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1. Emotion Recognition in the Real World: Passively Collecting and Estimating Emotions from Natural Speech Data of Individuals with Bipolar Disorder

   https://doi.org/10.1109/TAFFC.2024.3407683  

   Mower_Provost, Emily; Sperry, Sarah H; Tavernor, James; Anderau, Steve; Yocum, Anastasia; McInnis, Melvin G (January 2024, IEEE Transactions on Affective Computing)

   Emotions provide critical information regarding a person's health and well-being. Therefore, the ability to track emotion and patterns in emotion over time could provide new opportunities in measuring health longitudinally. This is of particular importance for individuals with bipolar disorder (BD), where emotion dysregulation is a hallmark symptom of increasing mood severity. However, measuring emotions typically requires self-assessment, a willful action outside of one's daily routine. In this paper, we describe a novel approach for collecting real-world natural speech data from daily life and measuring emotions from these data. The approach combines a novel data collection pipeline and validated robust emotion recognition models. We describe a deployment of this pipeline that included parallel clinical and self-report measures of mood and self-reported measures of emotion. Finally, we present approaches to estimate clinical and self-reported mood measures using a combination of passive and self-reported emotion measures. The results demonstrate that both passive and self-reported measures of emotion contribute to our ability to accurately estimate mood symptom severity for individuals with BD. 

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2. Multimodal Analysis of Eye Movements and Fatigue in a Simulated Glass Cockpit Environment

   https://doi.org/10.3390/aerospace8100283  

   Naeeri, Salem; Kang, Ziho; Mandal, Saptarshi; Kim, Kwangtaek (October 2021, Aerospace)

   Pilot fatigue is a critical reason for aviation accidents related to human errors. Human-related accidents might be reduced if the pilots’ eye movement measures can be leveraged to predict fatigue. Eye tracking can be a non-intrusive viable approach that does not require the pilots to pause their current task, and the device does not need to be in direct contact with the pilots. In this study, the positive or negative correlations among the psychomotor vigilance test (PVT) measures (i.e., reaction times, number of false alarms, and number of lapses) and eye movement measures (i.e., pupil size, eye fixation number, eye fixation duration, visual entropy) were investigated. Then, fatigue predictive models were developed to predict fatigue using eye movement measures identified through forward and backward stepwise regressions. The proposed approach was implemented in a simulated short-haul multiphase flight mission involving novice and expert pilots. The results showed that the correlations among the measures were different based on expertise (i.e., novices vs. experts); thus, two predictive models were developed accordingly. In addition, the results from the regressions showed that either a single or a subset of the eye movement measures might be sufficient to predict fatigue. The results show the promise of using non-intrusive eye movements as an indicator for fatigue prediction and provides a foundation that can lead us closer to developing a near real-time warning system to prevent critical accidents. 

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3. Intelligibility as a measure of speech perception: Current approaches, challenges, and recommendations

   https://doi.org/10.1121/10.0016806  

   Baese-Berk, Melissa M.; Levi, Susannah V.; Van Engen, Kristin J. (January 2023, The Journal of the Acoustical Society of America)

   Intelligibility measures, which assess the number of words or phonemes a listener correctly transcribes or repeats, are commonly used metrics for speech perception research. While these measures have many benefits for researchers, they also come with a number of limitations. By pointing out the strengths and limitations of this approach, including how it fails to capture aspects of perception such as listening effort, this article argues that the role of intelligibility measures must be reconsidered in fields such as linguistics, communication disorders, and psychology. Recommendations for future work in this area are presented. 

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4. Individual differences in relational reasoning

   https://doi.org/https://doi.org/10.3758/s13421-019-00964-y  

   Gray, Maureen E.; Holyoak, Keith J. (January 2020, Memory cognition)

   Various forms of relational processing have been linked to cognitive capacity measures, such as working memory and fluid intelligence. However, previous work has not established the extent to which different forms of relational processing reflect common factors, nor whether individual differences in cognitive style also contribute to variations in relational reasoning. The current study took an individual-differences approach to investigate the prerequisites for relational processing. In two studies, college students completed a battery of standardized tests of individual differences related to fluid intelligence and cognitive style, as well as a series of experimental tasks that require relational reasoning. Moderate correlations were obtained between relational processing and measures of cognitive capacity. Questionnaire measures of cognitive style generally did not improve predictions of relational processing beyond the influence of measures of cognitive capacity. 

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5. Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization

   Korotin, Alexander; Li, Lingxiao; Solomon, Justin; Burnaev, Evgeny (March 2021, International Conference on Learning Representations)

   Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. In this paper, we present a scalable algorithm to compute Wasserstein-2 barycenters given sample access to the input measures, which are not restricted to being discrete. While past approaches rely on entropic or quadratic regularization, we employ input convex neural networks and cycle-consistency regularization to avoid introducing bias. As a result, our approach does not resort to minimax optimization. We provide theoretical analysis on error bounds as well as empirical evidence of the effectiveness of the proposed approach in low-dimensional qualitative scenarios and high-dimensional quantitative experiments. 

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