Remote physiological signal recovery with efficient spatio-temporal modeling.

Contactless physiological signal measurement has great applications in various fields, such as affective computing and health monitoring. Physiological measurements based on remote photoplethysmography (rPPG) are realized by capturing the weak periodic color changes. The changes are caused by the variation in the light absorption of skin surface during systole and diastole stages of a functioning heart.

A conceptual framework of cognitive-affective theory of mind: towards a precision identification of mental disorders.

To explore the minds of others, which is traditionally referred to as Theory of Mind (ToM), is perhaps the most fundamental ability of humans as social beings. Impairments in ToM could lead to difficulties or even deficits in social interaction. The present study focuses on two core components of ToM, the ability to infer others' beliefs and the ability to infer others' emotions, which we refer to as cognitive and affective ToM respectively.

Hybrid visual and memory search for scenes and objects with variable viewpoints.

In hybrid search, observers search visual arrays for any of several target types held in memory. The key finding in hybrid search is that response times (RTs) increase as a linear function of the number of items in a display (visual set size), but RTs increase linearly with the log of the memory set size. Previous experiments have shown this result for specific targets (find exactly this picture of a boot on a blank background) and for broad categorical targets (find any animal).

Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder.

Major depressive disorder (MDD) is a prevalent mental health condition and has become a pressing societal challenge. Early prediction of treatment response may aid in the rehabilitation engineering of depression, which is of great practical significance for the relief of suffering and burden of MDD. In this paper, we present a sequence modeling approach that uses data collected by passive sensing techniques to predict patients with an outcome of treatment responded defined by the reduction in clinical administrated scales.

An improved multi-input deep convolutional neural network for automatic emotion recognition.

Current decoding algorithms based on a one-dimensional (1D) convolutional neural network (CNN) have shown effectiveness in the automatic recognition of emotional tasks using physiological signals. However, these recognition models usually take a single modal of physiological signal as input, and the inter-correlates between different modalities of physiological signals are completely ignored, which could be an important source of information for emotion recognition. Therefore, a complete end-to-end multi-input deep convolutional neural network (MI-DCNN) structure was designed in this study.

Top-down control of attention by stereoscopic depth.

Stereoscopic depth has a mixed record as a guiding attribute in visual attention. Visual search can be efficient if the target lies at a unique depth; whereas automatic segmentation of search arrays into different depth planes does not appear to be pre-attentive. These prior findings describe bottom-up, stimulus-driven depth guidance.

Binocularity and visual search-Revisited.

Binocular rivalry is a phenomenon of visual competition in which perception alternates between two monocular images. When two eye's images only differ in luminance, observers may perceive shininess, a form of rivalry called binocular luster. Does dichoptic information guide attention in visual search? Wolfe and Franzel (Perception & Psychophysics, 44(1), 81-93, 1988) reported that rivalry could guide attention only weakly, but that luster (shininess) "popped out," producing very shallow Reaction Time (RT) × Set Size functions.