Pattern separation is the means by which the brain discriminates similar experiences. It enables retrieval of individuated memories without confusing them with other memories. It is the reason one remembers where one parked the car today and does not mix it up with where one parked it previously. Adult neurogenesis refers to the ongoing production of neurons in the mature brain. One of the likely roles of adult neurogenesis in the hippocampus is facilitating pattern separation. Induced reduction of adult neurogenesis in non-human animals is associated with depression- and anxiety-like behaviors. One possible explanation is that reduced neurogenesis leads to reduced pattern separation, further leading to overgeneralization of threat situations. Instead of perceiving threats where it should, the animal risks perceiving threats everywhere. Emotional resilience is the ability to recover from adversity with a minimum of lingering negative effects such as depression or anxiety. This thesis investigates whether pattern separation in the human hippocampus supports emotional resilience. I performed a systematic review of studies that used the Mnemonic Similarity Task – a memory task commonly used to measure human pattern separation – to investigate the relationship between pattern separation and anxiety. The results are inconclusive but suggest a possible interaction effect whereby pattern separation and high-arousal states like stress predict anxiety. Together with the evidence from the non-human animal studies, this suggests that reduced pattern separation as caused by reduced neurogenesis could make one vulnerable to developing anxiety disorders.

The fundamental principle that reliability is a property of scores and not of instruments provides the foundation of a meta-analytic technique called reliability generalization (RG). RG studies characterize the reliability of scores generated by a given instrument and identify methodological and sample characteristics that contribute to the variability in the reliability of those scores. The present study is an RG of the Substance Abuse Subtle Screening Inventory (SASSI). Reliability estimates were obtained from 19.8% of studies using the SASSI. Bivariate correlations revealed strong, positive correlations between SASSI score reliability and score variability of the Subtle Attributes (r = .877, p < .05) and Family History (r = .892, p < .05) subscales and between score reliability and ethnicity for both the Family History (r = .683, p < .05) and Tendency to Involvement in Correctional Setting (r = .76, p < .05) subscales.

L'apprentissage fédéré est un paradigme émergent qui permet à un grand nombre de clients disposant de données hétérogènes de coordonner l'apprentissage d'un modèle global unifié sans avoir besoin de partager les données entre eux ou avec un stockage central. Il améliore la confidentialité des données, car celles-ci sont décentralisées et ne quittent pas les dispositifs clients. Les algorithmes standard d'apprentissage fédéré impliquent le calcul de la moyenne des paramètres du modèle ou des mises à jour du gradient pour approcher le modèle global au niveau du serveur. Cependant, dans des environnements hétérogènes, le calcul de la moyenne peut entraîner une perte d'information et conduire à une mauvaise généralisation en raison du biais induit par les gradients dominants des clients. Nous supposons que pour mieux généraliser sur des ensembles de données non-i.i.d., les algorithmes devraient se concentrer sur l'apprentissage du mécanisme invariant qui est constant tout en ignorant les mécanismes parasites qui diffèrent entre les clients. Inspirés par des travaux récents dans la littérature sur la distribution des données, nous proposons une approche de calcul de la moyenne masquée par le gradient pour FL comme alternative au calcul de la moyenne standard des mises à jour des clients. mises à jour des clients. Cette technique d'agrégation des mises à jour des clients peut être adaptée en tant que remplacement dans la plupart des algorithmes fédérés existants. Nous réalisons des expériences approfondies avec l'approche de masquage du gradient sur plusieurs algorithmes FL avec distribution, monde réel et hors distribution (en tant qu'algorithme fédéré). Hors distribution (comme le pire des scénarios) avec des déséquilibres quantitatifs. déséquilibres quantitatifs et montrent qu'elle apporte des améliorations constantes, en particulier dans le cas de clients hétérogènes. clients hétérogènes. Des garanties théoriques viennent étayer l'algorithme proposé. / Federated learning is an emerging paradigm that permits a large number of clients with heterogeneous data to coordinate learning of a unified global model without the need to share data amongst each other or to a central storage. In enhances data privacy as data is decentralized and do not leave the client devices. Standard federated learning algorithms involve averaging of model parameters or gradient updates to approximate the global model at the server. However, in heterogeneous settings averaging can result in information loss and lead to poor generalization due to the bias induced by dominant client gradients. We hypothesize that to generalize better across non-i.i.d datasets, the algorithms should focus on learning the invariant mechanism that is constant while ignoring spurious mechanisms that differ across clients. Inspired from recent works in the Out-of-Distribution literature, we propose a gradient masked averaging approach for FL as an alternative to the standard averaging of client updates. This client update aggregation technique can be adapted as a drop-in replacement in most existing federated algorithms. We perform extensive experiments with gradient masked approach on multiple FL algorithms with in-distribution, real-world, and out-of-distribution (as the worst case scenario) test datasets along with quantity imbalances and show that it provides consistent improvements, particularly in the case of heterogeneous clients. Theoretical guarantees further supports the proposed algorithm.

Social competence is needed for interaction among peers, teachers, and families in order for children to be successful in school. Children enter school with various levels of social competence. Social skills training is an effective method for building social skills; however, many programs fail to generalize these skills across settings and time. This study investigated the effects of a social skills training intervention for first and second grade students with emotional and behavioral problems. The intervention blended direct instruction, role-plays, and children's literature, with peers supporting both the acquisition and generalization of the social skills through positive peer reporting (PPR) in other school settings. Results indicate that four students, with or at-risk for emotional and behavioral disorder, in the first and second grade, produced an increased rate of the acquisition and generalization of the skills, How to Follow Directions, How to Ignore Distractions, and How to Ask for Help across various settings with the support of the social skills instruction paired with PPR. This demonstrates that elements of bibliotherapy paired with positive peer reporting may be effective in increasing the acquisition and generalization of social skills across multiple settings.

Conventional language models estimate the probability that a word sequence within a chosen language will occur. By contrast, the purpose of our work is to estimate the probability that the word sequence belongs to the chosen language. The language of interest in our research is comprehensible well-formed English. We explain how conventional language models assume what we refer to as a degree of generalization, the extent to which a model generalizes from a given sequence. We explain why such an assumption may hinder estimation of the probability that a sequence belongs. We show that the probability that a word sequence belongs to a chosen language (represented by a given sequence) can be estimated by avoiding an assumed degree of generalization, and we introduce two methods for doing so: Minimal Number of Segments (MINS) and Segment Selection. We demonstrate that in some cases both MINS and Segment Selection perform better at distinguishing sequences that belong from those that do not than any other method we tested, including Good-Turing, interpolated modified Kneser-Ney, and the Sequence Memoizer.

Multi-Task Learning (MTL) has been an active research area in machine learning for two decades. By training multiple relevant tasks simultaneously with information shared across tasks, it is possible to improve the generalization performance of each task, compared to training each individual task independently. During the past decade, most MTL research has been based on the Regularization-Loss framework due to its flexibility in specifying various types of information sharing strategies, the opportunity it offers to yield a kernel-based methods and its capability in promoting sparse feature representations. However, certain limitations exist in both theoretical and practical aspects of Regularization-Loss-based MTL. Theoretically, previous research on generalization bounds in connection to MTL Hypothesis Space (HS)s, where data of all tasks are pre-processed by a (partially) common operator, has been limited in two aspects: First, all previous works assumed linearity of the operator, therefore completely excluding kernel-based MTL HSs, for which the operator is potentially non-linear. Secondly, all previous works, rather unnecessarily, assumed that all the task weights to be constrained within norm-balls, whose radii are equal. The requirement of equal radii leads to significant inflexibility of the relevant HSs, which may cause the generalization performance of the corresponding MTL models to deteriorate. Practically, various algorithms have been developed for kernel-based MTL models, due to different characteristics of the formulations. Most of these algorithms are a burden to develop and end up being quite sophisticated, so that practitioners may face a hard task in interpreting and implementing them, especially when multiple models are involved. This is even more so, when Multi-Task Multiple Kernel Learning (MT-MKL) models are considered. This research largely resolves the above limitations. Theoretically, a pair of new kernel-based HSs are proposed: one for single-kernel MTL, and another one for MT-MKL. Unlike previous works, we allow each task weight to be constrained within a norm-ball, whose radius is learned during training. By deriving and analyzing the generalization bounds of these two HSs, we show that, indeed, such a flexibility leads to much tighter generalization bounds, which often results to significantly better generalization performance. Based on this observation, a pair of new models is developed, one for each case: single-kernel MTL, and another one for MT-MKL. From a practical perspective, we propose a general MT-MKL framework that covers most of the prominent MT-MKL approaches, including our new MT-MKL formulation. Then, a general purpose algorithm is developed to solve the framework, which can also be employed for training all other models subsumed by this framework. A series of experiments is conducted to assess the merits of the proposed mode when trained by the new algorithm. Certain properties of our HSs and formulations are demonstrated, and the advantage of our model in terms of classification accuracy is shown via these experiments.

<p>Low levels of on-task behaviors can be troublesome for both teachers and students leading to difficulties associated with regulating off-task and disruptive behaviors and providing continuous prompts. Research indicates that students with intellectual disabilities (IDs) frequently engage in off-task and disruptive behaviors (e.g., talking, sleeping, and making negative statements). According to teachers, the on-task behaviors of students with IDs are unsatisfactory due to a behavioral deficit; as a result, these students demand more individual time and attention from adults than their typically developing classmates. This dependence on external prompts can have negative consequences for students with IDs, including exclusion from general education classes and school dropout. Although empirical investigations to address on-task behaviors is limited in Türkiye, Turkish educators indicated that one of their primary concerns was to manage off-task behaviors of students with disabilities in their classrooms. General education classroom teachers also have suggested that special education classrooms were a better placement for students with IDs because of the need to manage off-task behaviors via one-on-one or small group instructional arrangements. As a result of these off-task issues, there is a need for interventions to assist teachers in improving on-task behaviors of students with IDs which may, in turn, promote the inclusion of these students into general education classrooms. </p> <p>  One such intervention is self-management. Self-management strategies in general and self-monitoring in particular have been found to be effective in enhancing on-task behaviors of students with IDs due in part to intrusiveness, adaptability, and reactivity impact. These interventions can also be used to promote inclusion because the responsibility of behavior management passes from the teacher to the student.This change in responsibility could leave teachers more time to teach instead of providing continuous prompts given the higher teacher-student ration in general education classrooms. Unfortunately, there are several limitations in self-management research in Türkiye including the following: (a) the implementation of self-management interventions to improve on-task behaviors has been prominently conducted with students with autism spectrum disorders (ASD) and learning disabilities (LD); (b) the vast majority of these interventions has been conducted in segregated settings such as special education classrooms in middle school settings; and (c) systematic planning in generalization and maintenance has been lacking or limited that have caused lack of generalization of increased on-task behaviors to other settings. Given that Türkiye has only two studies investigated self-management interventions with students with IDs, these interventions have similar concerns as Western countries including lack of investigations in general education classrooms and the absence of generalization and maintenance planning.  </p> <p>In the current data set, self-management interventions (i.e., self-monitoring, self-evaluation, token economy) was utilized to improve on-task behaviors of 4 students with IDs in general education classrooms in Türkiye. A single case multiple-baseline across participants design was used. Therefore, this study aimed to investigate (a) the magnitude of the effect of self-monitoring of the on-task behaviors of Turkish students with IDs, (b) the extent to which the on-task behaviors of Turkish students with IDs generalized and maintained after exposure to self-monitoring training, (c) the effect of self-monitoring on the academic behaviors of Turkish students with IDs, and (d) the relationship between the implementation of self-monitoring and teacher reports on changes in students’ on-task behaviors.  Self-management interventions were implemented across three settings (i.e., Turkish-Language Art [TLA], math, social studies), and generalization data were collected in English-Language Art classes (ELA). Additionally, an average of 16-week maintenance data were collected from all the intervention settings (i.e., TLA, math, social studies). Based on two statistical analyses (i.e., Tau-U and Performance Criteria Based Effect Size [PCES]), the effect of self-management interventions was <em>immediate</em>, <em>generalized</em> across settings, and <em>maintained</em> over long period of time. PCESimmediate was computed to be 1.14 with a significant effect. The overall impact of the Tau-<em>U</em> intervention was 1.00 CI95 (.705 to 1.00), with generalization and maintenance effects of 1.00 CI95 (.695 to 1.00) and 1.00 CI95 (.592 to 1.00), respectively. The total PCES values were determined to be 1.2 for high effectiveness, 1.08 for generalization, and 1.2 for strong effect maintenance. The classroom teachers’ overall classroom behavior ratings were also aligned with the increased on-task behaviors. Therefore, study findings suggested that self-management interventions that originated in the West can be implemented in diverse cultural contexts, specifically with Turkish students with IDs in inclusive classrooms. Implications for future studies are discussed.   </p>

Children with Down syndrome often have high body mass index scores, brought on by hypothyroidism, poor mastication, decreased metabolic rates, and inconsistent physical fitness routines. Along with various genotypic characteristics, several behavioral tendencies accompany the diagnosis of Down syndrome. People with this condition often engage in noncompliant behaviors in an attempt to escape work-related tasks such as exercising. A lack of a consistent fitness regimen may result in additional health complications for this particular group of people, as well as ensuing concerns from the parents or guardians who care for them. Because of the propensities for poor physical health in people with Down syndrome, it is imperative that this group of people include exercise-related activities in their health-care routines to help promote a positive well-being from childhood to adulthood.The purpose of this study is to report on the results of an intervention which utilized high-probability tasks and principles of generalization to address noncompliant behaviors in a 9-year-old boy who had Down syndrome and a history of engaging in refusal towards exercise-related activities. Gross motor skills adapted from the Test of Gross Motor Development assessment were used throughout the study to evaluate both compliance and accuracy of the pre-selected movements. This study used a changing conditions design to assess John’s growth throughout 5 distinct phases. Results from both the high-probability tasks and generalization interventions showed an overall increase in the participant’s compliance and accuracy of skill development throughout all stages of the experiment. Implications from this study provide positive support for using antecedent-based interventions to help individuals with Down syndrome engage in exercise-related activities.

No description available.

No description available.