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Although the influence of stereotype threat (ST) on working self-concepts has been highlighted in recent years, its neural underpinnings are unclear. Notably, the aging ST, which largely influences older adults’ cognitive ability, mental and physical health, did not receive much attention. In order to investigate these issues, electroencephalogram (EEG) data were obtained from older adults during a modified Stroop task using neutral words, positive and negative self-concept words in aging ST vs. neutral control conditions. Results showed longer reaction times (RTs) for identifying colors of words under the aging ST compared to the neutral condition. More importantly, the negative self-concept elicited more positive late P300 amplitudes and enhanced theta band activities compared to the positive self-concept or neutral words under the aging ST condition, whereas no difference was found between these self-concepts and neutral words in the control condition. Furthermore, the aging ST induced smaller theta band synchronization and enhanced alpha band synchronization compared to the control condition. Moreover, we also observed valence differences in self-concepts where the negative self-concept words reduced early P150/N170 complex relative to neutral words. These findings suggest that priming ST could activate negative self-concepts as current working self-concept, and that this influence occurred during a late neural time course.

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Abstract. Previous research has suggested that power undermines cooperation in social dilemmas. However, the story may not be so simple. Guided by recent findings that power heightens sensitivity to unfairness, we examined the moderating effect of distributive justice on the association between power and cooperation. Across two experiments, when treated unfairly, high-power (vs. low-power) participants perceived greater unfairness. Moreover, high-power (vs. low-power) participants behaved less cooperatively not only when they interacted with the offender who treated them unfairly (Experiment 1), but also when they interacted with innocent third parties (Experiment 2). However, high-power and low-power participants showed no difference in perceived fairness and cooperation when treated fairly. These findings shed light on the association between power and cooperation by suggesting the modulating role of distributive justice, and they remind us that researchers should take participants’ personal sense of power into account when manipulating fairness.

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Previous research has demonstrated that some false recognitions are accompanied by retrieval of contexts in which the corresponding semantic associates were presented during the study phase. What kind of neural processes support context retrieval in false recognition? Using functional MRI (fMRI), we tested the hypothesis that context retrieval in false recognition might be supported by brain areas in the core recollection network which also support context retrieval in true recognition. During the study, lists of semantically associated words were presented together with scene pictures that served as contexts. During test, participants judged whether the presented word was old or new. If old, they judged which scene picture was its context and rated memory strength for the context. The results revealed that several regions in the core recollection network, including the bilateral hippocampus, left angular gyrus, and left anterior cingulate cortex, showed greater activations for true recognitions with strong context memory than those with weak context memory. More importantly, these same regions also showed greater activations for false recognitions with strong context memory than those with weak context memory. These findings helped account for why some false recognitions are vivid and accompanied by retrieval of context.

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To reduce the adverse effect of incorrect parameters for the traditional iterative tunable Q-factor wavelet transform, this paper proposes an iterative tunable Q-factor wavelet transform method for fault feature extraction. Firstly, before decomposing the bearing vibration signal by an iterative tunable Q-factor wavelet transform, the initial values of 3 basic factors should be set: the quality factor Q, redundancy r and the number of decomposition level J. Secondly, the kurtosis of a high resonance component, which is the result of an iterative tunable Q-factor wavelet transform, is calculated through multistep iteration until it meets the iteration stop condition. Finally, the envelope spectrum of the final low resonance component is calculated, and the type of bearing fault can be recognized according to the frequency of extreme points. The results show that this method can effectively suppress noise and in-band interference and avoid fault identification inaccuracies caused by improper parameters and can also identify the fault feature frequency more clearly. 1