How visual confidence on global motion is affected by local motion ambiguity and type of motion noise, and its correlation with autistic trait tendency?

Perceptual confidence has been found to correlate with task performance in general, and is believed to be independent of stimulus features. However, certain stimulus feature could induce a subjective sense of uncertainty, which could potentially influence confidence judgments beyond task performance. The present studies aimed at assessing the effects of the ambiguity of local motion signals on perceptual confidence on a global-motion task. Participants first discriminated the global motion directions of two multiple-aperture, global-motion patterns, one generated using multiple Gabor elements and the other using multiple Plaid elements. They then performed a two-interval, forced-choice confidence task by choosing which of the two perceptual responses they were more confident in being correct. In Experiment 1, when perceptual performance was controlled by varying coherence, we found that participants chose plaids more often than Gabors, even with perceptual performance matched between the two patterns. In Experiment 2, when perceptual performance was controlled by varying luminance contrast of noisy pixels in every motion frame, such “plaid preference” in confidence bias was significantly weakened. Besides, there has been numerous studies on visual perception of autistic individuals. But not many of them has looked into the relationship between their metacognition and perceptual judgement. This study aimed at assessing the relationship between the autistic trait tendency and metacognitive process about one’s perceptual performance. Our results show that, at the same level of objective task performance, subject perceptual confidence depends on both the ambiguity of local motion signals and the type of noise. Our results also shows that there is an association between the subject perceptual confidence and the autistic trait tendency

Examining parody work and subculture in post-2000s Hong Kong

published_or_final_versionMedia, Culture and Creative CitiesMasterMaster of Social Sciences in Media, Culture and Creative Citie

Automatic Semantic Causal Map Integration

Causal map integration is helpful to broaden group member’s eyesight and sheds insight on the detection of overall group’s cognition tendencies. However the existing causal map integration approaches are either based on human intervention mechanism that is criticized with researcher bias, or based on syntactic mechanism that lacks of semantic. In order to improve the current causal map integration methodology and practice, this study proposes the conceptualization and formalization of an innovative causal map integration approach, automatic semantic causal map integration, grounded on the Sowa’s Conceptual Graph Theory and Kosko’s Fuzzy Knowledge Combination Theory. The system prototype with an example is also illustrated

EMBEDDED SOCIAL LEARNING IN ONLINE SOCIAL NETWORKING

Online social networking has deeply penetrated university campuses, influencing multiple aspects of student life. We investigate the impacts of individual online social networking engagement (e.g., on Facebook) from a pedagogical standpoint. Based on social learning theory, we argue that online social networking engagement leads university students to attain positive learning outcomes (self-esteem development, satisfaction with university life, and performance). We further argue that three attributions of social learning (self-efficacy belief, social acceptance and acculturation) bridge individual online social networking engagement with desired learning outcomes. Results from a survey accompanied by focus group discussions demonstrate the substantial impact of university student online social networking on social learning processes and outcomes

Customer Churn Prediction of Telecom Company Using Machine Learning Algorithms

We can’t escape the fact that using telecommunications has become a significant part of our everyday lives. Since the Covid-19 pandemic, the telecommunication industry has become crucial.  Hence, the industry now enjoys growth opportunities. In this study, KNN, Random Forest (RF), AdaBoost, Logistic Regression (LR), XGBoost, and Support Vector Machine (SVM) are 6 supervised machine learning algorithms that will be used in this study to predict the customer churn of a telecom company in California. The goal of this study is to identify the classifier that predicts customer churn the most effectively. As evidenced by its accuracy of 79.67%, precision of 64.67%, recall of 51.87%, and F1-score of 57.57%, XGBoost is the overall most effective classifier in this study. Next, the purpose of this study is to identify the characteristics of customers who are most likely to leave the telecom company. These characteristics were discovered based on customers’ demographics and account information. Lastly, this study also provides the company with advice on how to retain customers. The study advises company to personalize the customer experience, implement a customer loyalty program, and apply AI in customer relationship management in retaining customers

Factor Structure of the Sleep Disturbance Scale for Children (SDSC) in those with Attention Deficit and Hyperactivity Disorder (ADHD)

Objective: To examine the factor structure of the Sleep Disorder Scale for Children (SDSC) in children and adolescents with attention deficit and hyperactivity disorder (ADHD). Method: The caregivers of 307 children with ADHD completed the SDSC. Standard and bifactor confirmatory factor analysis (CFA) evaluated the goodness-of-fit of competing factor structures. Results: The original and unidimensional factor structure produced sub-optimal fit. Bifactor exploratory factor analysis (EFA) was performed to examine the underlying structure of the SDSC. A revised bifactor solution comprising six-specific factors and a general factor was identified. A nested version of this model was deemed to be the preferred model, which also demonstrated good psychometric properties. Conclusion: There is evidence of a ‘general sleep difficulties’ factor in children with ADHD. Four of the six original factors were replicated in this study. However, the revised factor structure suggests that clinicians should be cautious of the utility of subscale scores pending further validation in ADHD samples. © 201

MFN2 mutations cause compensatory mitochondrial DNA proliferation.

MFN2 and OPA1 genes encode two dynamin-like GTPase proteins involved in the fusion of the mitochondrial membrane. They have been associated with Charcot–Marie–Tooth disease type 2A and autosomal dominant optic atrophy, respectively. We report a large family with optic atrophy beginning in early childhood, associated with axonal neuropathy and mitochondrial myopathy in adult life. The clinical presentation looks like the autosomal dominant optic atrophy ‘plus’ phenotype linked to OPA1 mutations but is associated with a novel MFN2 missense mutation (c.629A&gt;T, p.D210V). Multiple mitochondrial DNA deletions were found in skeletal muscle and this observation makes MFN2 a novel gene associated with ‘mitochondrial DNA breakage’ syndrome. Contrary to previous studies in patients with Charcot–Marie–Tooth disease type 2A, fibroblasts carrying the MFN2 mutation present with a respiratory chain deficiency, a fragmentation of the mitochondrial network and a significant reduction of MFN2 protein expression. Furthermore, we show for the first time that impaired mitochondrial fusion is responsible for a deficiency to repair stress-induced mitochondrial DNA damage. It is likely that defect in mitochondrial DNA repair is due to variability in repair protein content across the mitochondrial population and is at least partially responsible for mitochondrial DNA instability. <br/