The effects of reward magnitude on reward processing: An averaged and single trial event-related potential study

Meadows, Caroline C.; Gable, Philip A.; Lohse, Keith R.; Miller, Matthew W.

doi:10.1016/j.biopsycho.2016.06.002

Cited by 82 publications

(62 citation statements)

References 31 publications

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“…We found a different pattern of results when examining the feedback-related negativity (FRN), which typically indexes the difference in feedback-locked activity for trials that resulted in negative compared to positive feedback 46 . Consistent with previous findings [47][48][49][50][51][52] , we found a reliable effect of receipt vs omission of reward on FRN amplitude (b = 0.80, p <.001), and this effect was enhanced for high reward trials (b = 0.81, p = .007; Table S9). However, in addition to this, and contrary to the hypothesis we preregistered based on previous findings 53, 54 , we found that effects of reward receipt vs omission on FRN amplitude were reduced for trials with high efficacy compared to those with low efficacy (b = -0.83, p = .007; Fig.…”

Section: Influences Of Incentives On Eeg Signatures Of Response and Fsupporting

confidence: 91%

When effort matters: Expectations of reward and efficacy guide cognitive control allocation

Frömer

Lin

Wolf

et al. 2020

Preprint

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14The amount of mental effort we invest in a task is influenced by the reward we can expect if we 15 perform that task well. However, some of the rewards that have the greatest potential for driving 16 these efforts (e.g., jobs, grants) are partly determined by factors outside of one's control. In such 17 cases, effort has more limited efficacy for achieving one's desired outcome. We have proposed 18 that people integrate information about the expected reward and efficacy for effort to determine 19 the expected value of control, and then adjust their control (i.e. mental effort) allocation 20 accordingly. Here we test key predictions this makes about behavior and neural activity. We 21show that participants invest more control in a task when this control is more rewarding and 22 which people evaluate the potential rewards to expect for a certain control allocation, much less 41 is known about how they evaluate the efficacy of that control, nor how these two values are 42 integrated to determine how much control is invested. 43We have recently developed a model that formalizes the roles of evaluation, decision-44 making, and motivation in allocating and adjusting cognitive control 17,18 ( Fig.1). Our model 45 describes how cognitive control can be allocated based on the overall worth of executing 46 different types and amounts of control, which we refer to as their Expected Value of Control 47 (EVC). The EVC of a given control allocation is determined by the extent to which the costs that 48 would need to be incurred (mental effort) are outweighed by the benefits. Critically, these 49 4 benefits are a function of both the expected outcomes for reaching one's goal (reward, e.g., 50 money or praise) and the likelihood that this goal will be reached with a given investment of 51 control (efficacy) (Fig. 1A). The amount of control invested is predicted to increase 52 monotonically with a combination of these two incentive components (Fig. 1B). 53 54 Figure 1. The EVC model predicts that control should increase with expected reward and efficacy. 55 A. EVC model. Control intensity is chosen to optimize the trade-off between effort costs and payoff, 56 maximizing the expected value of control. The payoff of a given control signal is determined by the 57 expected reward and efficacy for a given control intensity. B. Increasing control signal intensity (x-axis) 58is associated with greater effort costs (red) but often leads to greater payoffs (blue). The EVC of a given 59 control intensity (purple) increases with expected payoff and decreases with expected costs, determining 60 the optimal (EVC-maximizing) amount of control to invest in a task (black arrows). The model predicts 61 that control should increase when reaching a goal is more rewarding (left) and when control is more 62 efficacious for reaching that goal (right). 64The EVC model integrates over and formalizes past theories that posit roles for 65 reward/utility and/or efficacy/controllability/agency in the motivation to engage in a particular 66 course of actio...

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Section: Influences Of Incentives On Eeg Signatures Of Response and Fsupporting

confidence: 91%

When effort matters: Expectations of reward and efficacy guide cognitive control allocation

Frömer

Lin

Wolf

et al. 2020

Preprint

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“…Recent research has found that the RewP is sensitive to approach‐motivated states that occur during the pursuit of a goal (Meadows, Gable, Lohse, & Miller, 2016; Threadgill & Gable, 2016, 2018b). The current experiments found that anger, a high intensity approach‐motivated negative affect, enhances the RewP, especially when that goal brings about revenge.…”

Section: Discussionmentioning

confidence: 99%

Revenge is sweet: Investigation of the effects ofApproach‐Motivatedanger on theRewPin the motivated anger delay (MAD) paradigm

Threadgill

Gable

2020

Human Brain Mapping

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Past research has found that neural activity associated with feedback processing is enhanced by positive approach‐motivated states. However, no past work has examined how reward processing changes in the context of revenge. Using a novel aggression paradigm, we sought to explore the influence of approach‐motivated anger on neural responses to feedback indicating the opportunity to seek revenge against an offending opponent by examining the reward positivity (RewP), an event‐related potential indexing performance feedback. In Experiment 1, after receiving insulting feedback from an opponent, participants played a reaction time game with three trial types: revenge trials, aggravation trials, and no‐consequence trials. Results revealed that RewP amplitudes were larger to revenge trial win feedback than no‐consequence trial win feedback or revenge trial loss feedback. RewP amplitudes were larger to both aggravation trial win and loss feedback than on no‐consequence trials. Experiment 2 examined the influence of approach‐motivated anger during the acquisition of rewards on the RewP without the possibility of retribution from the offending individual. Participants played a reaction time game similar to Experiment 1, except instead of giving or receiving noise blasts, participants could win money from the insulter (revenge trials) or a neutral‐party (e.g., bank). Results indicated that revenge wins elicited larger RewP amplitudes than bank wins. These results suggest that anger enhances revenge‐related RewP amplitudes to obtaining revenge opportunities and further aggravation wins or losses. Anger appears to enhance the pleasurable feelings of revenge.

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“…The LPP follows the RewP in time, capturing later-stage outcome processing in reward tasks (30), and may reflect more downstream processing of output from the earlier evaluation systems (31). The LPP has frequently been examined using emotionpicture viewing tasks (28,(32)(33)(34), though more recent studies demonstrated the LPP's role in reward processing in healthy controls (HC) (35)(36)(37)(38)(39) and motivated attention to potential rewards or punishments in SZ (40). Blunted LPP following reward gains has also been associated with higher negative symptoms in a transdiagnostic sample that included schizophrenia-spectrum disorders (41).…”

Section: Introductionmentioning

confidence: 99%

Reward processing electrophysiology in schizophrenia: Effects of age and illness phase

Abram

Roach

Holroyd

et al. 2020

Preprint

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Background: Reward processing abnormalities may underlie characteristic pleasure and motivational impairments in schizophrenia. Some neural measures of reward processing show strong age-related modulation, highlighting the importance of considering age effects on reward sensitivity. We compared event-related potentials (ERPs) reflecting reward anticipation (stimulus-preceding negativity, SPN) and evaluation (reward positivity, RewP; late-positive potential, LPP) across individuals with schizophrenia (SZ) and healthy controls (HC), with an emphasis on examining effects of chronological age, brain age (i.e., predicted age based on neurobiological measures), and illness phase.Methods: Subjects underwent EEG while completing a slot-machine task for which rewards were not dependent on performance accuracy, speed, or other preparatory demands. Slot-machine task EEG responses were compared between 54 SZ and 54 HC individuals, ages 19 to 65. Reward-related ERPs were analyzed with respect to chronological age, categorically-defined illness phase (early; ESZ versus chronic schizophrenia; CSZ), and were used to model brain age relative to chronological age.Results: Illness phase-focused analyses indicated there were no group differences in average SPN or RewP amplitudes. However, a group x reward outcome interaction revealed that ESZ differed from HC in later outcome processing, reflected by greater LPP responses following loss versus reward (a reversal of the HC pattern). While brain age estimates did not differ among groups, depressive symptoms in SZ were associated with older brain age estimates while controlling for negative symptoms.Conclusions: ESZ and CSZ did not differ from HC in reward anticipation or early outcome processing during a cognitively undemanding reward task, highlighting areas of preserved functioning. However, ESZ showed altered later reward outcome evaluation, pointing to selective reward deficits during the early illness phase of schizophrenia. Further, an association between ERP-derived brain age and depressive symptoms in SZ extends prior findings linking depression with reward-related ERP blunting. Taken together, both illness phase and age may impact reward processing in SZ, and brain aging may offer a promising, novel marker of reward dysfunction that warrants further study.

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The effects of reward magnitude on reward processing: An averaged and single trial event-related potential study

Cited by 82 publications

References 31 publications

When effort matters: Expectations of reward and efficacy guide cognitive control allocation

When effort matters: Expectations of reward and efficacy guide cognitive control allocation

Revenge is sweet: Investigation of the effects ofApproach‐Motivatedanger on theRewPin the motivated anger delay (MAD) paradigm

Reward processing electrophysiology in schizophrenia: Effects of age and illness phase

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