When is an error not a prediction error? An electrophysiological investigation

Holroyd, Clay B.; Krigolson, Olave E.; Baker, R.; Lee, Seung; Gibson, Jessica

doi:10.3758/cabn.9.1.59

Cited by 159 publications

(179 citation statements)

References 72 publications

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“…Several ERP studies have examined the effects of experience on the FRN, however. These studies have consistently found that the FRN is sensitive to outcome valence and likelihood (14)(15)(16)(17)(18)(19)(22)(23)(24)(25)(26). We replicated these results in the no instruction condition and showed that the FRN can evolve in the absence of behavioral change in the instruction condition.…”

Section: Discussionsupporting

confidence: 71%

“…Because neural responses depended only on the selected option (i.e., neural responses after the 66 cue did not depend on whether it was paired with the 33 or the 0 cue, and neural responses after the 33 cue did not depend on whether it was paired with the 66 or the 0 cue), we excluded the factor of cue pair from further analyses. To isolate the FRN, we compared losses and wins that were equally likely (16,26). We created a probable outcome difference wave (losses after 33 cues minus wins after 66 cues) and an improbable outcome difference wave (losses after 66 cues minus wins after 33 cues).…”

Section: Methodsmentioning

confidence: 99%

“…[This hypothesis rests on the assumption that the FRN tracks a neural learning signal (for reviews, see refs. 22 and 24), an assumption that is strengthened by the fact that the FRN is maximal when participants perceive a relationship between their actions and trial outcomes (25,26).] Second, if instruction does not disengage reward valuation regions, and if information provided by instruction penetrates reward valuation regions, an FRN will appear in both conditions.…”

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confidence: 99%

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Modulation of the feedback-related negativity by instruction and experience

Walsh

Anderson

2011

Proc. Natl. Acad. Sci. U.S.A.

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A great deal of research focuses on how humans and animals learn from trial-and-error interactions with the environment. This research has established the viability of reinforcement learning as a model of behavioral adaptation and neural reward valuation. Error-driven learning is inefficient and dangerous, however. Fortunately, humans learn from nonexperiential sources of information as well. In the present study, we focused on one such form of information, instruction. We recorded event-related potentials as participants performed a probabilistic learning task. In one experiment condition, participants received feedback only about whether their responses were rewarded. In the other condition, they also received instruction about reward probabilities before performing the task. We found that instruction eliminated participants' reliance on feedback as evidenced by their immediate asymptotic performance in the instruction condition. In striking contrast, the feedback-related negativity, an event-related potential component thought to reflect neural reward prediction error, continued to adapt with experience in both conditions. These results show that, whereas instruction may immediately control behavior, certain neural responses must be learned from experience.R einforcement learning (RL) formalizes the notion that humans and animals learn from trial-and-error interactions with the environment (1). According to many RL models, differences between actual and expected outcomes, or reward prediction errors, provide teaching signals. These signals convey information about the magnitude and valence of the difference between actual and expected rewards. By using reward prediction errors to revise expectations, RL models increasingly select advantageous actions. Behavioral studies furnished early support for RL in the form of the "law of effect" (2). This law states that actions that are followed by rewards will be repeated. Single-cell recordings from animals provided further support by showing that responses of midbrain dopamine neurons to outcomes scale according to the differences between actual and expected rewards (3). Neuroimaging experiments have since extended this result to humans by demonstrating that blood-oxygen level-dependent (BOLD) responses in the striatum and prefrontal cortex also mirror reward prediction errors (4).On the basis of these findings, RL has emerged as a prominent theory of behavioral adaptation and neural reward valuation. As it stands, however, RL is an incomplete theory. Individuals learn from nonexperiential sources of information as well. For example, by using language to acquire knowledge about outcome likelihoods, humans can avoid costly mistakes. This raises the question, How does information provided by instruction mediate trial-and-error learning?Several theories seek to explain how the brain uses instruction and experience to select actions (5-8). These theories agree that instruction engages the prefrontal cortex and medial temporal lobes (PFC/MTL), whereas experience engages ...

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Section: Discussionsupporting

confidence: 71%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Modulation of the feedback-related negativity by instruction and experience

Walsh

Anderson

2011

Proc. Natl. Acad. Sci. U.S.A.

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“…In humans, FRPs follow several properties predicted by reinforcement learning rules and, in particular, a sensitivity to outcome predictability (Frank et al, 2005;Cohen et al, 2007;Eppinger et al, 2008;Holroyd et al, 2009). The description of a larger FRP for the first, unsure, correct trial compared to certain correct trials in repetition also concurs with such sensitivity to outcome predictability.…”

Section: Feedback-related Potentials and Expectationssupporting

confidence: 55%

Frontal Feedback-Related Potentials in Nonhuman Primates: Modulation during Learning and under Haloperidol

Vezoli

Procyk²

2009

J. Neurosci.

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Feedback monitoring and adaptation of performance involve a medial reward system including medial frontal cortical areas, the medial striatum, and the dopaminergic system. A considerable amount of data has been obtained on frontal surface feedback-related potentials (FRPs) in humans and on the correlate of outcome monitoring with single unit activity in monkeys. However, work is needed to bridge knowledge obtained in the two species. The present work describes FRPs in monkeys, using chronic recordings, during a trial and error task. We show that frontal FRPs are differentially sensitive to successes and failures and can be observed over long-term periods. In addition, using the dopamine antagonist haloperidol we observe a selective effect on FRP amplitude that is absent for pure sensoryrelated potentials. These results describe frontal dopaminergic-dependent FRPs in monkeys and corroborate a human-monkey homology for performance monitoring signals.

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“…In gambling, feedback is not useful for behavioral adjustments and therefore participants may not generate expectancies. At best, their expectancies are hard to predict (Hajcak et al, 2007;Holroyd et al, 2009). Additionally, in many gambling studies explicitly addressing the role of valence, positive and negative feedback both occur in 50% of cases and thus are equally expected (Gehring and Willoughby, 2002;Hajcak et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

The Processing of Unexpected Positive Response Outcomes in the Mediofrontal Cortex

et al. 2012

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The human mediofrontal cortex, especially the anterior cingulate cortex, is commonly assumed to contribute to higher cognitive functions like performance monitoring. How exactly this is achieved is currently the subject of lively debate but there is evidence that an event's valence and its expectancy play important roles. One prominent theory, the reinforcement learning theory by Holroyd and colleagues (2002, 2008), assigns a special role to feedback valence, while the prediction of response-outcome (PRO) model by Alexander and Brown (2010, 2011) claims that the mediofrontal cortex is sensitive to unexpected events regardless of their valence. However, paradigms examining this issue have included confounds that fail to separate valence and expectancy.In the present study, we tested the two competing theories of performance monitoring by using an experimental task that separates valence and unexpectedness of performance feedback. The feedback-related negativity of the event-related potential, which is commonly assumed to be a reflection of mediofrontal cortex activity, was elicited not only by unexpected negative feedback, but also by unexpected positive feedback. This implies that the mediofrontal cortex is sensitive to the unexpectedness of events in general rather than their valence and by this supports the PRO model.

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When is an error not a prediction error? An electrophysiological investigation

Cited by 159 publications

References 72 publications

Modulation of the feedback-related negativity by instruction and experience

Modulation of the feedback-related negativity by instruction and experience

Frontal Feedback-Related Potentials in Nonhuman Primates: Modulation during Learning and under Haloperidol

The Processing of Unexpected Positive Response Outcomes in the Mediofrontal Cortex

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