throughout the stages, both the user’s effectiveness and efficiency will be evaluated. If the user experiences anxiety, efficiency should decrease in stage 2 and effectiveness should decrease in stage 3. Based on their results, they will receive an assessment of their optimal stress level for highest performance. The results from all of three games will be compiled to provide an anxiety assessment. The player will then be prompted to complete the Hamilton Anxiety Rating Scale (HAM-A) which will be used to verify whether the game results match their true anxiety levels.


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