Prediction From Minimal Experience: How People Predict the Duration of an Ongoing Epidemic.

People are known for good predictions in domains they have rich experience with, such as everyday statistics and intuitive physics. But how well can they predict for problems they lack experience with, such as the duration of an ongoing epidemic caused by a new virus? Amid the first wave of COVID-19 in China, we conducted an online diary study, asking each of over 400 participants to predict the remaining duration of the epidemic, once per day for 14 days. Participants' predictions reflected a reasonable use of publicly available information but were meanwhile biased, subject to the influence of negative affect and future time perspectives. Computational modeling revealed that participants neither relied on prior distributions of epidemic durations as in inferring everyday statistics, nor on mechanistic simulations of epidemic dynamics as in computing intuitive physics. Instead, with minimal experience, participants' predictions were best explained by similarity-based generalization of the temporal pattern of epidemic statistics. In two control experiments, we further confirmed that such cognitive algorithm is not specific to the epidemic scenario and that minimal and rich experience do lead to different prediction behaviors for the same observations. We conclude that people generalize patterns in recent history to predict the future under minimal experience.

Miniature Linguistic Systems for Individuals With Autism Spectrum Disorder: A Systematic Review and Meta-Analysis.

PURPOSE: Miniature linguistic systems (also known as matrix training) is a method of organizing learning targets to achieve generative learning or recombinative generalization. This systematic review is aimed at determining whether matrix training is effective for individuals with autism spectrum disorder (ASD) in terms of improving recombinative generalization for instruction-following, expressive language, play skills, and literacy skills. METHOD: A systematic review methodology was employed to limit bias in the various review stages. A multifaceted search was conducted. Potential primary studies were imported into Covidence, a systematic review software, and inclusion criteria were applied. Data were extracted regarding (a) participant characteristics, (b) matrix designs, (c) intervention methods, and (d) dependent variable. A quality appraisal using the What Works Clearinghouse (WWC) Single-Case Design Standards (Version 1.0, Pilot) was carried out. In addition to the visual analysis of the data, an effect size estimate, non-overlap of all pairs (NAP), was generated for each participant. Independent t tests and between-subjects analyses of variance were conducted to identify moderators of effectiveness. RESULTS: Twenty-six studies including 65 participants met criteria for inclusion. All included studies were single-case experimental designs. Eighteen studies received a rating of Meets Standards Without Reservations or Meets Standards With Reservations. The aggregated combined NAP scores for acquisition, recombinative generalization, and maintenance of a range of outcomes were in the high range. CONCLUSIONS: Findings suggested that matrix training is an effective teaching method for individuals with ASD for acquisition, recombinative generalization, and maintenance of a range of outcomes. Statistical analyses to identify moderators of effectiveness were insignificant. Based on the WWC Single-Case Design Standards matrix training meets criteria to be considered an evidence-based practice for individuals with ASD.

VISAL-A novel learning strategy to address class imbalance.

In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) to handle the imbalanced data classification task. VISAL's objective is to create more room for the generalization of minority class samples by bringing in both the angular and euclidean margins into the cross-entropy learning strategy. When evaluated on the imbalanced versions of CIFAR, Tiny ImageNet, COVIDx and IMDB reviews datasets, our proposed method outperforms the state of the art works by a significant margin.

An Intelligent Multiattribute Decision-Support Framework Based on Parameterization of Neutrosophic Hypersoft Set.

Hypersoft set is a novel area of interest which is able to tackle the real-world scenarios where classification of parameters into their respective sub-parametric values in the form of overlapping sets is mandatory. It employs a new approximate mapping which considers such sets in the form of sub-parametric tuples as its domain. The existing soft set-like structures are insufficient to tackle such kind of situations. This research intends to establish a novel concept of parameterization of fuzzy set under hypersoft set environment with uncertain components of intuitionistic fuzzy set and neutrosophic set. Two novel structures, i.e., fuzzy parameterized intuitionistic fuzzy hypersoft set (fpifhs-set) and fuzzy parameterized neutrosophic hypersoft set (fpnhs-set), are developed by employing algebraic techniques like theoretic, analytical, pictorial, and algorithmic techniques. After characterizing the elementary properties and set-theoretic operations of fpifhs-set and fpnhs-set, two novel algorithms are proposed to solve real-life decision-making COVID-19 problem. The results of both algorithms are compared with related already established models through certain evaluating features to judge the advantageous aspects of the proposed study. The generalization of the proposed models is discussed by describing some of their particular cases.