Event‐related potential activity in the basal ganglia differentiates rewards from nonrewards: Temporospatial principal components analysis and source localization of the feedback negativity: Commentary

Cohen, Michael X; Cavanagh, James F.; Slagter, Heleen A.

doi:10.1002/hbm.21358

Cited by 58 publications

(36 citation statements)

References 6 publications

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“…In contrast, localisation of the proposed FRN component to the ventral striatum, combined with the finding that it codes exclusively for +RPEs, converges with recent fMRI meta-analyses showing the striatum to be strongly +RPE biased (Bartra et al, 2013;Garrison et al, 2013;Liu, Hairston, Schrier, & Fan, 2011). In these meta-analyses, the areas of the striatum responding to +RPEs were also relatively large, increasing the plausibility of their producing a scalp detectable voltage, something which has previously been questioned (Cohen, Cavanagh, & Slagter, 2011). .…”

Section: Discussionsupporting

confidence: 63%

Principal components analysis of reward prediction errors in a reinforcement learning task

Sambrook

Goslin

2016

NeuroImage

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Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340 ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at ~330 ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found.

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Section: Discussionsupporting

confidence: 63%

Principal components analysis of reward prediction errors in a reinforcement learning task

Sambrook

Goslin

2016

NeuroImage

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“…The ACC and a second potential source in the basal ganglia appear to contribute to the FN in distinct ways, such that the former is associated with increased activity to monetary loss and the latter with increased activity to monetary gain; this interpretation is also consistent with the extant neuroimaging literature (Liu et al, 2011). As with any application of source analysis to ERP data, however, this result should be interpreted with some caution (Cohen et al, 2011a; Foti et al, 2011c) and further substantiated with complementary evidence from depth electrodes and lesion studies. For example, gain-related delta may reflect coordinated frontostriatal activation to rewards rather than a direct contribution of the striatum to the scalp-recorded signal per se.…”

Section: Discussionmentioning

confidence: 59%

Anterior cingulate activity to monetary loss and basal ganglia activity to monetary gain uniquely contribute to the feedback negativity

Foti¹,

Weinberg²,

Bernat³

et al. 2015

Clinical Neurophysiology

149

164

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Objective The feedback negativity (FN) is an event-related potential that differentiates unfavorable versus favorable outcomes. Although thought to reflect error-related activity within the anterior cingulate cortex, recent work indicates the FN may also reflect reward-related activity that has been linked to the basal ganglia. To date, it remains unclear how to reconcile these conflicting perspectives. Methods We decomposed the FN by applying time-frequency analysis to isolate activity unique to monetary losses and gains. The FN was recorded from 84 individuals during a laboratory gambling task. Results Two signals contributed to the FN elicited by unpredictable outcomes: theta activity (4-7 Hz) was increased following monetary loss, and delta activity (< 3 Hz) was increased following monetary gain. Predictable outcomes elicited delta but not theta activity. Source analysis revealed distinct generators, with loss-related theta localized to the anterior cingulate cortex and gain-related delta to a possible source in the striatum. Symptoms of depression, anxiety, and stress reactivity were specifically associated with blunted gain-related delta. Conclusions The FN may be a composite of loss- and gain-related neural activity, reflecting distinct facets of reward processing. Significance Gain-related delta activity may provide unique information about reward dysfunction in major depression and other internalizing psychopathology.

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“…Consequently, the observed interaction between task difficulty and reward in this component appears to be consistent with the results of the previous fMRI study of Krebs et al (2012) showing a very similar interaction pattern in the dopaminergic midbrain with highest activation levels in respond to cues that predicted both reward and high difficulty. Of course, it should be noted that ERP measurements will not directly reflect activity in deep brain structures such as the dopaminergic midbrain (Cohen et al, 2011), but only through cortical consequences of its involvement. This possible link to the dopaminergic system raises another alternative, or possibly supplementary, interpretation for the current results.…”

Section: Discussionmentioning

confidence: 99%

Task preparation processes related to reward prediction precede those related to task-difficulty expectation

et al. 2014

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Recently, attempts have been made to disentangle the neural underpinnings of preparatory processes related to reward and attention. Functional magnetic resonance imaging (fMRI) research showed that neural activity related to the anticipation of reward and to attentional demands invokes neural activity patterns featuring large-scale overlap, along with some differences and interactions. Due to the limited temporal resolution of fMRI, however, the temporal dynamics of these processes remain unclear. Here, we report an event-related potentials (ERP) study in which cued attentional demands and reward prospect were combined in a factorial design. Results showed that reward prediction dominated early cue processing, as well as the early and later parts of the contingent negative variation (CNV) slow-wave ERP component that has been associated with task-preparation processes. Moreover these reward-related electrophysiological effects correlated across participants with response-time speeding on reward-prospect trials. In contrast, cued attentional demands affected only the later part of the CNV, with the highest amplitudes following cues predicting high-difficulty potential-reward targets, thus suggesting maximal task preparation when the task requires it and entails reward prospect. Consequently, we suggest that task-preparation processes triggered by reward can arise earlier, and potentially more directly, than strategic top-down aspects of preparation based on attentional demands.

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Event‐related potential activity in the basal ganglia differentiates rewards from nonrewards: Temporospatial principal components analysis and source localization of the feedback negativity: Commentary

Cited by 58 publications

References 6 publications

Principal components analysis of reward prediction errors in a reinforcement learning task

Principal components analysis of reward prediction errors in a reinforcement learning task

Anterior cingulate activity to monetary loss and basal ganglia activity to monetary gain uniquely contribute to the feedback negativity

Task preparation processes related to reward prediction precede those related to task-difficulty expectation

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