In making sense of the visual world, the brain's processing is driven by two factors: the physical information provided by the eyes (“bottom-up” data) and the expectancies driven by past experience (“top-down” influences). We use degraded stimuli to tease apart the effects of bottom-up and top-down processes because they are easier to recognize with prior knowledge of undegraded images. Using machine learning algorithms, we quantify the amount of information that brain regions contain about stimuli as the subject learns the coherent images. Our results show that several distinct regions, including high-level visual areas and the retinotopic cortex, contain more information about degraded stimuli with prior knowledge. Critically, these regions are separate from those that exhibit classical priming, indicating that top-down influences are more than feature-based attention. Together, our results show how the neural processing of complex imagery is rapidly influenced by fleeting experiences.
Scott Gorlin; Ming Meng; Jitendra Sharma; Hiroki Sugihara; Mriganka Sur; Pawan Sinha
The time required to recover from cold-induced paralysis (chill-coma) is a common measure of insect cold tolerance used to test central questions in thermal biology and predict the effects of climate change on insect populations. The onset of chill-coma in the fall field cricket (Gryllus pe ... more
Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, usin ... more
Undifferentiated nasopharyngeal carcinomas (NPCs) are commonly present with latent EBV infection. However, events regulating EBV infection at early stages of the disease and the role of EBV in disease pathogenesis are largely undefined. Genetic alterations leading to activation of cyclin D1 ... more