The TAP Lab has always placed emphasis on laboratory studies, longitudinal work, and novel measures to advance knowledge about self-injurious thoughts and behaviors. Early on (2010-2014), this took the form of experiments with painful stimuli and psychophysiological measures, and longitudinal studies with implicit measures. You can access our papers on this work by clicking here. More recently, this has taken the form of virtual reality experiments and a range of machine learning studies. We briefly discuss some of our previous and ongoing work with these methods below.
Virtual reality experiments
Why virtual reality? Suicide research has an inherent disadvantage: We cannot conduct studies that seek to make our outcome of interest (i.e., suicidal behaviors) more likely. This means that we cannot directly study the causes of suicidal behaviors in the lab. Because knowledge about the causes of a phenomenon are crucial for understanding, predicting, and preventing that phenomenon, this is a massive limitation for suicide research. We have been stuck with relying on correlational and longitudinal information to guess at causes, and it’s not gone well (see suicide rates, meta-analyses on prediction and prevention). But due to ethical and logistical limitations, we have had no recourse. Thankfully, with recent advances in technology, virtual reality studies can finally provide a way forward.
They have the potential to safely-but-meaningfully approximate suicidal situations and behaviors in the laboratory. Virtual reality can realistically simulate a wide range of potential suicidal situations and provide environmental affordances for a wide range of suicidal behaviors. These behaviors can be used as outcome measures in experiments that seek to test ideas about the causes of suicidal behavior. Virtual suicide is obviously not isomorphic with actual suicide (thankfully), but we believe that it is close enough to provide meaningful (though tentative) information about suicide causes. As technology continues to progress and realism is intensified, virtual suicidal behavior will become an even closer approximation of actual suicidal behavior, further improving the meaningfulness of these kinds of experiments. For the past three years we have been developing and testing a viable approach to virtual reality experiments on suicide. Below, we briefly describe this work.
Initial feasibility and validity studies. Please see our recent paper in Behaviour Research and Therapy for information on our initial three studies on the development and validation of the VR suicide approach. The paper can be found here. As a quick summary our initial work: (1) VR suicide exposure is safe; (2) VR suicide scenarios are rated as highly realistic and suicide-relevant; (3) there is approximately a 5% VR suicide rate under neutral conditions; (4) several predictors of suicide significantly predict VR suicide (e.g., prior suicidal behavior and plans, agitation, risk taking, thwarted belongingness, fearlessness about death, and male sex); (5) the reasons that people give for not engaging in suicidal behavior resemble the reasons that people give for not engaging in VR suicide; and (6) the VR suicide rate increases to 25% under conditions of reward/avoidance, indicating that this method is sensitive to experimental manipulations.
We recently conducted two additional studies (currently under review) that further confirm the safety of this approach. In one study, we followed-up over 150 participants randomly assigned to be exposed to VR suicide scenarios or control scenarios. Across a two year follow-up period, results indicated that VR suicide exposure did not cause any increases in suicidality, negative emotions, suicidal capability, or any other such factor. In a separate study of over 120 participants (half of whom had a recent history of suicidality), we followed participants for two weeks after exposure to VR suicide scenarios. Results indicated that exposure to these scenarios did not cause any increases in suicidality, negative emotions, suicidal capability, or any other such factors. Combined with our initial safety study (see above), these findings indicate that exposure to VR suicide scenarios has no immediate, short-term, or long-term effects on suicidality or related phenomena (even in people with a recent history of suicidality).
Interesting initial findings of ongoing work. We have conducted a series of studies (and are currently conducting several more) that show a consistent pattern of findings: people are far more likely to engage in VR suicide when they perceive it has some kind of beneficial function (e.g., some kind of avoidance of something undesirable or attainment of something desirable). For example, various types of stress, rejection, etc. do not increase the VR suicide rate. But telling people that engaging in VR suicide will help them avoid a later stressor markedly increases the VR suicide rate (~300-500%). This suggests that it’s not stress, sadness, rejection, etc. that directly cause suicide. It is the belief that engaging in suicide will help one avoid a very undesirable future situation or feeling that directly causes suicide. Interestingly, this is not specific to the avoidance of/escape from unpleasant situations; it applies to the attainment of positives as well. Even more interestingly, personal characteristics (e.g., depression, prior self-injury, fearlessness about death) have far smaller effects on VR suicide than situational characteristics (e.g., imposing a function on VR suicide). This suggests that, when it comes to suicidal behaviors, the nature of the situation has a lot more influence than the nature of the person. In part due to the limitations of prior research methods, relatively little research, practice, and theory has focused on situations — traditionally, the focus has been on the characteristics of the person. These emerging findings suggest that it may be helpful to focus much more on situational contributions to suicidal behaviors.
Machine Learning Work
Machine learning is a method that fits well with our view of suicidality predictors/causes as complex and indeterminate (see our Summarizing Knowledge Section for more information on this view). This has clear implications for prediction and, indeed, machine learning greatly improves predictive accuracy compared to traditional methods. Led by our collaborator Dr. Jessica Ribeiro at FSU, we have several previous and ongoing projects aimed at using machine learning methods to better predict and understand suicide risk. See here for Dr. Ribeiro's work.
Because machine learning is a general computational approach (i.e., it is not specific to prediction), we are also applying it to address other types of questions about the nature of suicidality. Below, we note a few of our ongoing projects in this area:
Is the difference between suicide ideators and attempters complex and indeterminate (or simple and determinate)? Several recent theories have proposed that particular factors or small sets of factors should distinguish ideators from attempters. But none of these specific factors seems to distinguish between ideators and attempters much better than random chance. As an alternative consistent with our new Paradigm, we hypothesize that the difference between ideators and attempters is complex and indeterminate. In other words, to distinguish between ideators and attempters, it will require the consideration of a relatively large number of factors combined in a complicated way, and there is no single complicated algorithm or recipe for this distinction. We are using both unsupervised and supervised machine learning to investigate this possibility. Across five large data sets, preliminary results strongly support the complex and indeterminate hypothesis.
Are there meaningful differences between suicidal civilians and suicidal military service members? Inspired by the temporary rise in the military suicide rate (compared to the civilian rate) approximately 10 years ago, many have proposed that there may be important differences between suicidal civilians and suicidal military service members. Unfortunately, none of the proposed distinguishing factors seemed to differentiate between these two groups much better than random chance. Another possibility is that there are differences between these groups, but they are complex and indeterminate. We are currently using supervised machine learning methods to investigate this possibility.
Are suicidality phenomena (and related phenomena) natural kinds or non-essentialized categories with blurry boundaries? Traditionally, many researchers have assumed that suicidality phenomena are natural kinds (i.e., each phenomenon has an essence and firm boundaries between it and all other phenomena). Some hold a strong version of this view, proposing many very specific subtypes of suicidality and essences for each; others hold a weaker version of this view, proposing a few strong subtypes (e.g., ideation vs. attempt) with murkier subtypes in between. Based on our Protean Paradigm, we hypothesize that all subtypes of suicidality (including ideation vs. attempt) are non-essentialized. In other words, none of these categories has a distinguishing feature that separates it from all others, and each has great overlap with all others. We hypothesize that this overlap extends to non-suicidal phenomena as well, including nonsuicidal self-injury, drug use, and eating disorder symptoms. We believe that traditional statistical methods and unsupervised machine learning will do a poor job of distinguishing among these categories, and that supervised machine learning will do a much better (though still far from perfect) job of distinguishing among these categories.