Relative Changes from Prior Reward Contingencies Can Constrain Brain Correlates of Outcome Monitoring

Mushtaq, Faisal; Stoet, Gijsbert; Bland, Amy R.; Schaefer, Alexandre

doi:10.1371/journal.pone.0066350

Cited by 21 publications

(33 citation statements)

References 75 publications

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“…Bunzeck, Dayan, Dolan, and Duzel (2010) found a similar scaling effect in a study of humans using fMRI. Schultz (2009) the FRN is accentuated when measured in the gain rather than loss domain (Kreussel et al, 2012;Kujawa, Smith, Luhmann, & Hajcak, 2013;Mushtaq, Stoet, Bland, & Schaefer, 2013;Sambrook, Roser, & Goslin, 2012;Yu & Zhang, 2014). This suggests the possibility of a neural dissociation of how outcomes are processed in gain and loss domains that is of broad 14 theoretical interest, not least because this reduced sensitivity for outcomes in the loss domain, or "loss indifference", is in direct opposition to the prediction of loss aversion made by prospect theory.…”

Section: Rpe Modulatormentioning

confidence: 99%

A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages.

Sambrook

Goslin

2015

Psychological Bulletin

334

388

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Economic approaches to decision making assume that people attach values to prospective goods and act in order to maximise their obtained value. Neuroeconomics strives to observe these values directly in the brain. A widely used valuation term in formal learning and decision-making models is the reward prediction error: the value of an outcome relative to its expected value. An influential theory (Holroyd and Coles, 2002) claims that an electrophysiological component, the feedback related negativity (FRN), codes a reward prediction error in the human brain. Such a component should be sensitive to both the prior likelihood of reward and its magnitude on receipt. A number of studies have found the FRN to be insensitive to reward magnitude, thus questioning the Holroyd and Coles account.However, because of marked inconsistencies in how the FRN is measured, a meaningful synthesis of this evidence is highly problematic. We conducted a meta-analysis of the FRN's response to both reward magnitude and likelihood using a novel method in which published effect sizes were disregarded in favour of direct measurement of the published waveforms themselves, with these waveforms then averaged to produce "great-grand averages". Under this standardised measure, the meta-analysis revealed strong effects of magnitude and likelihood on the FRN consistent with it encoding a reward prediction error. In addition, it revealed strong main effects of reward magnitude and likelihood across much of the waveform, indicating sensitivity to unsigned prediction errors or "salience". The great grand average technique is proposed as a general method for meta-analysis of ERPs.Keywords: Feedback related negativity (FRN); Event-related potential (ERP); Reward prediction error (RPE); Unsigned prediction error; Meta-analysis; Great grand average Masked Manuscript without Author Information 2 Explaining human behavior under choice requires understanding how humans assign value to goods and actions. This valuation occurs at a nexus of psychological influences running from high level processes such as framing effects and counterfactual comparisons down to basic physiological influences such as satiation. It is likely to be dependent on an individual's knowledge both through conscious extrapolation from experience and simple reinforcement learning.Early attempts to explain human valuation were aimed at demonstrating that choice was entirely rational, and embodied key axioms of neoclassical economics such as expected utility. This approach employed a black box methodology, observing the "revealed preferences" of outward behavior in favor of the underlying apparatus of valuation, and treating humans only "as if" they computed utilities (Friedman, 1953;Samuelson, 1937).These assumptions have come under attack from the field of behavioral economics, which has succeeded in documenting widespread and consistent deviations from rational choice. A fully psychological approach, behavioral economics has endeavored to open the black box and consider a more va...

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Section: Rpe Modulatormentioning

confidence: 99%

A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages.

Sambrook

Goslin

2015

Psychological Bulletin

334

388

View full text Add to dashboard Cite

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“…apparent differential sensitivity to the utility of þ RPEs and À RPEs might simply arise from differential sensitivity to either gain or loss outcomes generally. Indeed, in directly addressing sensitivity of the FRN in the two domains, Kreussel et al (2012), Kujawa, Smith, Luhmann, and Hajcak (2013), Mushtaq, Stoet, Bland, and Schaefer (2013), Sambrook, Roser, and Goslin (2012) and Yu and Zhang (2014) have all shown a relative insensitivity to the utility of RPEs expressed in the loss domain, with Holroyd, Larsen, and Cohen (2004) finding non-significant results in the same direction. This calls into question the validity of claims that the FRN is insensitive to the utility of À RPEs given that much of this evidence is based on mixed gambles.…”

Section: Introductionmentioning

confidence: 99%

Mediofrontal event-related potentials in response to positive, negative and unsigned prediction errors

Sambrook

Goslin

2014

Neuropsychologia

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Reinforcement learning models make use of reward prediction errors (RPEs), the difference between an expected and obtained reward. There is evidence that the brain computes RPEs, but an outstanding question is whether positive RPEs ("better than expected") and negative RPEs ("worse than expected") are represented in a single integrated system. An electrophysiological component, feedback related negativity, has been claimed to encode an RPE but its relative sensitivity to the utility of positive and negative RPEs remains unclear. This study explored the question by varying the utility of positive and negative RPEs in a design that controlled for other closely related properties of feedback and could distinguish utility from salience. It revealed a mediofrontal sensitivity to utility, for positive RPEs at 275-310ms and for negative RPEs at 310-390ms. These effects were preceded and succeeded by a response consistent with an unsigned prediction error, or "salience" coding.

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“…Thus, we propose that middle and large size stimuli led to more efficient initial outcome evaluation than small size ones. Future studies should always include several scoring methods to demonstrate the reliability of the reported results-a strategy that has already been implemented by some research groups (Banis & Lorist, 2012;Mushtaq, Stoet, Bland, & Schaefer, 2013;Pfabigan et al, 2015).…”

mentioning

confidence: 99%

Size does matter! Perceptual stimulus properties affect event‐related potentials during feedback processing

2015

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The current study investigated whether or not the physical aspect of stimulus size has an effect on neuronal correlates of feedback processing. A time estimation task was administered applying three different feedback stimulus categories: small, middle, and large size stimuli. Apart from early visual ERPs such as P1 and N1 components, later feedback processing stages were also affected by the size of feedback stimuli. In particular, small size stimuli compared to middle and large size ones led to diminished amplitudes in both FRN and P300 components, despite intact discrimination between negative and positive outcomes in these two ERPs. In contrast, time estimation performance was not influenced by feedback size. The current results indicate that small size feedback stimuli were perceived as less salient and hence were processed less deeply than the others. This suggests that future feedback studies could manipulate feedback salience simply by presenting differently sized feedback stimuli, at least when the focus lies on FRN and P300 amplitude variation.

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Relative Changes from Prior Reward Contingencies Can Constrain Brain Correlates of Outcome Monitoring

Cited by 21 publications

References 75 publications

A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages.

A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages.

Mediofrontal event-related potentials in response to positive, negative and unsigned prediction errors

Size does matter! Perceptual stimulus properties affect event‐related potentials during feedback processing

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