Marianne began researching human emotion as an undergraduate in the laboratory of Elizabeth Phelps at NYU under the guidance of Catherine Hartley. Later she worked as a lab manager for Daniela Schiller at Mount Sinai. She completed her PhD with Tor Wager at CU Boulder in 2019 where she specialized in machine learning applications to neuroimaging analysis and then began her post doc with Jamil Zaki at Stanford shortly after. She is interested in decoding how the brain represents emotions and how these representations are modified through social interaction. She hopes that her research can benefit society by promoting mutual aid and transformative justice.
Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset.
IEEE transactions on affective computing
2021; 12 (3): 579-594
Human emotions unfold over time, and more affective computing research has to prioritize capturing this crucial component of real-world affect. Modeling dynamic emotional stimuli requires solving the twin challenges of time-series modeling and of collecting high-quality time-series datasets. We begin by assessing the state-of-the-art in time-series emotion recognition, and we review contemporary time-series approaches in affective computing, including discriminative and generative models. We then introduce the first version of the Stanford Emotional Narratives Dataset (SENDv1): a set of rich, multimodal videos of self-paced, unscripted emotional narratives, annotated for emotional valence over time. The complex narratives and naturalistic expressions in this dataset provide a challenging test for contemporary time-series emotion recognition models. We demonstrate several baseline and state-of-the-art modeling approaches on the SEND, including a Long Short-Term Memory model and a multimodal Variational Recurrent Neural Network, which perform comparably to the human-benchmark. We end by discussing the implications for future research in time-series affective computing.
View details for DOI 10.1109/taffc.2019.2955949
View details for PubMedID 34484569
Recommendations for the Development of Socioeconomically-Situated and Clinically-Relevant Neuroimaging Models of Pain.
Frontiers in neurology
2021; 12: 700833
Pain is a complex, multidimensional experience that emerges from interactions among sensory, affective, and cognitive processes in the brain. Neuroimaging allows us to identify these component processes and model how they combine to instantiate the pain experience. However, the clinical impact of pain neuroimaging models has been limited by inadequate population sampling - young healthy college students are not representative of chronic pain patients. The biopsychosocial approach to pain management situates a person's pain within the diverse socioeconomic environments they live in. To increase the clinical relevance of pain neuroimaging models, a three-fold biopsychosocial approach to neuroimaging biomarker development is recommended. The first level calls for the development of diagnostic biomarkers via the standard population-based (nomothetic) approach with an emphasis on diverse sampling. The second level calls for the development of treatment-relevant models via a constrained person-based (idiographic) approach tailored to unique individuals. The third level calls for the development of prevention-relevant models via a novel society-based (social epidemiologic) approach that combines survey and neuroimaging data to predict chronic pain risk based on one's socioeconomic conditions. The recommendations in this article address how we can leverage pain's complexity in service of the patient and society by modeling not just individuals and populations, but also the socioeconomic structures that shape any individual's expectations of threat, safety, and resource availability.
View details for DOI 10.3389/fneur.2021.700833
View details for PubMedID 34557144
View details for PubMedCentralID PMC8453079
Touch and social support influence interpersonal synchrony and pain.
Social cognitive and affective neuroscience
Interpersonal touch and social support can influence physical health, mental well-being, and pain. However, the mechanisms by which supportive touch promotes analgesia are not well understood. In Study 1, we tested how three kinds of social support from a romantic partner (passive presence, gentle stroking, and handholding) affect pain ratings and skin conductance responses (SCRs). Overall, support reduced pain ratings in women, but not men, relative to baseline. Support decreased pain-related SCRs in both women and men. Though there were no significant differences across the three support conditions, effects were largest during handholding. Handholding also reduced SCRs in the supportive partner. Additionally, synchronicity in couples' SCR was correlated with reductions in self-reported pain, and individual differences in synchrony were correlated with the partner's trait empathy. In Study 2, we re-analyzed an existing dataset to explore fMRI activity related to individual differences in handholding analgesia effects in women. Increased activity in a distributed set of brain regions, including valuation-encoding frontostriatal areas, was correlated with lower pain ratings. These results may suggest that social support can reduce pain by changing the value of nociceptive signals. This reduction may be moderated by interpersonal synchrony and relationship dynamics.
View details for DOI 10.1093/scan/nsaa048
View details for PubMedID 32301998