Learning of irrelevant stimulus-response associations modulates cognitive control.

It has been shown that manipulating the proportion of congruent to incongruent trials in conflict tasks (e.g., Stroop, Simon, and flanker tasks) can vary the size of conflict effects, however, by two different mechanisms. One theory is the control learning account (the brain learns the probability of conflict and uses it to proactively adjust the control demand for future trials). The other is the irrelevant stimulus-response learning account (the brain learns the probability of irrelevant stimulus-response associations and uses it to prepare responses). Previous fMRI studies have detected the brain regions that contribute to the control-learning-modulated conflict effects, but it is less known what neural substrates underlie the conflict effects modulated by irrelevant S-R learning. We here investigated this question with a model-based fMRI study, in which the proportion of congruent to incongruent trials changed dynamically in the Simon task and the models learned the probability of irrelevant S-R associations quantitatively. Behavioral analyses showed that the unsigned prediction errors (PEs) of responses generated by the learning models correlated with reaction times irrespective of congruent and incongruent trials, indicating that large unsigned PEs associated with slow responses. The fMRI results showed that the regions of fronto-parietal and cingulo-opercular network involved in cognitive control were significantly modulated by the unsigned PEs, also irrespective of congruent and incongruent trials, indicating that large unsigned PEs associated with transiently increased activity in these regions. These results together suggest that learning of irrelevant S-R associations modulates reactive control, which demonstrates a new way to modulate cognitive control compared to the control learning account.

Learning Representative Features by Deep Attention Network for 3D Point Cloud Registration.

Three-dimensional point cloud registration, which aims to find the transformation that best aligns two point clouds, is a widely studied problem in computer vision with a wide spectrum of applications, such as underground mining. Many learning-based approaches have been developed and have demonstrated their effectiveness for point cloud registration. Particularly, attention-based models have achieved outstanding performance due to the extra contextual information captured by attention mechanisms. To avoid the high computation cost brought by attention mechanisms, an encoder-decoder framework is often employed to hierarchically extract the features where the attention module is only applied in the middle. This leads to the compromised effectiveness of the attention module. To tackle this issue, we propose a novel model with the attention layers embedded in both the encoder and decoder stages. In our model, the self-attentional layers are applied in the encoder to consider the relationship between points inside each point cloud, while the decoder utilizes cross-attentional layers to enrich features with contextual information. Extensive experiments conducted on public datasets prove that our model is able to achieve quality results on a registration task.

The Influence of Medical Professional Knowledge on Empathy for Pain: Evidence From fNIRS.

Empathy is a mental ability that allows one person to understand the mental and emotional state of another and determines how to effectively respond to that person. When a person receives cues that another person is in pain, neural pain circuits within the brain are activated. Studies have shown that compared with non-medical staff, medical practitioners present lower empathy for pain in medical scenarios, but the mechanism of this phenomenon remains in dispute. This work investigates whether the neural correlates of empathic processes of pain are altered by professional medical knowledge. The participants were 16 medical students who were enrolled at a Chinese medical college and 16 non-medical students who were enrolled at a normal university. Participants were scanned by functional near-infrared spectroscopy while watching pictures of medical scenarios that were either painful or neutral situations. Subjects were asked to evaluate the pain intensity supposedly felt by the model in the stimulus displays, and the Interpersonal Reactivity Index-C (IRI-C) questionnaire was used to measure the empathic ability of participants. The results showed that there is no significant difference between medical professional and non-medical professional subjects in IRI-C questionnaire scores. The subjects of medical professions rated the pain degree of medical pictures significantly lower than those of non-medical professions. The activation areas in non-medical subjects were mainly located in the dorsolateral prefrontal cortex, frontal polar regions, posterior part of the inferior frontal gyrus, supramarginal gyrus, supplementary somatosensory cortex and angular gyrus, whereas there was a wide range of activation in the prefrontal lobe region in addition to the somatosensory cortex in medical professionals. These results indicate that the process of pain empathy in medical settings is influenced by medical professional knowledge.