Implicit predictions of future rewards and their electrophysiological correlates

Trimber, Elizabeth M.; Luhmann, Christian C.

doi:10.1016/j.bbr.2017.07.005

Cited by 6 publications

(9 citation statements)

References 56 publications

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“…Late losses could signal that the current choice might need re-evaluation in order to stay on track of the most rewarding stimulus choice. This interpretation ties in with findings that report increased LPC amplitudes in response to unexpected losses (Trimber & Luhmann, 2017;Muller-Gras et al, 2019, Donaldson et al, 2016, and links between the LPC and subsequent behavioral adjustments (San Martín et al, 2013;Chase et al, 2011). Late gain feedback, on the other hand, does not provide any additional or new information about the context or the choice that is being made and no updating is needed, hence LPC amplitudes become smaller.…”

Section: Discussionsupporting

confidence: 66%

“…The LPC has originally been studied in the context of affective processing . In the context of stimulus-reward learning, LPC amplitudes were observed to be larger in response to losses than to gains (Trimber & Luhmann, 2017). Furthermore, LPC amplitudes were found to be sensitive to reward expectancy, with largest amplitudes elicited by unexpected losses (Trimber & Luhmann, 2017;Muller-Gras et al, 2019, Donaldson et al, 2016.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of stimulus-reward learning, LPC amplitudes were observed to be larger in response to losses than to gains (Trimber & Luhmann, 2017). Furthermore, LPC amplitudes were found to be sensitive to reward expectancy, with largest amplitudes elicited by unexpected losses (Trimber & Luhmann, 2017;Muller-Gras et al, 2019, Donaldson et al, 2016. In addition, LPC amplitudes in response to losses are found to be linked to subsequent behavioral adjustments, suggesting that the LPC plays a role in behavioral optimization based on feedback (San Martín et al, 2013;von Borries et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The FRN is followed by the late positive component (LPC), a positive-going wave, with maxima occurring at parieto-central electrode sites, which starts around 400 ms after stimulus presentation (Trimber & Luhmann, 2017;Muller-Gras et al, 2019). The LPC has originally been studied in the context of affective processing .…”

Section: Introductionmentioning

confidence: 99%

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Where is the money? Dynamics in feedback processing and attention during spatial probabilistic learning

Pütz

Berg

Lorist

2021

Preprint

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Learned feature-based stimulus-reward-associations can modulate behavior and the underlying neural processing of information. In our study, we investigated the neurocognitive mechanisms underlying learning of spatial stimulus-reward-associations. Participants performed a probabilistic spatial reward-learning task that required participants, within 40 trials, to learn which out of four locations on a computer screen yielded the most gain-feedback when chosen. Our behavioral findings show that participants learned to choose which location was most rewarding. Those findings were paralleled by significant amplitude differences in event-related potentials (ERPs) elicited by the presentation of loss and gain feedback; the amplitude of the feedback-related negativity (FRN) was more negative in response to loss feedback compared to gain feedback, but showed no modulation by trial-number. On the other hand, the late positive component (LPC), became larger in response to losses as the learning-set progressed, but smaller in response to gains. Additionally, immediately following feedback presentation, brain activity in the visual cortex - read out through alpha frequency oscillations measured over occipital sites - was predictive of the amplitude of the N2pc ERP component, a marker of spatial attention orienting, observed on the next trial. Taken together, we elucidated neurocognitive dynamics underlying feedback processing in spatial reward learning, and the subsequent effects that spatial stimulus-reward association learning have on spatial attention.

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Section: Discussionsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Where is the money? Dynamics in feedback processing and attention during spatial probabilistic learning

Pütz

Berg

Lorist

2021

Preprint

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“…In terms of latency, the LP observed in our data is consistent with the late positive potential ('LPP'), a feedback component related to emotional processing. However, on a spatial basis, the LPP typically follows a posterior scalp distribution associated with visual cortical areas 25,116 (see 130 for a more frontal distribution). The LPP also displays a positive bias for negative stimuli or losses in emotion as well as in value-based decision making studies 25,116,130,131 , whereas the observed LP showed more positive values for positive outcomes instead.…”

Section: Electrophysiological Responses To Presented Feedbackmentioning

confidence: 99%

Neural processing of iterated prisoner’s dilemma outcomes indicates next-round choice and speed to reciprocate cooperation

Constantino

Garat

Nicolaisen-Sobesky

et al. 2019

Preprint

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Cooperation upholds life in organized societies, but its neurobiological mechanisms remain unresolved. Recent theoretical analyses have contrasted cooperation by its fast versus slower modes of decisionmaking. This raises the question of the neural timescales involved in the integration of decision-related information, and of the participating neural circuits. Using time-resolved electroencephalography (EEG) methods, we characterized relevant neural signatures of feedback processing at the iterated prisoner's dilemma (iPD), an economic task that addresses cooperation-based exchange between social 1 5 10 15 20

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Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing

Glazer¹,

Kelley²,

Pornpattananangkul

et al. 2018

International Journal of Psychophysiology

244

262

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Most reward-related electroencephalogram (EEG) studies focus exclusively on the feedback-related negativity (FRN, also known as feedback negativity or FN, medial-frontal negativity or MFN, feedback error-related negativity or fERN, and reward positivity or RewP). This component is usually measured approximately 200-300 ms post-feedback at a single electrode in the frontal-central area (e.g., Fz or FCz). The present review argues that this singular focus on the FRN fails to leverage EEG's greatest strength, its temporal resolution, by underutilizing the rich variety of event-related potential (ERP) and EEG time-frequency components encompassing the wider temporal heterogeneity of reward processing. The primary objective of this review is to provide a comprehensive understanding of often overlooked ERP and EEG correlates beyond the FRN in the context of reward processing with the secondary goal of guiding future research toward multistage experimental designs and multicomponent analyses that leverage the temporal power of EEG. We comprehensively review reward-related ERPs (including the FRN, readiness potential or RP, stimulus-preceding negativity or SPN, contingent-negative variation or CNV, cue-related N2 and P3, Feedback-P3, and late-positive potential or LPP/slow-wave), and reward-related EEG time-frequency components (changes in power at alpha, beta, theta, and delta bands). These electrophysiological signatures display distinct time-courses, scalp topographies, and reflect independent psychological processes during anticipatory and/or outcome stages of reward processing. Special consideration is given to the time-course of each component and factors that significantly contribute to component variation. Concluding remarks identify current limitations along with recommendations for potential important future directions.

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Implicit predictions of future rewards and their electrophysiological correlates

Cited by 6 publications

References 56 publications

Where is the money? Dynamics in feedback processing and attention during spatial probabilistic learning

Where is the money? Dynamics in feedback processing and attention during spatial probabilistic learning

Neural processing of iterated prisoner’s dilemma outcomes indicates next-round choice and speed to reciprocate cooperation

Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing

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