In this paper, we describe the design, collection, and validation of a new video database that includes holistic and dynamic emotion ratings from 83 participants watching 22 affective movie clips. In contrast to previous work in Affective Computing, which pursued a single “ground truth” label for the affective content of each moment of each video […]
A.J. Pandina graduated from Tufts University in 2022 with a degree in Biopsychology along the pre-medical track. At Tufts, he worked to assess the influence of caffeine on problem-solving and taught English at the local center for adult Spanish-speaking refugees. Now, as a Clinical Research Assistant in both the Neurobiology of Fear Lab and Baker […]
Assessing sleep and activity is critical within inpatient psychiatry. In our pursuit to enhance the accuracy and depth of available sleep and activity data for clinicians, we have developed a system to extract and display measurements related to sleep and activity from patients who are receiving treatment in the hospital. Our primary goal is to […]
DynAMos: “The Dynamic Affective Movie Clip Database for Subjectivity Analysis” is a new video database that includes holistic and dynamic emotion ratings from over 100 participants watching 22 affective movie clips. Recognizing the subjectivity inherent to emotional experiences, we provide in the database the full distribution of all participants’ ratings. We argue that this choice represents […]
This is a neuroimaging study conducted at McLean Hospital which is focused on studying changes in functional brain network architecture in patients with OCD over the course of intensive, residential treatment. The goal of this project is to study changes in brain networks and harm avoidance behavior that track with OCD-related symptoms and illness behavior […]
Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect […]