Modeling Biological Face Recognition with Deep Convolutional Neural Networks.

Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground, and recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers.

Guiding visual attention in deep convolutional neural networks based on human eye movements.

Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision, have evolved into best current computational models of object recognition, and consequently indicate strong architectural and functional parallelism with the ventral visual pathway throughout comparisons with neuroimaging and neural time series data. As recent advances in deep learning seem to decrease this similarity, computational neuroscience is challenged to reverse-engineer the biological plausibility to obtain useful models. While previous studies have shown that biologically inspired architectures are able to amplify the human-likeness of the models, in this study, we investigate a purely data-driven approach.

Comparing Object Recognition in Humans and Deep Convolutional Neural Networks-An Eye Tracking Study.

Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast architectural and functional similarities in visual challenges such as object recognition. Recent insights have demonstrated that both hierarchical cascades can be compared in terms of both exerted behavior and underlying activation. However, these approaches ignore key differences in spatial priorities of information processing.

Predicting Engagement in Electronic Surveillance in Romantic Relationships.

The Internet is a common medium through which people engage in interpersonal electronic surveillance (IES) of one another. We know little empirically about what predicts IES in romantic relationships. The present study expands on factors identified in previous studies (including demographic characteristics, relational characteristics, and other psychosocial variables) to predict surveillance in romantic relationships.