Performance feedback during learning is accompanied by a negative event-related potentials (ERP) component, the feedback-related negativity (FRN), which codes a reward prediction error. An open issue relates to the coding of feedback stimuli in observational learning. The present study aimed to determine differences in the neural processing of feedback in active and observational learners in a between-subjects design. By choosing between different stimuli, 15 active learners could learn a rule determining the probability of monetary reward. Each of the 15 observers was yoked to the performance of one active learner. In test trials, observers could prove whether they had gained insight into the rule. Although both groups learned at a comparable rate, FRN amplitudes following negative feedback were significantly reduced in observational relative to active learners, whereas there was no difference for the FRN in response to positive feedback. Additionally, between-group differences were already observed in the time window preceding the FRN, between 150 and 220 ms after feedback onset. The processing of feedback stimuli thus depends upon the direct relevance for one's own action planning. The FRN as an error signal indicating the need for behavioral adaptation appears to be especially relevant, if negative feedback is linked to agency.

Processing of performance-related feedback is an essential prerequisite for adaptive behavior. Even though in everyday life feedback is rarely immediate, to date very few studies have investigated whether the feedback-related negativity (FRN), a relative negativity in the ERP approximately 200 to 300 ms after feedback that is sensitive to feedback valence and predictability, is modulated by feedback timing, and findings are inconsistent. The present study investigated effects of gradually increasing feedback delays on feedback processing in the FRN time window. Subjects completed a probabilistic learning task in which feedback was provided after short, intermediate, or long delays. Difference wave-based analyses showed that amplitudes decreased linearly with increasing feedback delay. A distinct pattern was observed for the FRN as defined in the original waveforms, with FRN amplitudes being largest for long and smallest for short delays. This pattern of results is consistent with the notion that the neural systems underlying feedback processing vary depending on feedback timing. The gradually reduced difference wave signal might reflect a gradual shift away from processing in frontostriatal circuits toward medial temporal involvement. To what extent increased signal amplitudes for longer delays in the original waveforms are related to processing in certain brain structures will need to be determined in future studies.

Both execution and observation of erroneous actions have been shown to increase the activity of the anterior cingulate cortex (ACC) as reflected in characteristic event-related potential (ERP) components labelled error-related negativity (ERN) and observer error-related negativity (oERN), respectively. Whereas these labels implicate a modulation of both components by response accuracy, recent findings suggest a more general involvement of the ACC in the detection of unexpected events. In previous studies, a lower frequency of erroneous as compared with correct observed actions resulted in lower expectation of erroneous actions. The present study investigates whether ERPs following observed actions are modulated by response accuracy or violation of expectation. Sixteen human subjects observed a virtual person whose actions in a game were expected or unexpected. Action expectation was independent of accuracy. In both conditions, subjects observed correct and incorrect actions equally often. Whereas ERPs were not modulated by accuracy, we found an enhanced amplitude of a negative frontocentral ERP component in the time window of the oERN for unexpected as compared with expected observed actions, which we suggest reflects an action prediction error. These results propose that the function of the ACC in performance monitoring depends less on accuracy of actions but rather on predictions and their violations. Future research will have to clarify whether the present ERP modulations revealed a feature of the oERN or whether they represent a distinct component.

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Feedback to both actively performed and observed behaviour allows adaptation of future actions. Positive feedback leads to increased activity of dopamine neurons in the substantia nigra, whereas dopamine neuron activity is decreased following negative feedback. Dopamine level reduction in unmedicated Parkinson’s Disease patients has been shown to lead to a negative learning bias, i.e. enhanced learning from negative feedback. Recent findings suggest that the neural mechanisms of active and observational learning from feedback might differ, with the striatum playing a less prominent role in observational learning. Therefore, it was hypothesized that unmedicated Parkinson’s Disease patients would show a negative learning bias only in active but not in observational learning. In a between-group design, 19 Parkinson’s Disease patients and 40 healthy controls engaged in either an active or an observational probabilistic feedback-learning task. For both tasks, transfer phases aimed to assess the bias to learn better from positive or negative feedback. As expected, actively learning patients showed a negative learning bias, whereas controls learned better from positive feedback. In contrast, no difference between patients and controls emerged for observational learning, with both groups showing better learning from positive feedback. These findings add to neural models of reinforcement-learning by suggesting that dopamine-modulated input to the striatum plays a minor role in observational learning from feedback. Future research will have to elucidate the specific neural underpinnings of observational learning.