Emotion toward anticipated and actual outcomes acts as a vital signal on emotional decision-making, and the Iowa Gambling Task (IGT) can mimic this decision-making process. Pain can impair emotional decision-making behaviors because it captures attention and distracts from the task at hand. Alternatively, pain may facilitate emotional decision-making behaviors by prompting alertness and mobilizing cognitive resources to maximize rewards. The present study investigated the influence of ongoing pain on emotional decision-making behaviors using the IGT. Our study recruited two groups of participants and applied capsaicin (pain group) or control cream (control group) to their forearms. We then compared performances and selections between the pain and control groups. The results revealed that participants successfully learned the required adaptive selection strategy as the task progressed. The study observed a tendency toward optimal choices for both groups under the condition of frequent-small losses. However, we observed a disadvantageous preference for the control group, but not the pain group, when faced with choices with infrequent but large losses. The study implies that a distressing pain experience motivates individuals to adjust goal-directed behaviors to maximize their rewards in a task. Thus, the finding suggests that ongoing pain facilitates emotional decision-making behaviors.

To investigate neural mechanisms of human psychology with electroencephalography (EEG), we typically instruct participants to perform certain tasks with simultaneous recording of their brain activities. The identification of task-related EEG responses requires data analysis techniques that are normally different from methods for analyzing resting-state EEG. This review aims to demystify commonly used signal processing methods for identifying task-related EEG activities for psychologists. To achieve this goal, we first highlight the different preprocessing pipelines between task-related EEG and resting-state EEG. We then discuss the methods to extract and visualize event-related potentials in the time domain and event-related oscillatory responses in the time-frequency domain. Potential applications of advanced techniques such as source analysis and single-trial analysis are briefly discussed. We conclude this review with a short summary of task-related EEG data analysis, recommendations for further study, and caveats we should take heed of.

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non-invasively. Some important mental functions could be encoded by resting-state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting-state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting-state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting-state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft-used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.