88 research outputs found
Reed buoyant despite slump. By Matt Kelly
No full texthttps://rdc.reed.edu/v1/resources/ffd67915-87a8-49ef-b5c2-31a93bf8d6b2/thumb/128.jpgEssay about giving and Reed's Centennial Campaign. Quoted are D.D. Wigley '77, Xander Twombly '87, Aravind Sankar '91, and Adarsh Pyarelal '11
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Decoding Emotional Responses: A Comparative Study of fNIRS and EEG Neuroimaging Techniques
No full textThis master's thesis explores the prediction of valence and arousal scores, key dimensions of emotional states, from functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) data. The primary focus is on improving prediction accuracy by addressing the hemodynamic response function (HRF) in fNIRS and harnessing the capabilities of advanced machine learning models. The study investigates whether temporal offsets to account for the HRF, combined with selective window sizing for data analysis, enhance the prediction of emotional states. Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) are employed to exploit the spatial and temporal features inherent in EEG and fNIRS data. This research includes two key components: (1) implementing offsets to align fNIRS data with the HRF, and (2) determining optimal window sizes to capture relevant regional brain activity. The effectiveness of CNN and LSTM models is evaluated in predicting valence and arousal scores. Results indicate that adjusting for HRF and selecting appropriate window sizes significantly improves the predictive accuracy
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Vocal Entrainment in Multi-Party Conversations: An Exploration of Automated and Experimental Approaches
No full textWhen people engage in conversation, cooperative tasks, or information-exchange, they tend to unconsciously align their linguistic features. This behaviour has been researched under many terms such as entrainment (Hirschberg 2011), synchrony (Borrie et al. 2019), convergence (Ward & Litman 2007), coordination (Branigan et al. 2000, Soares et al. 2024), accommodation (Giles et al. 1991b) and social resonance (Kopp 2010). Linguistic entrainment has been observed across the vocal (Levitan et al. 2012), lexical (Gonzales et al. 2010), semantic (Ireland et al. 2011), and syntactic (Branigan et al. 2000) levels. It is indicative of the quality of interaction and success in cooperative tasks (Litman et al. 2016, Levitan et al. 2015), and correlates with rapport, attraction and trust (Michalsky & Schoormann 2018, Nasir et al. 2017). This makes entrainment useful for the study of team cohesion and cooperation. Entrainment can be measured at the turn-exchange level (local) or conversation level (global). Dyadic or two-party conversations have been the focus of most of this research, while multi-party vocal entrainment is relatively under-explored. This dissertation assesses existing methodologies for measuring vocal entrainment and explores deep neural network architectures for modelling entrainment in spoken conversations. It also evaluates the impact of group size on entrainment models and discusses the challenges in multi-party vocal entrainment modelling. Experiment 1 utilises an existing benchmark (Nasir et al. 2020) for dyadic vocal entrainment modelling and reports its performance on multi-party spoken conversations. It also presents the findings from an annotation task designed to compare dyadic and multi- party conversations. The results show that existing vocal entrainment models are not sensitive to entrainment information in multi-party conversations. Moreover, the annotation task shows that multi-party conversations have complex and less predictable turn-taking patterns than dyadic conversations, thus highlighting a key difference between two- and multi-party conversations. Experiment 2 reports on the results for modelling dyadic vocal entrainment with an LSTM-based model and shows that this architecture is sensitive to entrainment information in natural conversations. Using local entrainment measures, this architecture can differentiate between conversations in which entrainment is present, and contexts in which it is not. The findings have implications for some key applications in entrainment modelling, such as identifying points of entrainment and disentrainment within conversations, analysing entrainment in ongoing conversations, and predicting entrainment in upcoming turns. In Experiment 3, I report on another implementation of the LSTM model for modelling local entrainment in multi-party conversations. I implement the LSTM model for two main tasks– evaluating the efficacy of transfer learning from two- to multi-party conversations, and evaluating the model’s training and test performance on multi-party conversational data. The results show that the LSTM model is sensitive to the quality of interaction between groups of speakers. They also demonstrate that local entrainment is a viable tool for modelling multi-party conversations. These experiments show that turn-level changes in acoustic features are a robust measure for entrainment and conversational naturalness, and can be successfully used as training data for entrainment models. The LSTM-based models are a good fit for building systems that can predict the voice characteristics of upcoming turns by learning from information in entrained speech. Further, the results show that entrainment information becomes available early in the conversation, making it possible to detect entrainment in ongoing conversations. Local entrainment measures can also be used to identify moments of cooperation and conflict within conversations. These experiments also shed light on the impact of group size and complex turn-taking on vocal entrainment, and offer potential solutions for overcoming the inherent challenges of modelling entrainment in multi-party spoken conversations
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