A Computational Model of Hopelessness and Active-Escape Bias in Suicidality

Abstract

Currently, psychiatric practice lacks reliable predictive tools and a sufficiently detailed mechanistic understanding of suicidal thoughts and behaviors (STB) to provide timely and personalized interventions. Developing computational models of STB that integrate across behavioral, cognitive and neural levels of analysis could help better understand STB vulnerabilities and guide personalized interventions. To that end, we present a computational model based on the active inference framework. With this model, we show that several STB risk markers -hopelessness, Pavlovian bias and active-escape bias -are interrelated via the drive to maximize one's model evidence. We propose four ways in which these effects can arise: (1) increased learning from aversive outcomes, (2) reduced belief decay in response to unexpected outcomes, (3) increased stress sensitivity and (4) reduced sense of stressor controllability. These proposals stem from considering the neurocircuits implicated in STB: how the locus coeruleus -norepinephrine (LC-NE) system together with the amygdala (Amy), the dorsal prefrontal cortex (dPFC) and the anterior cingulate cortex (ACC) mediate learning in response to acute stress and volatility as well as how the dorsal raphe nucleus -serotonin (DRN-5-HT) system together with the ventromedial prefrontal cortex (vmPFC) mediate stress reactivity based on perceived stressor controllability. We validate the model by simulating performance in an Avoid/ Escape Go/No-Go task replicating recent behavioral findings. This serves as a proof of concept and provides a computational hypothesis space that can be tested empirically and be used to distinguish planful versus impulsive STB subtypes. We discuss the relevance of the proposed model for treatment response prediction, including pharmacotherapy and psychotherapy, as well as sex differences as it relates to stress reactivity and suicide risk.

Decision‐making biases in suicide attempters with major depressive disorder: A computational modeling study using the balloon analog risk task (BART)

“…Furthermore, to contrast our current findings with other related research in the field, we highlight the suicide model proposed by Karvelis and Diaconescu (2022), which suggested that hopelessness was a mediating factor for suicidality. We share the idea that hopelessness could increase suicidal ideation and might play a role in increasing the negative emotions in the process of suicide decision.…”

“…It is also interesting to note that, norepinephrine, known to be a general biomarker of suicide，was found to be associated with both loss aversion (Sokol‐Hessner & Rutledge, 2018), and active‐escape behaviors (Karvelis & Diaconescu, 2022), providing support of a shared mechanism between loss aversion and active‐escape behaviors, two aspects highlighted by our current findings. The combination of decision‐making computational modeling, in‐depth psychological process studies, and biomarkers may form exciting new research directions in suicide prediction studies.…”

“…This adaptation is part of a research direction that values the importance of a loss situation in suicide studies. Some notable recent studies in this area have used computational modeling and biological studies (Dombrovski & Hallquist, 2022; Karvelis & Diaconescu, 2022), to explore the link between a Pavlovian bias (e.g., loss aversion, active avoidance). Loss aversion, as a Pavlovian response (Huys et al, 2012), causes individuals to focus more on the painful feelings they are currently experiencing.…”

“…We share the idea that hopelessness could increase suicidal ideation and might play a role in increasing the negative emotions in the process of suicide decision. Our current study focused further on suicide decision weighing that could distinguish between suicidal ideation and suicidal behavior, which could be two different mechanisms, While the hopelessness factor may play a central role in the generation of suicidal ideation, it did not distinguish between the participants with suicide ideation from those with suicidal behavior (Karvelis & Diaconescu, 2022). Our study focused on the process of suicidal behavior, and the predictability of loss was a factor that played an important role in the value weighing of suicide decision.…”

Suicide prevention and ketamine: insights from computational modeling

“…Increased Pavlovian control can therefore lead to more impulsive actions and biases that are not optimal in the long run. Karvelis and Diaconescu ( 49 ) used computational modeling to formally conceptualize the active-escape and Pavlovian biases in suicidality as a product of perturbations in probabilistic learning. According to the model, the Pavlovian active-escape bias and other suicide risk markers, including hopelessness and reduced cognitive control, may stem from the following four mechanistically distinct parameters that capture components of learning and stress responsiveness: increased stress sensitivity, increased learning from stressors, reduced sense of controllability of stressors, and a reduced ability to unlearn maladaptive beliefs.…”