Predicting risk decisions in a modified Balloon Analogue Risk Task: Conventional and single-trial ERP analyses

Abstract

Event-related potential (ERP) has the potential to reveal the temporal neurophysiological dynamics of risk decision-making, but this potential has not been fully explored in previous studies. When predicting risk decision with ERPs, most studies focus on between-trial analysis that reflects feedback learning, while within-trial analysis that could directly link option assessment with behavioral output has been largely ignored. Suitable task design is crucial for applying within-trial prediction. In this study, we used a modified version of the classic Balloon Analogue Risk Task (BART). In each trial of the task, participants made multiple rounds of decisions between a risky option (pump up the balloon) and a safe option (cash out). Behavioral results show that as the level of economic risk increased, participants were less willing to make a risky decision and also needed a longer response time to do so. In general, the ERP results showed distinct characteristics compared with previous findings based on between-trial prediction, particularly about the role of the P1 component. Specifically, both the P1 (amplitude and latency) and P3 (amplitude) components evoked by current outcomes predicted subsequent decisions. We suggest that these findings indicate the importance of selective attention (indexed by the P1) and motivational functions (indexed by the P3), which may help clarify the cognitive mechanism of risk decision-making. The theoretical significance of these findings is discussed.

Decision‐ and feedback‐related brain potentials reveal risk processing mechanisms in patients with alcohol use disorder

“…The relationships between decision-phase and outcomephase processes manifested in ERP components were examined in AUD and HC concerning the primary issue of whether abnormal risky decisions involve a failure at the decision phase (including processes such as reward prediction) and/or at the outcome phase (including prediction error and reward valuation), considering the modulation of these processes by impulsivity. A variant of the balloon analogue risk task 1 (BART; Lejuez et al, 2002Lejuez et al, , 2003, modified for EEG, was used for the assessment of risk-taking proneness (Lejuez et al, 2003) and particularly for distinguishing decision and feedback/outcome-related processes (Gu et al, 2018;Kiat et al, 2016). Its validity has been demonstrated in healthy individuals (Wallsten, Pleskac, & Lejuez, 2005) and in AUD (Fein & Chang, 2008) based on self-reports of risk behavior such as smoking, gambling, and unprotected sexual encounters (Lejuez et al, 2003) and based on relationships between sensation-seeking and/or impulsivity and decision behavior (Lauriola, Panno, Levin, & Lejuez, 2013;Lejuez et al, 2002).…”

“…This pattern of results identifies distinct anomalies at decision-and feedback-evaluation stages. Most fundamentally, the large decision P3 (Cuzen, Andrew, Thomas, Stein, & Fein, 2013;Lopez-Caneda et al, 2013) and its normal variation as a function of decision choice Gu et al, 2018;Steffen et al, 2011) argue that poor decision behavior in AUD is not a function of failing to engage decision prompts but is, at least in part, a subsequent failure to process differential feedback. The decision P3 (or N2-P3 complex) has been related to reward prediction (Hajcak et al, 2005;Steffen et al, 2011;Yeung & Sanfey, 2004) and to more effortful processing supported by the recruitment of additional neural or cognitive resources for difficult choices (Kiefer, Marzinzik, Weisbrod, Scherg, & Spitzer, 1998;Lopez-Caneda et al, 2012;Petit et al, 2014;Sirevaag, Kramer, Coles, & Donchin, 1989;Yee & Miller, 1994).…”

“…Decision P3 varies with risk level (i.e., the chance of winning) and with monetary gain (i.e., outcome value; He, Guan, Zhao, & Cao, ; Schuermann, Endrass, & Kathmann, ). High‐risk decisions, as indicated by high‐valued choices with less predictable outcomes, were associated with a larger decision P3 than were low‐risk decisions (Gu, Zhang, Luo, Wang, & Broster, ; Hassall et al, ; Kiat, Straley, & Cheadle, ). P3 amplitude also predicted whether participants subsequently made a risky or a safe choice on a given trial (Gu et al, ).…”

“…High‐risk decisions, as indicated by high‐valued choices with less predictable outcomes, were associated with a larger decision P3 than were low‐risk decisions (Gu, Zhang, Luo, Wang, & Broster, ; Hassall et al, ; Kiat, Straley, & Cheadle, ). P3 amplitude also predicted whether participants subsequently made a risky or a safe choice on a given trial (Gu et al, ). To date, smaller P3 in AUD and in individuals at risk of developing AUD has been reported in oddball tasks or go/no‐go tasks (Euser et al, ; Hada, Porjesz, Chorlian, Begleiter, & Polich, ; Hill et al, ; Kamarajan et al, ; Oscar‐Berman, ; Pfefferbaum, Ford, White, & Mathalon, ).…”

Electrophysiological indexes of option characteristic processing

“…First, in most previous ERP studies, available options did not change throughout a whole experiment; thus, participants acquired knowledge on option characteristics before the time point of their presentation (Hajcak, Moser, Holroyd, & Simons, 2006;Zheng, Li, Wang, Wu, & Liu, 2015). As a result, it is challenging to ensure that the evoked electrophysiological signals are time-locked to option assessment (Carlson, Zayas, & Guthormsen, 2009;Gu, Zhang, Luo, Wang, & Broster, 2018). Second, individuals adjust their choices according to previous decision outcomes (O'Doherty, Cockburn, & Pauli, 2017;Schultz, 2004), which may in turn modulate the electrophysiological signals associated with option assessment in the next trial.…”