When outcomes are not completely certain, we have to grapple with risk. Different individuals have characteristically different attitudes to risk - something that has been extensively investigated in psychology and psychiatry, albeit largely using venerable measures that lack certain axiomatically-desirable properties. Here we consider a modern risk measure for modeling human and animal decision-making called conditional value at risk (CVaR) which is particularly apposite because of its preferential focus on worst-case outcomes. We discuss theoretical characteristics of CVaR in single and multi-step decision-making problems, relating our findings to avoidance and worry. This is joint work with Chris Gagne.

There is growing interest in understanding the evolution of depressive symptomatology over time, the dynamics of how symptoms interact during periods of wellness and as one approaches an episode of illness. It is thought that by understanding these dynamics we can develop tools to identify early warning signs of depression before it takes hold. But this sort of research is prohibitively challenging; it requires research participants to actively log their thoughts, feelings, and emotions regularly, over months or even years to capture critical transitions into a depressed state. An alternative is to use sources of data, such as social media posts, that people produce routinely in the course of their everyday life. Recent data has shown this might be possible; depressed individuals use language differently, for example, using more first-person singular pronouns (I, me, my) and more emotional negative words (hurt, ugly, nasty). In a set of two studies, I will present research testing if we can use social media posts to detect depression, I will test how specific such findings are to depression versus other aspects of mental health and finally, if these ‘linguistic symptoms’ can be used to test core theories about the network structure of depression and how it changes during episodes of illness.

Anxiety is associated with elevated self-report of aversion to uncertainty and ambiguity. However there has been relatively little attempt to characterize the underlying mechanisms. Over recent years, computational modelling has been used to advance our understanding of human decision-making and the brain mechanisms that support it. This approach can help us to formalize and understand how choice behaviours can be optimally adapted to different situations and the ways in which individuals may deviate from optimal behaviour.
In everyday life, our decision-making often takes place under some form of uncertainty. We can distinguish ‘first-order’ uncertainty which occurs when a given action only leads to a given outcome on a proportion of occasions from ‘second-order’ uncertainty, which describes uncertainty regarding the action-outcome contingency itself. Two sources of second-order uncertainty are contingency volatility and contingency ambiguity. In experiment 1, we manipulated contingency volatility and revealed that elevated trait anxiety is linked to a deficit in adjusting probabilistic learning to changes in volatility and also to reduced peripheral (pupil dilation) responses to volatility. In experiment 2, through bifactor modelling of Internalizing symptoms and hierarchical modelling of task performance, we determined that this difficulty in optimizing probabilistic learning under volatility is common to both anxiety and depression. In experiment 3, we investigated another source of second order uncertainty. Here, we manipulated the level of ambiguity – or missing information – present on each trial. High trait anxious individuals showed elevated ambiguity aversion, being especially sensitive to increases in the amount of missing information when choosing between two options. Analysis of fMRI data revealed that participants show elevated activity in the dorsal anterior cingulate and inferior frontal sulcus at time of choice on trials with high missing information when they subsequently engaged with versus avoided the ambiguous option; this pattern was strongest in high trait anxious individuals. One possibility is that these frontal regions support rational evaluation of alternate actions as opposed to simple heuristic-based avoidance of options characterized by high second-order uncertainty.

Social media data is being increasingly used to computationally learn about and infer the mental health states of individuals and populations. Despite being touted as a powerful means to shape interventions and impact mental health recovery, little do we understand about the theoretical, domain, and psychometric validity of this novel information source, or its underlying biases, when appropriated to augment conventionally gathered data, such as surveys and verbal self-reports. This talk presents a critical analytic perspective on the pitfalls of social media signals of mental health, especially when they are derived from “proxy” diagnostic indicators, often removed from the real-world context in which they are likely to be used. Then, to overcome these pitfalls, this talk presents results from two case studies, where machine learning algorithms to glean mental health insights from social media were developed in a context-sensitive and human-centered way, in collaboration with domain experts and stakeholders. The first of these case studies, a collaboration with a health provider, focuses on the individual-perspective, and reveals the ability and implications of using social media data of consented schizophrenia patients to forecast relapse and support clinical decision-making. Scaling up to populations, in collaboration with a federal organization and towards influencing public health policy, the second case study seeks to forecast nationwide rates of suicide fatalities using social media signals, in conjunction with health services data. The talk concludes with discussions of the path forward, emphasizing the need for a collaborative, multi-disciplinary research agenda while realizing the potential of social media data and machine learning in mental health -- one that incorporates methodological rigor, ethics, and accountability, all at once.

Digital tools have been proven effective to deliver mental health screening and intervention, but uptake is usually very low, severely limiting generalizability of findings and impact of tools. The COVID-19 pandemic has substantially increased the urgency to develop nimble and valid screening instruments and tools that effectively address users' needs. We have developed a framework of digital data triangulation, intervention co-development, and integration with brick and mortar systems, and will present preliminary results.

Digital phenotyping and machine learning technologies have shown the potentials to measure objective behavioral and physiological markers, provide risk assessment for people who might have a high risk of poor mental health and wellbeing, and help better decisions or behavioral changes to support health and wellbeing. I will introduce a series of studies, algorithms, and systems we have developed for measuring, predicting, and supporting personalized health and wellbeing. I will also discuss challenges, learned lessons, and potential future directions in mental health and wellbeing research.