Visual feature processing in a large stroke cohort: evidence against modular organization.

Mid-level visual processing represents a crucial stage between basic sensory input and higher-level object recognition. The conventional model posits that fundamental visual qualities like color and motion are processed in specialized, retinotopic brain regions (e.g.

Feature binding is slow: Temporal integration explains apparent ultrafast binding.

Visual perception involves binding of distinct features into a unified percept. Although traditional theories link feature binding to time-consuming recurrent processes, Holcombe and Cavanagh (2001) demonstrated ultrafast, early binding of features that belong to the same object. The task required binding of orientation and luminance within an exceptionally short presentation time.

Human Visual Cortex and Deep Convolutional Neural Network Care Deeply about Object Background.

Deep convolutional neural networks (DCNNs) are able to partially predict brain activity during object categorization tasks, but factors contributing to this predictive power are not fully understood. Our study aimed to investigate the factors contributing to the predictive power of DCNNs in object categorization tasks. We compared the activity of four DCNN architectures with EEG recordings obtained from 62 human participants during an object categorization task.

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks.

Humans can quickly recognize objects in a dynamically changing world. This ability is showcased by the fact that observers succeed at recognizing objects in rapidly changing image sequences, at up to 13 ms/image. To date, the mechanisms that govern dynamic object recognition remain poorly understood.

Testing, explaining, and exploring models of facial expressions of emotions.

Models are the hallmark of mature scientific inquiry. In psychology, this maturity has been reached in a pervasive question-what models best represent facial expressions of emotion? Several hypotheses propose different combinations of facial movements [action units (AUs)] as best representing the six basic emotions and four conversational signals across cultures. We developed a new framework to formalize such hypotheses as predictive models, compare their ability to predict human emotion categorizations in Western and East Asian cultures, explain the causal role of individual AUs, and explore updated, culture-accented models that improve performance by reducing a prevalent Western bias.