This workshop provides an overview of the essential components of sleep and its critical connection to mental health. Participants will explore how sleep impacts overall well-being and gain practical tips to improve sleep quality. The session will also offer additional resources for when basic strategies are not enough, empowering attendees to take steps toward more restful and restorative sleep.

Attending a conference is more than just sharing your research, it’s about the people you meet and the connections you build. Strong networks can lead to collaborations, mentorship, job opportunities, and help you feel part of a community in your field. Whether you’re attending your first conference or already have some experience, this session will share practical tips and strategies to help you network with confidence. Hear insights from fellow students, learn what works, and contribute your own experiences in an interactive discussion.

Today’s information landscape can feel increasingly overwhelming. With Google Scholar, ChatGPT, APA PsycInfo, Omni, PubMed, where does one begin and when does one stop? This pre-conference workshop introduces two interesting tools that can assist advanced researchers in discovering additional references and insight. Research Rabbit (https://www.researchrabbit.ai/) helps discover and map scholarly networks through visual connections between key papers while SciSpace (https://scispace.com/) helps understand, summarize and engage with academic articles. Participants will also see how both tools integrate with Zotero to support citation management and research workflows. The session will include a demonstration of both tools using practical examples and will share tips for incorporating them into information seeking and reading activities.

This workshop is intended for undergraduate or graduate students with little or no experience in writing and revising scientific articles. If you would like to deepen your understanding of the steps leading to the publication of research and improve your writing skills, this interactive workshop is designed for you! Throughout the workshop, the specific characteristics of different types of manuscripts (e.g., empirical and theoretical articles) will be presented. In addition, all the stages a scientific manuscript goes through, from initial drafting to publication, including the peer review process, will be explained in detail. Following this theoretical presentation, you will take part in a practical session where you will evaluate a manuscript and suggest revisions to improve it. This exercise will help you consolidate the concepts covered and apply them to a real article. Participating in this workshop will not only strengthen your scientific writing skills but also give you a clearer understanding of reviewers’ expectations and of the review process leading to publication

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depression, yet substantial variability in clinical response remains. Why do patients receiving the same intervention experience markedly different outcomes? This presentation will argue that treatment response is inherently multifactorial, shaped by the interaction of biological, lifestyle, and neurophysiological factors.
I will begin with a brief overview of rTMS for depression and introduce an ongoing clinical trial comparing unilateral and bilateral theta-burst stimulation (the UBD trial). Building on this context, I will then examine key determinants of treatment response.
Drawing on recent work from our group, I will highlight biological sex and baseline physical activity as important factors influencing outcomes. Emerging evidence suggests that these factors interact, potentially through differences in cortical anatomy, network organization, and hormone-related modulation of plasticity.
Finally, I will introduce TMS-EEG approaches to probe cortical excitability and inhibition, focusing on the N100 component as a candidate biomarker of response. Together, these findings support a shift toward precision neuromodulation, where stratification, dose-informed stimulation, and brain-based markers guide individualized treatment strategies.

The field of Artificial Intelligence in Education has a long history of building and evaluating Intelligent Tutoring Systems (ITSs), which are technologies that provide personalized guidance and scaffolding to students during instructional activities. Traditionally, these systems required substantial implementation effort from various stakeholders, including programmers and instructors. In contrast, large language models (LLMs) are general-purpose tools that can automatically generate explanations, hints, and solutions. This raises an important question: Do ITSs still have value?
Prior to the emergence of LLMs, we focused on designing and evaluating instructional interventions within ITSs that provided structured activities that aimed to encourage constructive reasoning. Our recent work examines how students engage with LLMs in less constrained settings, as well as whether relatively lightweight designs that leverage LLM functionalities can encourage constructive engagement.
Taken together, in this talk I argue that while LLMs expand access to help and instructional content, ITS-like designs remain necessary for promoting constructive engagement during instructional activities.

Walking and talking is an everyday activity. You may assume that you do not need too much attention or cognitive resources to do this activity. A large literature will tell you that when attention is divided things can go wrong (i.e., you could trip and fall, you could stop talking when walking, etc.). With functional near infra-red spectroscopy or fNIRS, an innovative portable neuroimaging technology, we can take a glimpse at what is going on in the brain when you are doing these activities. We can use this information to better understand neural processes, differences between age groups, potential markers of risk of physical/cognitive declines and to track improvements in cognitive health after interventions.

This seminar will highlight recent findings from Prof. Andrew Nicholson’s program of research at the University of Ottawa, Canada. The presentation will begin with neuroimaging studies on the distinct neural correlates of post-traumatic stress disorder (PTSD) and its dissociative subtype, emphasizing how these insights may inform more personalized treatment approaches. Nicholson will then share emerging evidence on neurofeedback as a non-invasive intervention for PTSD, illustrating how individuals can learn to self-regulate disrupted brain activation patterns in the aftermath of trauma. Results from a recent randomized controlled trial (RCT) of neurofeedback will be presented, showing clinically significant reductions in PTSD symptoms alongside a shift toward normalization of pathological neural network connectivity. The final part of the talk will examine the neurobiology of minority stress, highlighting how oppression and discrimination contribute to trauma-related symptoms, moral injury, and negative mental health outcomes. Taken together, by bridging these areas of inquiry, this presentation will demonstrate how neuroimaging technologies can be applied to advance understanding of mental health conditions affecting both traumatized and marginalized populations, and to guide the development of more effective and equitable interventions.

This presentation will provide a short overview of the development of supportive smart home technology at the Sensors and Analytics to Monitor Mobility and Memory (SAM3) research group. It will review why this technology is important, how it can address some of the challenges of aging, and how artificial intelligence ties it together.

Where do people turn when the health information they need is missing, overlooked, or difficult to access? Increasingly, the answer is technology. This shift is especially important in women’s health, an area that has long been underrepresented in research, education, and public health communication. Social media, online platforms, period-tracking apps, and other digital health tools have become major sources of information on topics such as menstrual and reproductive health, hormonal conditions, and overall well-being. For many women, these technologies offer accessible and empowering ways to learn about their bodies and health. But do they truly improve understanding, or do they instead create the illusion of knowledge and contribute to the spread of misinformation? This presentation explores how technology is reshaping women’s health education through social media, online health information, and digital tools. Drawing on recent findings, it examines how these platforms may influence both perceived and objective knowledge of women’s health topics. It also highlights current projects and initiatives that use technology to address knowledge gaps, broaden access to accurate information, and make women’s health research more widely accessible. Ultimately, this presentation offers a timely perspective on how innovation is shaping the future of women’s health education and literacy.

Driving is an everyday activity that requires continuous attention, decision-making, and the ability to respond to changing road conditions. While it may seem automatic, research shows that when attention is divided or cognitive demands increase, driving performance can be affected. As crash rates increased, researchers began to identify protective factors that could reduce the likelihood of accidents and improve road safety, particularly in relation to changes in cognitive functioning across the lifespan. Driving simulators were developed to study driving behaviour in a safe and controlled environment. By replicating real-world driving scenarios and allowing researchers to manipulate conditions such as traffic and road design, simulators make it possible to examine driver responses under situations that would be difficult or unsafe to study on real roads. This presentation will highlight how driving simulators have been used across areas such as human factors and medical research to study driver behaviour under conditions like distraction, increased mental demands, and age-related cognitive changes. It will also examine how reaction time is used to understand driver responses to pressure and potential hazards, demonstrating how simulators provide insight into driver behaviour and road safety.

Story recall remains a primary method of assessing episodic memory, but manual scoring of participants’ responses can be laborious and time consuming. Computational methods offer an efficient, scalable, automated alternative for recall scoring. We asked whether two computational approaches could match traditional human scoring of story recall. The first approach employed a classical natural language processing (NLP) pipeline using rule-based techniques, including n-gram extraction, named entity recognition, and dependency parsing (Python Natural Language Toolkit). The second approach leveraged the advanced language understanding capabilities of large language models (LLMs). We used the classical NLP method and two LLMs (OpenAI’s GPT-4o and Google’s Gemini 2.0/2.5) separately to score n = 160 young and n = ~95 older adults’ immediate and delayed recall of pairs of stories created by Taler et al. (2021). Responses were classified as veridical (i.e., word-for-word), gist (i.e., general idea), or distorted (i.e., clearly errors). Remarkably, both approaches closely matched the human scoring of veridical recall. The classical NLP pipeline failed to capture gist and distortion scores, whereas the LLMs demonstrated moderate correlations with human ratings on both dimensions. We explored different LLM prompting methods and received similar results each time. Computational methods are approaching human-level performance in scoring memory veridical recall, though they still need improvement in capturing the subtleties of memory errors. Further development of these automated tools will help make cognitive and neuropsychological testing more efficient and accessible, and may be especially useful with large datasets.