EEG oscillatory activity associated to monetary gain and loss signals in a learning task: Effects of attentional impulsivity and learning ability

Pascalis, Vilfredo De; Varriale, Vincenzo; Rotonda, Marco

doi:10.1016/j.ijpsycho.2011.06.005

Cited by 31 publications

(15 citation statements)

References 99 publications

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“…Additionally, greater enhancement of feedback-locked beta predicted enhanced motivated-learning during a reward task, replicating a previous time-estimation study (Luft, Nolte, et al, 2013). Altogether, this pattern of feedback-locked beta is in line with the interpretation of this EEG index as a reward-specific signal (Cohen et al, 2007; De Pascalis et al, 2012; HajiHosseini et al, 2012; Marco-Pallares et al, 2008; Marco-Pallarés et al, 2009). …”

Section: Discussionsupporting

confidence: 86%

“…Similar to feedback-locked delta (but opposite to feedback-locked theta), researchers have consistently found stronger beta power to positive feedback (e.g., monetary gains), compared to negative feedback (e.g., monetary losses) (Cohen et al, 2007; De Pascalis, Varriale, & Rotonda, 2012; HajiHosseini, Rodríguez-Fornells, & Marco-Pallarés, 2012; Marco-Pallares et al, 2008; Marco-Pallarés et al, 2009). Given that a similar pattern of enhanced beta power has been reported in the ventral striatum of animals during a reward-processing task (Berke, 2009; Courtemanche, Fujii, & Graybiel, 2003), it has been proposed that feedback-locked beta power represents reward-related signals from this region.…”

Section: Introductionmentioning

confidence: 97%

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Willing to wait: Elevated reward-processing EEG activity associated with a greater preference for larger-but-delayed rewards

Pornpattananangkul

Nusslock

2016

Neuropsychologia

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While almost everyone discounts the value of future rewards over immediate rewards, people differ in their so-called delay-discounting. One of the several factors that may explain individual differences in delay-discounting is reward-processing. To study individual-differences in reward-processing, however, one needs to consider the heterogeneity of neural-activity at each reward-processing stage. Here using EEG, we separated reward-related neural activity into distinct reward-anticipation and reward-outcome stages using time-frequency characteristics. Thirty-seven individuals completed a behavioral delay-discounting task. Reward-processing EEG activity was assessed using a separate reward-learning task, called a reward time-estimation task. During this task, participants were instructed to estimate time duration and were provided performance feedback on a trial-by-trial basis. Participants received monetary-reward for accurate-performance on Reward trials, but not on No-Reward trials. Reward trials, relative to No-Reward trials, enhanced EEG activity during both reward-anticipation stage (including, cued-locked delta power during cue-evaluation and pre-feedback alpha suppression during feedback-anticipation) and at the reward-outcome stage (including, feedback-locked delta, theta and beta power). Moreover, all of these EEG indices correlated with behavioral performance in the time-estimation task, suggesting their essential roles in learning and adjusting performance to maximize winnings in a reward-learning situation. Importantly, enhanced EEG power during Reward trials for 1) pre-feedback alpha suppression, 2) feedback-locked theta and 3) feedback-locked beta was associated with a greater preference for larger-but-delayed rewards. Results highlight the association between a stronger preference toward larger-but-delayed rewards and enhanced reward-processing. Moreover, our reward-processing EEG indices detail the specific stages of reward-processing where these associations occur.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 97%

Willing to wait: Elevated reward-processing EEG activity associated with a greater preference for larger-but-delayed rewards

Pornpattananangkul

Nusslock

2016

Neuropsychologia

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“…Similarly, abnormal gamma oscillations are related to dysfunctional activity in the dopaminergic system in patients with psychiatric disorders, GABAergic disinhibition, excitatory activation of the brain, and seeking behaviors related to addiction 39–44 . Interestingly, gamma phasic synchrony plays a role in the learning process under the loss condition in a monetary reward task but this change is modulated by impulsivity 45 . This finding implies that increased gamma coherence is associated with inhibition and punishment-related learning in HCs but has another meaning in individuals with impulsivity problems; i.e., it may be associated with increased risk-taking behavior.…”

Section: Discussionmentioning

confidence: 99%

Neural connectivity in Internet gaming disorder and alcohol use disorder: A resting-state EEG coherence study

Park

Lee

Kim

et al. 2017

Sci Rep

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The present study compared neural connectivity and the level of phasic synchronization between neural populations in patients with Internet gaming disorder (IGD), patients with alcohol use disorder (AUD), and healthy controls (HCs) using resting-state electroencephalography (EEG) coherence analyses. For this study, 92 adult males were categorized into three groups: IGD (n = 30), AUD (n = 30), and HC (n = 32). The IGD group exhibited increased intrahemispheric gamma (30–40 Hz) coherence compared to the AUD and HC groups regardless of psychological features (e.g., depression, anxiety, and impulsivity) and right fronto-central gamma coherence positively predicted the scores of the Internet addiction test in all groups. In contrast, the AUD group showed marginal tendency of increased intrahemispheric theta (4–8 Hz) coherence relative to the HC group and this was dependent on the psychological features. The present findings indicate that patients with IGD and AUD exhibit different neurophysiological patterns of brain connectivity and that an increase in the fast phasic synchrony of gamma coherence might be a core neurophysiological feature of IGD.

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“…In contrast, positive events are associated with increase of power in the high beta [6], [8] and/or low gamma frequency band [10] about 200–400 ms after feedback (although see also [11], [12]). In contrast to the theta response, which appears to distinguish feedback stimuli in a binary manner, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the latter, so far few studies have investigated the association between impulsive personality traits and electrophysiological responses to feedback. It is suggested that impulsive individuals exhibit diminished reactivity of the reward system, reflected in reduced FRN and/or theta oscillatory responses [12], [20], [21]. However, the evidence for this hypothesis is so far rather weak, and findings have not always been replicated [18].…”

Section: Introductionmentioning

confidence: 99%

Relationship between Oscillatory Neuronal Activity during Reward Processing and Trait Impulsivity and Sensation Seeking

et al. 2013

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BackgroundThe processing of reward and punishment stimuli in humans appears to involve brain oscillatory activity of several frequencies, probably each with a distinct function. The exact nature of associations of these electrophysiological measures with impulsive or risk-seeking personality traits is not completely clear. Thus, the aim of the present study was to investigate event-related oscillatory activity during reward processing across a wide spectrum of frequencies, and its associations with impulsivity and sensation seeking in healthy subjects.MethodsDuring recording of a 32-channel EEG 22 healthy volunteers were characterized with the Barratt Impulsiveness and the Sensation Seeking Scale and performed a computerized two-choice gambling task comprising different feedback options with positive vs. negative valence (gain or loss) and high or low magnitude (5 vs. 25 points).ResultsWe observed greater increases of amplitudes of the feedback-related negativity and of activity in the theta, alpha and low-beta frequency range following loss feedback and, in contrast, greater increase of activity in the high-beta frequency range following gain feedback. Significant magnitude effects were observed for theta and delta oscillations, indicating greater amplitudes upon feedback concerning large stakes. The theta amplitude changes during loss were negatively correlated with motor impulsivity scores, whereas alpha and low-beta increase upon loss and high-beta increase upon gain were positively correlated with various dimensions of sensation seeking.ConclusionsThe findings suggest that the processing of feedback information involves several distinct processes, which are subserved by oscillations of different frequencies and are associated with different personality traits.

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EEG oscillatory activity associated to monetary gain and loss signals in a learning task: Effects of attentional impulsivity and learning ability

Cited by 31 publications

References 99 publications

Willing to wait: Elevated reward-processing EEG activity associated with a greater preference for larger-but-delayed rewards

Willing to wait: Elevated reward-processing EEG activity associated with a greater preference for larger-but-delayed rewards

Neural connectivity in Internet gaming disorder and alcohol use disorder: A resting-state EEG coherence study

Relationship between Oscillatory Neuronal Activity during Reward Processing and Trait Impulsivity and Sensation Seeking

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