Give it a second try? The influence of feedback and performance in the decision of reattempting.

Feedback evaluation can affect behavioural continuation or discontinuation, and is essential for cognitive and motor skill learning. One critical factor that influences feedback evaluation is participants' internal estimation of self-performance. Previous research has shown that two event-related potential components, the Feedback-Related Negativity (FRN) and the P3, are related to feedback evaluation. In the present study, we used a time estimation task and EEG recordings to test the influence of feedback and performance on participants' decisions, and the sensitivity of the FRN and P3 components to those factors. In the experiment, participants were asked to reproduce the total duration of an intermittently presented visual stimulus. Feedback was given after every response, and participants had then to decide whether to retry the same trial and try to earn reward points, or to move on to the next trial. Results showed that both performance and feedback influenced participants' decision on whether to retry the ongoing trial. In line with previous studies, the FRN showed larger amplitude in response to negative than to positive feedback. Moreover, our results were also in agreement with previous works showing the relationship between the amplitude of the FRN and the size of feedback-related prediction error (PE), and provide further insight in how PE size influences participants' decisions on whether or not to retry a task. Specifically, we found that the larger the FRN, the more likely participants were to base their decision on their performance - choosing to retry the current trial after good performance or to move on to the next trial after poor performance, regardless of the feedback received. Conversely, the smaller the FRN, the more likely participants were to base their decision on the feedback received.

Action effect predictions in 'what', 'when', and 'whether' intentional actions.

It has been proposed that intentional action can be separated into three major types depending on the nature of the action choice - what (selecting what to do), when (selecting when to act) and whether (to perform the action or not). While many theories on action control assume that intentional action involves the prediction of action effects, there has not been any attempt to compare the three types of intentional actions (what, when, whether) with respect to action-effect prediction. Here, we employ an action-effect prediction paradigm where participants select the action on every trial based on either the what (choosing between alternative actions), when (choosing to respond at different time points) or whether (choosing to perform an action or not) action components, and each action choice is followed by either a predicted (standard) or a mispredicted (deviant) tone. We found a significant P2 difference between standard/deviant tones reflecting the formation of action-effect predictions regardless of whether the action choice was based on the 'what', 'when' or 'whether' decision. Furthermore, our analysis revealed that this P2 difference for the prediction effect was not observable in non-action trials within the 'whether' condition, which suggests an action-specific prediction process.

Learning something new versus changing your ways: Distinct effects on midfrontal oscillations and cardiac activity for learning and flexible adjustments.

We need to be able to learn new behaviour, but also be capable of changing existing routines, when they start conflicting with our long-term goals. Little is known about to what extent blank-slate learning of new and adjustment of existing behavioural routines rely on different neural and bodily mechanisms. In the current study, participants first acquired novel stimulus-response contingencies, which were subsequently randomly changed to create the need for flexible adjustments. We measured midfrontal theta oscillations via EEG as an indicator of neural conflict processing, as well as heart rate as a proxy of autonomic activity. Participants' trial-wise learning progress was estimated via computation modelling. Theta power and heart rate significantly differed between correct and incorrect trials. Differences between correct and incorrect trials in both neural and cardiac feedback processing were more pronounced for adjustments compared to blank-slate learning. This indicates that both midfrontal and cardiac processing are sensitive to changes in stimulus-response contingencies. Increases in individual learning rates predicted lower impact of performance feedback on midfrontal theta power, but higher impact on heart rate. This suggests that cardiac and midfrontal reactivity are partially reflective of different mechanisms related to feedback learning. Our results shed new light on the role of neural and autonomic mechanisms for learning and behavioural adjustments.