Recent smell loss is the best predictor of COVID-19 among individuals with recent respiratory symptoms

In a preregistered, cross-sectional study we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC=0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0-10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable

PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling

Locally linear model tree (LoLiMoT) and piecewise linear network (PLN) learning algorithms are two approaches in local linear neurofuzzy modeling. While both methods belong to the class of growing tree learning algorithms, they use different logics. PLN learning relies on training data, it needs rich training data set and no division test, so it is much faster than LoLiMoT, but it may create adjacent neurons that may lead to singularity in regression matrix. On the other hand, LoLiMoT almost always leads to acceptable output error, but it often needs more rules. In this paper, to exploit the complimentary performance of both algorithms piecewise linear model tree (PiLiMoT) learning algorithm is introduced. In essence, PiLiMoT is a combination of LoLiMoT and PLN learning. The initially proposed algorithm is improved by adding the ability to merge previously divided local linear models, and utilizing a simulated annealing stochastic decision process to select a local model for splitting. Comparing to LoLiMoT and PLN learning, our proposed improved learning algorithm shows the ability to construct models with less number of rules at comparable modeling errors. Algorithms are compared through a case study of nonlinear function approximation. Obtained results demonstrate the advantages of combined modified method

An information-based approach to handle various types of uncertainty in fuzzy bodies of evidence.

Fuzzy evidence theory, or fuzzy Dempster-Shafer Theory captures all three types of uncertainty, i.e. fuzziness, non-specificity, and conflict, which are usually contained in a piece of information within one framework. Therefore, it is known as one of the most promising approaches for practical applications. Quantifying the difference between two fuzzy bodies of evidence becomes important when this framework is used in applications. This work is motivated by the fact that while dissimilarity measures have been surveyed in the fields of evidence theory and fuzzy set theory, no comprehensive survey is yet available for fuzzy evidence theory. We proposed a modification to a set of the most discriminative dissimilarity measures (smDDM)-as the minimum set of dissimilarity with the maximal power of discrimination in evidence theory- to handle all types of uncertainty in fuzzy evidence theory. The generalized smDDM (FsmDDM) together with the one previously introduced as fuzzy measures make up a set of measures that is comprehensive enough to collectively address all aspects of information conveyed by the fuzzy bodies of evidence. Experimental results are presented to validate the method and to show the efficiency of the proposed method

Investigating the Interference control in Internet Addiction Disorder: Evidence from Brain Oscillatory Activity associated with Stroop Task

It is common for individuals with internet addiction disorder (IAD) to demonstrate impairments in interference and inhibitory control. A major objective of this study was to explore how interference control is related to event-related spectral perturbations (ERSPs) and whether participants with IAD experience changes in these spectral dynamics. Twenty-one IAD participants and twenty healthy controls (HCs) administered a Stroop task while their brains’ electroencephalographic activity (EEG) was recorded. ERSPs were extracted from the EEG and a cluster-based random permutation test was performed to test the difference in power between the two groups at each time frequency point. ERSP Stroop effect in theta was significantly reduced for the IAD group in an earlier time window, comparing to what was observed in the HC group. According to these results, IADs were unable to successfully inhibit their brain activation for stimulus conflict detection. Furthermore, IAD participants displayed a significant ERSP Stroop effect at beta2 and gamma frequencies - with the main contribution coming from bilateral dorsal frontal and parietal cortex over the scalp when compared to HC participants. In our study, IADs displayed reduced conflict detection and response selection compared to HCs, as measured by theta band indices, as well as impaired conflict resolution, as revealed by altered interaction dynamics between beta2 and gamma bands. This study is one of the first studies to use cluster-based random permutation tests to investigate oscillatory dynamics in conflict processing for IAD group

Understanding Social Cognition with Delta-Rule Active Inference Learning

Trait, attitude and preference learning encompasses the encoding of stable characteristics from observed behaviors, which are then used to make predictions and influence how one interacts with an individual in various contexts. Being able to understand the risk-taking tendencies of others is a complex example of such social inference job. In this research, we used a Bayesian framework to explore how humans can gauge another person's risk-taking tendency. We used a sequential scenario where an observer watched the other person's choices between a high-risk gamble and a guaranteed smaller reward. We proposed an approximate Bayesian observer to assess an agent's risk attitude. This learner utilizes a probabilistic generative model to model the decision making process of others and then employs a variational Bayesian method to invert the generative model. Our research adds to the accumulating evidence that inverting generative models are an essential computing tool for understanding social behavior. The learner updates the posterior estimation of the other's risk attitude on a trial-by-trial basis, with the discrepancy between the model predictions and the choices observed followed the widely accepted prediction error framework namely delta rule. By combining the algorithmic advantages of delta rule with the computational advantage of Bayesian framework, we fashioned a more effective and comprehensible learner. We showed that the accumulated uncertainty the observer builds up while predicting the agent's choices is the main factor that determines inferential uncertainty, i.e., the uncertainty the learner has in estimation of the agent's hidden attitude, which is the impetus for learning and discovery. The model begins with a high learning rate and gradually reduces it as more trials take place. This reflects the way humans learn from examples sequentially, exploring and then making use of the information as they progress. As more data is accumulated from someone who follows a consistent decision-making process, the observer's faith in his own judgments should grow, and he should be less affected by each new observation

Peer-mediated social signals alter risk tolerance in teenage boys based on how far they are from their peers

During early adolescence, peer influences play a crucial role in shaping learning and decision preferences. When teens observe what their peers are doing, they can learn and change their behavior, especially when they are taking risks. Our study incorporated an economical behavioral task and computational modeling framework to examine whether and how early male adolescents' risk attitudes change when they see information about their peers' choices. We recruited 38 middle school male students aged 12-15 years. The experiment consisted of three sessions: The first session and the third session were designed to evaluate the risk attitude of the participants. In the second session, participants were asked to guess the choices made by their peers, and then the computer gave them feedback on the correctness of their predictions. Each participant was randomly assigned to risk-taking or risk-averse peers. Our results revealed that teenagers who predicted risk-averse peers exhibited significant declines in their risk attitudes during the last session. On the other hand, participants with risk-seeking peers exhibited a significantly higher level of risk attitudes after predicting their peers. The data showed that these peer-biased changes in risk attitudes are proportional to the gap between teens' and their peers' risk perspectives. Results showed that their perspectives aligned closer after receiving the information, and approximately a third of the gap was eliminated. This shift may be part of an adaptive process that involves social integration

Internet addicts show impaired interference control ability: Event-related potentials and oscillatory brain responses associated with Stroop Task

Impairment in interference control and inhibitory control is common among people with internet addiction disorders (IAD). The purpose of this study was to investigate the correlation between interference control and event-related spectral perturbations (ERSPs) as well as to examine conflict monitoring indices in IADs. Methods: Participants were recruited from the IAD (n = 21) and healthy control (HC) (n = 20) and administered both the Stroop and a modified version of the Stroop tasks while electroencephalography was recorded. A comparison of the pre-onset baseline with the grand averaged ERP activity was made to determine the distribution of early MFN, late MFN, and SP components. To measure the difference in power between two groups at each time frequency point, event-related spectral perturbations (ERSP) were extracted from the EEG. Furthermore, a cluster-based random permutation test was used. Results: In comparison to the HC group, the IAD group would display decreased ERP activity in early and late MFN due to diminished ability to detect stimulus and response conflicts. Moreover, IAD participants showed reduced activity in conflict SP as a result of defective neuronal reflections of compensatory cognitive control and adjustment processes. On the basis of ERSP of the EEG, it was concluded that low-frequency (theta) and high-frequency (beta2 and gamma) bands are significantly involved in interference control. In contrast to what was observed in the HC group, the ERSP Stroop effect in theta was significantly reduced for the IAD group in an earlier time window. This study indicates that individuals with IADs have impaired executive function, which means that they are unable to prevent their brains from activating in response to conflicting stimuli. Furthermore, ERSP Stroop effects were significantly higher for IAD participants over the scalp at beta2 and gamma frequencies - with a major contribution coming from bilateral dorsal frontal and parietal cortex Conclusions: According to the results, participants with IAD exhibited diminished conflict detection and response selection compared to HCs, as well as diminished conflict resolution, as revealed by damage interaction dynamics in the beta2 and gamma bands. Furthermore, our results indicate that participants with IAD would display a diminished conflict monitoring effect against HCs as indicated by the early and late MFN indices. The current study also revealed that IAD participants exhibited attenuated conflict adaptation compared with HC participants as evidenced by identical conflict SP amplitudes between the two groups

The role of asymmetric information in making decisions under uncertainty

Many decisions have to be made under partial ambiguity where information is not available about the full probability distribution of risks. To decide in a principled way, one would have to make some assumption(s) about hidden risks. We examined how people may balance between the valence of the available information and the potential information concealed by the ambiguity. Under partial ambiguity, people showed flexible skepticism towards the valence of the partially observable probabilistic information. When ambiguity size was small, risk taking was sensitive to valence: if the information was promising, ambiguity aversion increased, skeptically balancing the promising prospects of available positive evidence against the hazards of what might be hidden from the view. Conversely, when the available information was disappointing, ambiguity tolerance increased, cautiously anticipating more than what the available information promised. This flexible skepticism was not a trivially reflexive response to valence: when ambiguity was large (i.e., available information was unreliable), the valence of available information did not impact risk attitudes

Could the unknown unknown be good or bad news? The role of valence in decision making under ambiguity

A number of self-serving biases have recently been explained by asymmetric belief updating under risk which asserts that humans are quick to learn from positive but not negative information. However, risky decisions in real life are often made under ambiguity where only partial information is available about distribution of risks. We demonstrate that under ambiguity, belief updating is not asymmetric but a flexible process of skepticism towards the valence of partially observable facts. When ambiguity size was tractable, belief updating was sensitive to valence: if the information was promising, ambiguity attitude decreased, skeptically balancing the promising prospects of available evidence against the hazards of what might be hidden from the view. Conversely, when the information was disappointing, attitude toward ambiguity increased, cautiously encouraging the participant to be more adventurous than what the available information guaranteed. These results go contradict the predictions from optimistic learning under risk and suggest that belief updating is sensitive to the state of our knowledge and ignorance