Functional near-infrared spectroscopy (fNIRS) is a fast-developing non-invasive functional brain imaging technology widely used in cognitive neuroscience, clinical research and neural engineering. However, it is a challenge to effectively remove the global physiological noise in the fNIRS signal. The global physiological noise in fNIRS arises from multiple physiological origins in both superficial tissues and the brain. It has complex temporal, spatial and frequency characteristics, casting significant influence on the results. In the present study, we developed a novel wavelet-based method for fNIRS global physiological noise removal. The method is data-driven and does not rely on any additional hardware or subjective noise component selection procedure. It consists of two steps. Firstly, we use wavelet transform coherence to automatically detect the time-frequency points contaminated by the global physiological noise. Secondly, we decompose the fNIRS signal by using the wavelet transform, and then suppress the wavelet energy of the contaminated time-frequency points. Finally, we transform the signal back to a time series. We validated the method by using simulation and real data at both task-and resting-state. The results showed that our method can effectively remove the global physiological noise from the fNIRS signal and improve the spatial specificity of the task activation and the resting-state functional connectivity pattern.

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Decision making is vital to human behavior and can be divided into multiple stages including option assessment, behavioral output, and feedback evaluation. Studying how people evaluate option characteristics in the option assessment stage would provide important knowledge on human decision making. Using the event‐related potential (ERP) method, the present study investigated the neural mechanism of evaluating two types of option characteristics (i.e., reward magnitude and degree of uncertainty) in the temporal dimension. Thirty‐five volunteers participated in a monetary gambling task, where they either accepted or rejected gambles. The ERP results showed a double dissociation pattern, with the early P1 component being sensitive to magnitude but insensitive to degree of uncertainty, while both the N2 and P3 components showed the opposite pattern. The results suggest that these two fundamental option features are assessed rapidly and separately in the human brain. Specifically, small magnitude elicited a larger P1 than did large magnitude, indicating that the perceptual and attentional processing of options is modulated by magnitude. Both the N2 and P3 amplitudes evoked by the risky context were larger than those evoked by the ambiguous one, reflecting that more cognitive conflicts and resources are involved in the former condition. Furthermore, the P1, but not the N2 or P3, amplitude was sensitive to decisions, suggesting that early attentional processes may contribute to human decision making. These findings may provide insight into the temporal mechanisms of option characteristic processing.

Social misalignment occurs when a person’s attitudes and opinions deviate from those of others. We investigated how individuals react to social misalignment in risky (outcome probabilities are known) or ambiguous (outcome probabilities are unknown) decision contexts. During each trial, participants played a forced-choice gamble, and they observed the decisions of four other players after they made a tentative decision, followed by an opportunity to keep or change their initial decision. Behavioral and event-related potential data were collected. Behaviorally, the stronger the participants’ initial preference, the less likely they were to switch their decisions; whereas the more their decisions were misaligned with the majority, the more likely they were to switch. Electrophysiological results showed a hierarchical processing pattern of social misalignment. Misalignment was first detected binarily (i.e., match/mismatch) at an early stage, as indexed by the N1 component. During the second stage, participants became sensitive to low levels of misalignment, which were indexed by the feedback-related negativity. The degree of social misalignment was processed in greater detail, as indexed by the P3 component. Moreover, such hierarchical neural sensitivity is generalizable across different decision contexts (i.e., risky and ambiguous). These findings demonstrate a fine-grained neural sensitivity to social misalignment during decision-making under uncertainty.

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