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Introduction: Insomnia causes serious adverse health effects and is estimated to affect 10-30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and longitudinal objective sleep data collected by a smart bed platform. Methods: Users of the Sleep Number smart bed were invited to participate in an IRB approved study which required them to respond to four questionnaires (which included the Insomnia Severity Index; ISI) administered 6 weeks apart from each other in the period from November 2021 to March 2022. For 1,489 participants who completed at least 3 questionnaires, objective data (which includes sleep/wake and cardio-respiratory metrics) collected by the platform were queried for analysis. An incremental, passive-aggressive machine learning model was used to detect insomnia risk which was defined by the ISI exceeding a given threshold. Three ISI thresholds (8, 10, and 15) were considered. The incremental model is advantageous because it allows personalized fine-tuning by adding individual training data to a generic model. Results: The generic model, without personalizing, resulted in an area under the receiving-operating curve (AUC) of about 0.5 for each ISI threshold. The personalized fine-tuning with the data of just five sleep sessions from the individual for whom the model is being personalized resulted in AUCs exceeding 0.8 for all ISI thresholds. Interestingly, no further AUC enhancements resulted by adding personalized data exceeding ten sessions. Discussion: These are encouraging results motivating further investigation into the application of personalized fine tuning machine learning to detect insomnia risk based on longitudinal sleep data and the extension of this paradigm to sleep medicine.

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The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22-64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland-Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen's kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions.

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The impact of context on perception has been well documented for over a century. In some cases, the introduction of context to a set of target features may produce a unified percept, leading to a quicker and more accurate classification; a configural superiority effect (Pomerantz, Sager, & Stoever, 1977). Although this effect has been well characterized in terms of the stimulus features that produce the effect, the specific impact context has on the spatial strategies adopted by observers when making perceptual judgments remains unclear. Here, we sought to address this question by using the methods of response classification and ideal observer analysis. In our main experiment, we used a stimulus set known to produce the configural superiority effect and found that although observers were faster in the presence of context, they were actually less efficient at extracting stimulus information. This surprising result was attributable to the use of a spatial strategy in which observers relied on redundant, noninformative features in the presence of context. A control experiment ruled out the possibility that the mere presence of added context led to these strategic shifts. Our results support previous notions about the nature of the perceptual shifts that are induced by the configural superiority effect. However, they also show that configural processing is more nuanced than originally thought: Although observers may be faster at making judgments when context induces the percept of a configural whole, there appears to be a hidden cost in terms of the efficiency with which information is used. (PsycINFO Database Record

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Important perceptual judgments are often made by combining the opinions of several individuals to make a collective decision, such as when teams of physicians make diagnoses based on medical images. Although group-level decisions are generally superior to the decisions made by individuals, it remains unclear whether collective decision making is most effective when information is redundantly provided to all individuals within a group, or when each individual is responsible for only a portion of the total information. Here, we test this idea by having individuals and groups of different sizes make perceptual judgments about the presence of a weak visual signal. We found that groups viewing the entirety of information significantly outperformed groups that viewed limited portions of information, and that this difference in performance could be accounted for by a simple internal noise-averaging model. However, noise averaging alone was insufficient to account for improvements in individual and group-level performance as group size varied. These results indicate that sharing redundant information can enhance the quality of individual perceptual judgments and lead to better group decision making than dividing information across members of a group.

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Why do faces become easier to recognize with repeated exposure? Previous research has suggested that familiarity may induce a qualitative shift in visual processing from an independent analysis of individual facial features to analysis that includes information about the relationships among features (Farah, Wilson, Drain, & Tanaka Psychological Review, 105, 482-498, 1998; Maurer, Grand, & Mondloch Trends in Cognitive Science, 6, 255-260, 2002). We tested this idea by using a "summation-at-threshold" technique (Gold, Mundy, & Tjan Psychological Science, 23, 427-434, 2012; Nandy & Tjan Journal of Vision, 8, 3.1-20, 2008), in which an observer's ability to recognize each individual facial feature shown independently is used to predict their ability to recognize all of the features shown in combination. We find that, although people are better overall at recognizing familiar as opposed to unfamiliar faces, their ability to integrate information across features is similar for unfamiliar and highly familiar faces and is well predicted by their ability to recognize each of the facial features shown in isolation. These results are consistent with the idea that familiarity has a quantitative effect on the efficiency with which information is extracted from individual features, rather than a qualitative effect on the process by which features are combined.

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Unlike frozen snapshots of facial expressions that we often see in photographs, natural facial expressions are dynamic events that unfold in a particular fashion over time. But how important are the temporal properties of expressions for our ability to reliably extract information about a person's emotional state? We addressed this question experimentally by gauging human performance in recognizing facial expressions with varying temporal properties relative to that of a statistically optimal ("ideal") observer. We found that people recognized emotions just as efficiently when viewing them as naturally evolving dynamic events, temporally reversed events, temporally randomized events, or single images frozen in time. Our results suggest that the dynamic properties of human facial movements may play a surprisingly small role in people's ability to infer the emotional states of others from their facial expressions.

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Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas) distributed among groups of randomly rotated fragments (clutter). The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms), followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least [Formula: see text] ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas.

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