An electrophysiological index of changes in risk decision-making strategies

Abstract

Human decision-making is significantly modulated by previously experienced outcomes. Using event-related potentials (ERPs), we examined whether ERP components evoked by outcome feedbacks could serve as biomarkers to signal the influence of current outcome evaluation on subsequent decision-making. In this study, eighteen adult volunteers participated in a simple monetary gambling task, in which they were asked to choose between two options that differed in risk. Their decisions were immediately followed by outcome presentation. Temporospatial principle component analysis (PCA) was applied to the outcome-onset locked ERPs in the -200 – 1000 ms time window. The PCA factors that approximated classical ERP components (P2, feedback-related negativity, P3a, & P3b) in terms of time course and scalp distribution were tested for their association with subsequent decision-making strategies. Our results revealed that a fronto-central PCA factor approximating the classical P3a was related to changes of decision-making strategies on subsequent trials. The decision to switch between high- and low-risk options resulted in a larger P3a relative to the decision to retain the same choice. According to the results, we suggest the amplitude of the fronto-central P3a is an electrophysiological index of the influence of current outcome on subsequent risk decision-making. Furthermore, the ERP source analysis indicated that the activations of the frontopolar cortex and sensorimotor cortex were involved in subsequent changes of strategies, which enriches our understanding of the neural mechanisms of adjusting decision-making strategies based on previous experience.

Valence and magnitude ambiguity in feedback processing

“…To investigate whether the current outcomes modulate subsequent decision‐making (e.g., the “win‐stay, lose‐switch” strategy), we calculated the “switch ratio” (i.e., the likelihood of choosing different options in adjacent trials; see Zhang et al., 2014, 2013) and reaction time following each kind of outcome, and put them into an one‐way ANOVA of Outcome for sequence A (six levels: +9/−9/∆9/−99/+99/∆99) and sequence B (six levels: +9/+99/+∆/−9/−99/−∆) separately. The effect of the Outcome factor was insignificant in either sequence for switch ratio ( F s < 1.200, p s > .315; see Table 1) or reaction time ( F s < 1.269, p s > .290; see Table 1).…”

“…However, accumulating evidences from recent studies are in favor of Holroyd and Coles's (2002) original idea (e.g., Goyer et al., 2008; Gu et al., 2011; Meadows, Gable, Lohse, & Miller, 2016; Sambrook & Goslin, 2015). The P3 is a centro‐parietal positivity that appears after the FRN when elicited by outcome feedback (Polezzi, Sartori, Rumiati, Vidotto, & Daum, 2010; Wu & Zhou, 2009; Zhang et al., 2013). When investigating feedback processing, the P3 is often associated with the emotional significance of outcome feedback (Gu et al., 2011; Luo et al., 2014; Wu & Zhou, 2009).…”

Age-related characteristics of risky decision-making and progressive expectation formation

“…In addition, both the FRN and the reward positivity ERP components typically constitute a 'complex' with the following P3 component peaking at approximately between 300 and 500 ms [27][28][29]. The P3 is thought to reflect a more thorough evaluative process of the outcome events [30][31][32], including the local and global probabilistic properties, motivational significance of the stimuli, and also the amount of expended/invested attention [33].…”

“…In addition, both the FRN and the reward positivity ERP components typically constitute a 'complex' with the following P3 component peaking at approximately between 300 and 500 ms [27][28][29]. The P3 is thought to reflect a more thorough evaluative process of the outcome events [30][31][32], including the local and global probabilistic properties, motivational significance of the stimuli, and also the amount of expended/invested attention [33].Data collected in various experimental conditions in which inferences of reinforcement learning were addressed via feedback-related evaluation processes show an age-dependent decrease regarding the amplitudes of feedback-related ERP components [34][35][36]. Accordingly, imaging approaches suggest that the elderly have difficulties in representing outcome values during predictive processing due to decreased frontostriatal connectivity with age [5,20].…”

Age-dependent characteristics of feedback evaluation related to monetary gains and losses

“…The feedback associated P3 can be defined as a positivity peaking between 300-500 ms post-stimulus (Kamarajan et al, 2009;West, Bailey, Anderson, & Kieffaber, 2014;Wu & Zhou, 2009;Yeung & Sanfey, 2004), and it represents the process of elaborative evaluation of outcome reflecting the functional significance of stimuli (Gu et al, 2011;Lole, Gonsalvez, Barry, & De Blasio, 2013;Zhang et al, 2013). Two subtypes of the P3 component (P3a and P3b) can be defined with different scalp distributions and generator mechanisms (Polich & Criado, 2006;Squires, Squires, & Hillyard, 1975).…”