Ying Hu

Extant research on design thinking is subjective and limited. This manuscript combines protocol analysis and electroencephalogram to read design thoughts in the core design activities of concept generation phase. The results suggest that alpha band power had event related synchronization in the scenario task and divergent thinking occupies a dominant position. However, it had event related desynchronization in analogy and inference activities, etc., and it is stronger for mental pressure and exercised cognitive processing. In addition, the parietooccipital area differs significantly from other brain areas in most design activities. This study explores the relationship of different design thinking and EEG data, which is innovative and professional in the field of design, providing a more objective data basis and evaluation method for future applied research and diverse educational practices.

Warning signs, as a type of safety signs, are widely applied in our daily lives to informing people about potential hazards and prompting safe behavior. Although previous studies have paid attention to the color of warning signs, they are mostly based on surveys and behavioral experiments. The neural substrates underlying the perception of warning signs with different background colors remain not clearly characterized. Therefore, this research is intended to address this gap with event-related potentials technique. Warning signs with three different background colors were used in the experiment. The results showed that the perceptual differences between different warning signs were present in the form of differential ERPs components though subjects were not required to explicitly attend to the warning signs.

l‐Lactate is emerging as a crucial regulatory nexus for energy metabolism in the brain and signaling transduction in synaptic plasticity, memory processes, and drug addiction instead of being merely a waste by‐product of anaerobic glycolysis. In this review, the role of lactate in various memory processes, synapse plasticity and drug addiction on the basis of recent studies is summarized and discussed. To this end, three main parts are presented: first, lactate as an energy substrate in energy metabolism of the brain is described; second, lactate as a novel signaling molecule in synaptic plasticity, neural circuits, memory, and drug addiction is described; and third, in light of the above descriptions, it is plausible to speculate that lactate is predominantly a signaling molecule in specific memory processes and partly acts as an energy substrate. The future perspective in lactate signaling involving microglia and associated precise signaling pathways in the brain is highlighted.

Faces provide information about a person's identity, as well as their sex, age, and ethnicity. People also infer social and personality traits from the face — judgments that can have important societal and personal consequences. In recent years, deep convolutional neural networks (DCNNs) have proven adept at representing the identity of a face from images that vary widely in viewpoint, illumination, expression, and appearance. These algorithms are modeled on the primate visual cortex and consist of multiple processing layers of simulated neurons. Here, we examined whether a DCNN trained for face identification also retains a representation of the information in faces that supports social‐trait inferences. Participants rated male and female faces on a diverse set of 18 personality traits. Linear classifiers were trained with cross validation to predict human‐assigned trait ratings from the 512 dimensional representations of faces that emerged at the top‐layer of a DCNN trained for face identification. The network was trained with 494,414 images of 10,575 identities and consisted of seven layers and 19.8 million parameters. The top‐level DCNN features produced by the network predicted the human‐assigned social trait profiles with good accuracy. Human‐assigned ratings for the individual traits were also predicted accurately. We conclude that the face representations that emerge from DCNNs retain facial information that goes beyond the strict limits of their training.