B-Smart Laboratory - Biobehavioral Substance Mechanisms And Related Treatments

Broadly, Dennis McChargue and the B-Smart lab members are interested in the psychological and biological mechanisms that explain substance use behaviors in a variety of populations, as well as related harm associated with substance use. We are also interested in applying these findings to improve interventions and to treat substance use disorders.


Reinforcement and Punishment Mechanisms of Substance Use Behavior:

Across the history of understanding addictive processes research have explored reinforcement models. Such models have biological and psychological interactions that increase the likelihood of use.  Within the Bsmart Lab, we have utilized negative and positive reinforcement as explanatory pathways for the development of co-occurring psychiatric problems among substance using population.  The basic thought is that the efficiency and effectiveness of symptom alleviation from substance use produced learning trials that negatively reinforce chronic substance use among psychiatric populations (PTSD, Schizophrenia, Major Depression and Anxiety Disorders).  In addition, abstinence from said substances without adequate symptom alleviation substantially increases relapse of substance and/or psychiatric symptoms.  We have investigated this premise across a number of substances (e.g., Herschl et al., 2012ab; Highland et al, 2013; King & McChargue, 2014; Klanecky et al., 2020; McChargue & Doran, 2009) and psychiatric disorders (e.g., King & McChargue, 2014; Oakland & McChargue, 2014; McChargue et al., 2011).  We have also utilized experimental paradigms and treatment outcome approaches (e.g., King et al., 2019; McChargue et al., 2008; Oakland et al., in prep). 

From this initial line of research, it has become clear that demonstrating reinforcement mechanisms that link psychiatric symptoms to substance use or cessation is not the complete story (e.g., Hitsman et al., 2013; Oakland & McChargue, 2014). The issue lines in tying negative reinforcement to psychiatric symptoms and use.  Rather, there may be intermediary common pathways the progress patients towards use and that such pathways provide a more nuanced understanding of co-occurring psychiatric disorders (mental health and substance use). 

As such, Bsmart members have and are continuing to investigate that hypothesis that tolerance of stressful stimuli produce escape behavior to avoid or reduce said stimuli.  This learning process may reflect negative reinforcement facets that increases avoidance with the greatest avoidance being eventual relapse. Alternatively, this learning process may reflect positive punishment, which assumes that increased avoidance from stressful stimuli weakens abstinence efforts leading to eventual relapse.  Research has yet to clarify such phenomena within learning paradigms, which is one of our foci over the next few years.

Towards this end, we currently examine avoidance constructs (cognitive, behavioral and emotional) that interact with distress tolerance to predict treatment completion or reduced adherence (King & McChargue, 2014; Oakland et al., in prep; Oakland & McChargue, 2014). We are also developing experimental tests of avoidance and applying these tests within substance treatment seeking individuals (Oakland et al., in prep).  Our charge moves toward providing a more nuanced understanding of reinforcement and punishment pathways linking avoidance to eventual relapse.  In addition, we aim to develop treatments that mitigate these processes.

Social Ecological Mechanisms that Enhance Recovery

Consistent with our focus on associated learning facets, we view recovering from behaviorally oriented harm within a social ecological perspective.  In other words, we are developing a line of research that investigates social/community factors that enhance the risk of harm or decrease the risk of harm among those recovering from harm-related behaviors (i.e., substance use, criminogenic, sexual violence).  This broad line of research implicates a more generalized target and crosscuts different populations.  To this end, Bsmart members are developing models and treatments that enhance recovery efforts of individuals leaving transitional residential treatment for substance use disorders (e.g., Little, Tibbs & McChargue, in prep; Oakland et al., in prep; Oakland & McChargue, 2014).  We plan to use multi-level assessment including EMA and GPS tracking during recovery. We are also investigating restorative justice psychoeducation groups’ impact on recidivism from probation and prisons (Kennedy et al., 2019).  Lastly, we move towards developing social network models that increase the risk of alcohol-related sexual harm among rural bar goers. 

Decision-Making Mechanisms of Substance Use-Related Harm:

Sexual Violence & Alcohol:

Another research area in the BSmart lab involves examining the intersection of substance use and sexual violence. Generally, the lab is interested in determining the mechanisms involved in alcohol-related sexual perpetration. To understand these mechanisms, the lab is currently conducting a substance administration study, investigating how neuropsychological variables and in-vivo alcohol exposure influence sexual decision-making (measured by social cognitive performance and progression through a date rape vignette). We have a history of developing experimental decision-making paradigms (Tuliao et al., 2017; Tuliao, McChargue & Klanecky, 2017) that predict sexual violence among binge drinking males and the risk of victimization among binge drinking females (Klanecky et al., 2016ab; Tuliao et al., 2016). We examine cross-country/cultural facets of sexual violence risks (Klanecky et al., 2020; Tuliao, Landoy & McChargue, 2016; Tuliao et al., 2019; 2020), within-subjects factors that increase risk (Tuliao et al., 2020) and environmental factors (e.g., Richner et al., 2019, June).  Our future direction move towards developing a brief intervention to reduce sexual violence among male binge drinkers as well as gaining a better understanding of social ecological facets that increase violence.  Lastly, we test the effects of social media behavior that increase or decrease the likelihood of sexual violence during drinking situations.