The assessment of personality is crucial not only for scientific inquiries but also for real-world applications such as personnel selection. However, most existing ways to quantify personality traits rely on self-reported scales, which are susceptible to biases such as self-presentational concerns. In this study, we propose and evaluate a novel implicit measure of personality that uses machine learning (ML) algorithms to predict an individual's levels in the Big Five personality traits from 5 minutes of electroencephalography (EEG) recordings. Results from a large test sample of 196 volunteers indicated that the personality scores derived from the proposed measure converged significantly with a commonly used questionnaire, predicted behavioral indices and psychological adjustment in a manner similar to self-reported scores, and were relatively stable across time. These evaluations suggest that the proposed measure can serve as a viable alternative to conventional personality questionnaires in practice.
The Self-Attention Network (SAN) has been proposed to describe the underlying neural mechanism of the self-prioritization effect, yet the roles of the key nodes in the SAN-the left posterior superior temporal sulcus (LpSTS) and the dorsolateral prefrontal cortex (DLPFC)-still need to be clarified. One hundred and nine participants were randomly assigned into the LpSTS group, the DLPFC group, or the sham group. We used the transcranial magnetic stimulation (TMS) technique to selectively disrupt the functions of the corresponding targeted region, and observed its impacts on self-prioritization effect based on the difference between the performance of the self-matching task before and after the targeted stimulation. We analyzed both model-free performance measures and HDDM-based performance measures for the self-matching task. The results showed that the inhibition of LpSTS could lead to reduced performance in processing self-related stimuli, which establishes a causal role for the LpSTS in self-related processing and provide direct evidence to support the SAN framework. However, the results of the DLPFC group from HDDM analysis were distinct from the results based on response efficiency. Our investigation further the understanding of the differentiated roles of key nodes in the SAN in supporting the self-salience in information processing.
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