Negative Urgency as a Risk Factor for Hazardous Alcohol Use: Dual Influences of Cognitive Control and Reinforcement Processing

Negative Urgency (NU) is a prominent risk factor for hazardous alcohol use. While research has helped elucidate how NU relates to neurobiological functioning with respect to alcohol use, no known work has contextualized such functioning within existing neurobiological theories in addiction. Therefore, we elucidated mechanisms contributing to the NU–hazardous alcohol use relationship by combining NU theories with neurobiological dual models of addiction, which posit addiction is related to cognitive control and reinforcement processing. Fifty-five undergraduates self-reported NU and hazardous alcohol use. We recorded EEG while participants performed a reinforced flanker task. We measured cognitive control using N2 activation time-locked to the incongruent flanker stimulus, and we measured reinforcement processing using the feedback-related negativity (FRN) time-locked to better-than-expected negative reinforcement feedback. We modeled hazardous drinking using hierarchical regression, with NU, N2, and FRN plus their interactions as predictors. The regression model significantly predicted hazardous alcohol use, and the three-way interaction (NU×N2×FRN) significantly improved model fit. In the context of inefficient processing (i.e., larger N2s and FRNs), NU demonstrated a strong relationship with hazardous alcohol use. In the context of efficient processing (i.e., smaller N2s and FRNs), NU was unrelated to hazardous alcohol use. This analysis provides preliminary evidence that brain mechanisms of cognitive control and reinforcement processing influence the relationship between NU and hazardous alcohol use, and confirms a specific influence of negative reinforcement processing. Future clinical research could leverage these neurobiological moderators for substance misuse treatment.


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Negative Urgency (NU) is a prominent risk factor for hazardous alcohol use. While research has helped 28 elucidate how NU relates to neurobiological functioning with respect to alcohol use, no known work has 29 contextualized such functioning within existing neurobiological theories in addiction. Therefore, we 30 elucidated mechanisms contributing to the NU-hazardous alcohol use relationship by combining NU 31 theories with neurobiological dual models of addiction, which posit addiction is related to cognitive 32 control and reinforcement processing. Fifty-five undergraduates self-reported NU and hazardous alcohol 33 use. We recorded EEG while participants performed a reinforced flanker task. We measured cognitive 34 control using N2 activation time-locked to the incongruent flanker stimulus, and we measured 35 reinforcement processing using the feedback-related negativity (FRN) time-locked to better-than-36 expected negative reinforcement feedback. We modeled hazardous drinking using hierarchical regression, 37 with NU, N2, and FRN plus their interactions as predictors. The regression model significantly predicted 38 hazardous alcohol use, and the three-way interaction (NU×N2×FRN) significantly improved model fit. In 39 the context of inefficient processing (i.e., larger N2s and FRNs), NU demonstrated a strong relationship 40 with hazardous alcohol use. In the context of efficient processing (i.e., smaller N2s and FRNs), NU was 41 unrelated to hazardous alcohol use. This analysis provides preliminary evidence that brain mechanisms of 42 cognitive control and reinforcement processing influence the relationship between NU and hazardous 43 alcohol use, and confirms a specific influence of negative reinforcement processing. Future clinical 44 research could leverage these neurobiological moderators for substance misuse treatment.

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The trial structure of the task was as follows: first, a fixation was presented before every trial. A 209 black-and-white square or circle, denoting positive or negative reinforcement trials respectively (Knutson 210 et al., 2001), was presented for 500 ms, followed by a fixation that lasted 500 -700 ms. In the current 211 analysis, we focus our attention on the negative reinforcement condition, given the link between NU and 212 negative reinforcement processes. Congruent (< < < < < or > > > > >) or incongruent (< < > < < or > > < 213 > >) arrows were shown for 100 ms, followed by a fixation lasting 900 -1100 ms. Correct/incorrect 214 feedback was shown for 500 ms (only correct trials were analyzed, to avoid influences of error 215 monitoring), followed by a fixation lasting 500 -700 ms. Point feedback was shown for 1000 ms.

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Average return for correct negative reinforcement trials was zero points, but actual return varied from -30 217 to +30 points. Before participants began, they completed 50 trials for practice. During practice trials, the 218 outcome was always as expected (correct answers resulted in no loss or gain of points). Participants 219 progressed to the main task after obtaining 80% or better accuracy during practice. The task contained 220 960 trials divided into 16 blocks of 60 trials, requiring approximately 90 minutes.

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In line with previous research using random (as opposed to blocked) reinforcement presentations 222 (Knutson et al., 2001), participants were forewarned about the designation of trials as either positive or more or less points than expected on correct trials, this task produced better-than-expected and worse-226 than-expected outcomes without the confounding effect of error monitoring. Therefore, while all trials 227 were "wins" in the sense that "correct" feedback was delivered, trials could be better-than-expected or 228 worse-than-expected. While we recorded both EEG and behavior during task performance, the current

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Based on the theoretical link between NU and rash action specifically undertaken in attempts to relieve 310 aversive states, we examined neural markers of cognitive control and reinforcement processing from 311 negative reinforcement trials as moderators in this analysis. However, to test whether our results are 312 specific to negative reinforcement contexts, we also examined ERPs from positive reinforcement trials.

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Variables were standardized prior to regression analysis (Dawson, 2014