EEG indices of reward motivation and target detectability in a rapid visual detection task

Hughes, Gethin; Mathan, Santosh; Yeung, Nick

doi:10.1016/j.neuroimage.2012.09.003

Cited by 60 publications

(58 citation statements)

References 42 publications

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“…This is consistent with what is generally found in the literature regarding the influence of motivation on performance (Gilbert & Fiez, 2004;Hughes et al, 2013). The behavioural results also showed that accuracy on Stop trials was significantly lower in the motivational condition.…”

Section: Discussionsupporting

confidence: 92%

“…The relationship between the CNV and motivational manipulations has been inconsistent. Some studies found that the CNV amplitude in the response preparation interval is related to the level of motivation (Hughes, Mathan, & Yeung, 2013;Pierson, Ragot, Ripoche, & Lesevre, 1987;Walter, Winter, Cooper, Mccallum, & Aldridge, 1964) whereas others found no effect (Goldstein et al, 2006;Sobotka, Davidson, & Senulis, 1992).…”

Section: Introductionmentioning

confidence: 99%

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ERP evidence of cognitive strategy change in motivational conditions with varying level of difficulty

2015

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Recent research suggests that motivation improves cognitive functions but the particular mechanisms and precise behavioural conditions involved in such improvement still remain unknown. Particularly, it is unclear when in time and in which conditions these mechanisms are engaged. In the present study, we aimed to look at the neural markers of cognitive control strategies in different motivational conditions (motivation vs neutral) with different levels of difficulty (high vs low). Twenty-five adults completed a newly designed task in the four conditions above. Three ERP components were analysed: the CNV, LRP and P3b. We found that a motivational situation triggers the use of a proactive strategy when low cognitive control is required. A reactive strategy was used in a non-motivational situation and for difficult trials. Our study is also the first to provide evidence that the difference between proactive and reactive strategies occurs after the first stimulus (cue) is processed.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

ERP evidence of cognitive strategy change in motivational conditions with varying level of difficulty

2015

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“…The superior temporal resolution of EEG, compared to fMRI, allows researchers to more accurately dissociate neural-cognitive states that occur close to each other in time (Cohen, 2014; Luck, 2014), such as reward-anticipation and reward-outcome stages, as well as between different sub-stages within reward-anticipation itself (Brunia, Hackley, van Boxtel, Kotani, & Ohgami, 2011; Goldstein et al, 2006; McAdam & Seales, 1969). EEG, for instance, has been used to dissociate reward-anticipation from motor-preparation (Brunia et al, 2011; Hughes, Mathan, & Yeung, 2013), which has been a challenge in previous fMRI research on the relationship between reward-processing and delay discounting tendencies. As noted by Benningfield and colleagues (2014), for example, the fMRI MID task does not isolate motor-preparation processes from reward-anticipation processes.…”

Section: Introductionmentioning

confidence: 99%

“…Collectively, these findings suggest a relationship between alpha-suppression and anticipatory attention. More recently, stronger alpha suppression has been reported following monetary-reward cues and preceding monetary-reward feedback (Hughes et al, 2013). This additional suppression of alpha power by reward motivational cues suggests that alpha suppression indexes enhanced attentional processes during the anticipation of reward-related stimuli/feedback.…”

Section: Introductionmentioning

confidence: 99%

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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“…Research focused upon the FRN is not alone in raising questions about the interaction of reward and monitoring. While some research has suggested that the CNV is insensitive to reward (Goldstein et al, 2006), other work has demonstrated an influence of monetary incentives (Hughes et al, 2012). …”

Section: Introductionmentioning

confidence: 99%

Reward contingencies and the recalibration of task monitoring and reward systems: A high-density electrical mapping study

Morie¹,

Sanctis²,

Foxe³

2014

Neuroscience

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Task execution almost always occurs in the context of reward-seeking or punishment-avoiding behavior. As such, ongoing task monitoring systems are influenced by reward anticipation systems. In turn, when a task has been executed either successfully or unsuccessfully, future iterations of that task will be re-titrated on the basis of the task outcome. Here, we examined the neural underpinnings of the task-monitoring and reward-evaluation systems to better understand how they govern reward seeking behavior. Twenty-three healthy adult participants performed a task where they accrued points that equated to real world value (gift cards) by responding as rapidly as possible within an allotted timeframe, while success rate was titrated online by changing the duration of the timeframe dependent on participant performance. Informative cues initiated each trial, indicating the probability of potential reward or loss (four levels from very low to very high). We manipulated feedback by first informing participants of task success/failure, after which a second feedback signal indicated actual magnitude of reward/loss. High-density EEG recordings allowed for examination of event-related potentials (ERPs) to the informative cues and in turn, to both feedback signals. Distinct ERP components associated with reward cues, task preparatory and task monitoring processes, and reward feedback processes were identified. Unsurprisingly, participants displayed increased ERP amplitudes associated with task preparatory processes following cues that predicted higher chances of reward. They also rapidly updated reward and loss prediction information dependent on task performance after the first feedback signal. Finally, upon reward receipt, initial reward probability was no longer taken into account. Rather, ERP measures suggested that only the magnitude of actual reward or loss was now processed. Reward and task monitoring processes are clearly dissociable, but interact across very fast timescales to update reward predictions as information about task success or failure is accrued. Careful delineation of these processes will be useful in future investigations in clinical groups where such processes are suspected of having gone awry.

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EEG indices of reward motivation and target detectability in a rapid visual detection task

Cited by 60 publications

References 42 publications

ERP evidence of cognitive strategy change in motivational conditions with varying level of difficulty

ERP evidence of cognitive strategy change in motivational conditions with varying level of difficulty

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

Reward contingencies and the recalibration of task monitoring and reward systems: A high-density electrical mapping study

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