What happens when right means wrong? The impact of conflict arising from competing feedback responses.

Humans often rely on feedback to learn. Indeed, in learning the difference between feedback and an expected outcome is computed to inform future actions. Further, recent work has found that reward and feedback have a unique role in modulating conflict processing and cognitive control. However, it is still not clear how conflict, especially concerning the processing and evaluation of feedback, impacts learning. To address this, we examined the effects of feedback competition on feedback evaluation in a reinforcement learning task. Specifically, we had participants play a simple two-choice gambling game while electroencephalographic (EEG) data were recorded. On half of the experiment blocks, we reversed the meaning of performance feedback for each trial from its prepotent meaning to induce response conflict akin to the Stroop effect (e.g., '✓' meant incorrect). Behaviourally, we found that participants' accuracy was reduced as a result of incongruent feedback. Paralleling this, an analysis of our EEG revealed that incongruent feedback resulted in a reduction in amplitude of the reward positivity and the P300, components of the human event-related brain potential implicated in reward processing. Our results demonstrate the negative impact of conflict on feedback evaluation and the impact of this on subsequent performance.

Using Muse: Rapid Mobile Assessment of Brain Performance.

The advent of mobile electroencephalography (mEEG) has created a means for large scale collection of neural data thus affording a deeper insight into cognitive phenomena such as cognitive fatigue. Cognitive fatigue - a neural state that is associated with an increased incidence of errorful performance - is responsible for accidents on a daily basis which at times can cost human lives. To gain better insight into the neural signature of cognitive fatigue in the present study we used mEEG to examine the relationship between perceived cognitive fatigue and human-event related brain potentials (ERPs) and electroencephalographic (EEG) oscillations in a sample of 1,000 people. As a secondary goal, we wanted to further demonstrate the capability of mEEG to accurately measure ERP and EEG data. To accomplish these goals, participants performed a standard visual oddball task on an Apple iPad while EEG data were recorded from a Muse EEG headband. Counter to traditional EEG studies, experimental setup and data collection was completed in less than seven minutes on average. An analysis of our EEG data revealed robust N200 and P300 ERP components and neural oscillations in the delta, theta, alpha, and beta bands. In line with previous findings we observed correlations between ERP components and EEG power and perceived cognitive fatigue. Further, we demonstrate here that a linear combination of ERP and EEG features is a significantly better predictor of perceived cognitive fatigue than any ERP or EEG feature on its own. In sum, our results provide validation of mEEG as a viable tool for research and provide further insight into the impact of cognitive fatigue on the human brain.