احسان رضایت

Emotions are crucial in social interactions, influencing communication and relationships. Distinguishing the perceived emotion in conscious and unconscious emotional processing is a key research area with cognitive and physiological implications. This study investigates conscious and unconscious emotional processing through behavioral and pupillary responses. Participants completed emotion recognition tasks under varying states, revealing higher accuracy in conscious emotion identification. Emotions like anger, happiness, fear, surprise, and neutral elicited distinct response patterns. Pupillometry data showed pupil size suppression in the conscious state and enhancement in the unconscious state, with differences in peak pupil size across emotions. Task-related components, amplitude, and latency parameters differed between conscious and unconscious states, highlighting the role of awareness in emotional regulation. These findings emphasize the complex interplay of cognitive and physiological processes in emotional responses, providing insights into emotional recognition mechanisms. This study contributes to understanding emotional processing dynamics and has implications for psychology and neuroscience research.

Recent advancements in neurophysiological recording technologies have led to significant complexities in managing large-scale neural data, creating potential bottlenecks in the storage, sharing, and processing within the neuroscience community. To address these challenges, we developed the Big Neuronal Data Framework (BNDF), a distributed high-performance computing (HPC) solution. BNDF leverages open-source big data frameworks, Hadoop and Spark, to offer a flexible and scalable architecture. We tested BNDF on three large-scale electrophysiological datasets from nonhuman primate brains, demonstrating improved runtimes and scalability due to its distributed design. In comparative analyses against MATLAB, a widely used platform, BNDF showcased over five times faster performance in spike sorting, a common task in neuroscience. This significant speed advantage highlights BNDF’s potential to enhance the efficiency of neural data processing and analysis, making it a valuable tool for researchers navigating the complexities of modern neural datasets. Overall, BNDF represents a promising approach to streamline the handling of extensive neural data in the field of neuroscience.