Sensory sensitivity, intolerance of uncertainty and sex differences predicting anxiety in emerging adults

As multiple vulnerability factors have been defined for anxiety disorders, it is important to investigate the interactions among these factors to understand why and how some individuals develop anxiety. Sensory Sensitivity (SS) and Intolerance of Uncertainty (IU) are independent vulnerability factors of anxiety, but their unique relationship in predicting anxiety has rarely been studied in non-clinical populations. The objective of this investigation was to examine the combined effects of SS and IU on self-reported anxiety in a sample of university students. In addition, with the frequently reported sex bias in anxiety literature, we expected that the combined effects of vulnerability factors would be different for females and males. A convenience sample of 313 university students, ages 17–26 years was recruited. The participants completed the Intolerance of Uncertainty Scale (IUS-12), the Adult/Adolescent Sensory Profile (AASP), and the Beck Anxiety Inventory (BAI). Results of moderated mediation analyses demonstrated a strong partial mediation between SS and anxiety through IU, providing evidence that IU, a cognitive bias against the unknown, was one mechanism that explained how SS was related to anxiety. Further, the effect of IU on anxiety was approximately twice as strong in females. Our results highlight the importance of studying the unique relationships among multiple vulnerability factors to better understand anxiety susceptibility in emerging adults.Supported by the Council for Research in the Social Sciences (CRISS) of the Faculty of Social Sciences at Brock University and Brock University's Library Open Access Publishing Fund

Resting-state functional connectivity and reading subskills in children

Individual differences in reading ability have been linked to characteristics of functional connectivity in the brain in both children and adults. However, many previous studies have used single or composite measures of reading, leading to difficulty characterizing the role of functional connectivity in discrete subskills of reading. The present study addresses this issue using resting-state fMRI to examine how resting-state functional connectivity (RSFC) related to individual differences in children\u27s reading subskills, including decoding, sight word reading, reading comprehension, and rapid automatized naming (RAN). Findings showed both positive and negative RSFC-behaviour relationships that diverged across different reading subskills. Positive relationships included increasing RSFC among left dorsal and anterior regions with increasing decoding proficiency, and increasing RSFC between the left thalamus and right fusiform gyrus with increasing sight word reading, RAN, and reading comprehension abilities. In contrast, negative relationships suggested greater functional segregation of attentional and reading networks with improved performance on RAN, decoding, and reading comprehension tasks. Importantly, the results suggest that although reading subskills rely to some extent on shared functional networks, there are also distinct functional connections supporting different components of reading ability in children

Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System

A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has information that characterizes the learning behaviors of its users and can adjust its pedagogy accordingly. This study investigated an intelligent tutoring system with computer agents (AutoTutor) designed to improve comprehension skills in adults with low reading literacy. One goal of this study was to classify adults into different clusters based on their behavioral patterns (accuracy and response time to answer questions) while they interacted with AutoTutor to help them improve their reading comprehension skills. A second goal was to investigate whether adults’ behaviors were associated with different reading components. A third goal was to assess improvements in reading comprehension skills, based on psychometric tests, of different clusters of readers. Performance on AutoTutor was collected in a targeted 100-hour hybrid intervention for adult readers (n = 252) that included both human teachers and the AutoTutor system. The adults’ average accuracy and response time in AutoTutor were used to cluster the adults into four categories: higher performers (comparatively fast and accurate), conscientious readers (slow but accurate), under-engaged readers (fast at the expense of somewhat lower accuracy) and struggling readers (slow and inaccurate). Two psychometric tests of comprehension were used to assess comprehension. Gains in comprehension scores were highest for conscientious readers, lowest for struggling readers, with higher performing readers and under-engaged readers in between. The results provide guidance to enhance the adaptivity of AutoTutor

Automated Disengagement Tracking Within an Intelligent Tutoring System

This paper describes a new automated disengagement tracking system (DTS) that detects learners\u27 maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading comprehension skills. Learners interact with two computer agents in natural language in 30 lessons focusing on word knowledge, sentence processing, text comprehension, and digital literacy. Each lesson has one to three dozen questions to assess and enhance learning. DTS automatically retrieves and aggregates a learner\u27s response accuracies and time on the first three to five questions in a lesson, as a baseline performance for the lesson when they are presumably engaged, and then detects disengagement by observing if the learner\u27s following performance significantly deviates from the baseline. DTS is computed with an unsupervised learning method and thus does not rely on any self-reports of disengagement. We analyzed the response time and accuracy of 252 adult literacy learners who completed lessons in AutoTutor. Our results show that items that the detector identified as the learner being disengaged had a performance accuracy of 18.5%, in contrast to 71.8% for engaged items. Moreover, the three post-test reading comprehension scores from Woodcock Johnson III, RISE, and RAPID had a significant association with the accuracy of engaged items, but not disengaged items