Um experimento-charada usando data-show e resinas de troca iônica

Different memory types for generating saccades at different stages of learning

Hippocampal strata theta oscillations change their frequency and coupling during spatial learning

Mandatory information-based policy tools facilitate California farmers’ learning about nitrogen management

Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most neuroimaging research has focused on investigating cerebral mechanisms, while the way the spinal cord accompanies the brain in shaping human behavior has been largely overlooked. Although the recent advent of functional magnetic resonance imaging (fMRI) sequences that can simultaneously target the brain and spinal cord has opened up new avenues for studying these mechanisms at multiple levels of the CNS, research to date has been limited to inferential univariate techniques that cannot fully unveil the intricacies of the underlying neural states. To address this, we propose to go beyond traditional analyses and instead use a data-driven multivariate approach leveraging the dynamic content of cerebro-spinal signals using innovation-driven coactivation patterns (iCAPs). We demonstrate the relevance of this approach in a simultaneous brain-spinal cord fMRI dataset acquired during motor sequence learning (MSL), to highlight how large-scale CNS plasticity underpins rapid improvements in early skill acquisition and slower consolidation after extended practice. Specifically, we uncovered cortical, subcortical and spinal functional networks, which were used to decode the different stages of learning with a high accuracy and, thus, delineate meaningful cerebro-spinal signatures of learning progression. Our results provide compelling evidence that the dynamics of neural signals, paired with a data-driven approach, can be used to disentangle the modular organization of the CNS. While we outline the potential of this framework to probe the neural correlates of motor learning, its versatility makes it broadly applicable to explore the functioning of cerebro-spinal networks in other experimental or pathological conditions.

PurposeThe paper explores several key issues in organizational learning and their contributions to practice and policy. It determines the different stages of learning, crucial factors and performance outcomes associated with organizational learning.Design/methodology/approachFour Singapore organizations were studied, from which a total of 24 organizational learning practitioners representing both the private and public sectors were interviewed.FindingsQualitative data were presented in the form of excerpts from the respondents in support of the key research objectives as well as quantitatively through the number of mentions, a technique which is uncommon yet useful for an exploratory study.Research limitations/implicationsThe research was limited by the single geographical context, which might not adequately represent the Asian perspective. However, the paper provides room for continuation as the author introduces two other parts (for subsequent issues) that further detail implications for practice and policy based on the same study.Practical implicationsData revealed that: the complexity between the different levels of learning can be simplified by understanding the goals set at each level; and the relationship between learning and performance goes beyond tangible expectations.Originality/valueOf considerable value to practitioners is the discovery of several crucial factors influencing organizational learning and a list of possible learning outcomes ranked in order of importance; the ranking system being a unique technique helping practitioners in their decision‐making more effectively.

The issues of creating intelligent automated learning systems with a reflection function are considered. Using the theory of reflexive games by V.A. Lefebvre, an intelligent reflexive agent (IRA) is introduced into the structure of the learning system planner, providing a search for compromise solutions in the interaction of the models of the student, teacher, subject tutor and methodologist. The basic components of the model of reflexive perception of the IRA of the student and the learning situation are given, taking into account the specifics of decision-making for different stages of learning.

Active avoidance learning is a complex form of aversive feedback learning that in humans and other animals is essential for actively coping with unpleasant, aversive, or dangerous situations. Since the functional circuits involved in two-way avoidance (TWA) learning have not yet been entirely identified, the aim of this study was to obtain an overall picture of the brain circuits that are involved in active avoidance learning. In order to obtain a longitudinal assessment of activation patterns in the brain of freely behaving rats during different stages of learning, we applied single-photon emission computed tomography (SPECT). We were able to identify distinct prefrontal cortical, sensory, and limbic circuits that were specifically recruited during the acquisition and retrieval phases of the two-way avoidance learning task.

The authors’ interest is connected with the application of new technologies in physics education as a means of improving the learning achievement of Physics students at different stages of learning The New Kind of Visual Model instructional strategy introduced in this research is a computer intelligent-based PowerPoint visual simulation used to investigate learning achievement in physics. The research study adopted the Quasi-Experimental Research design. The population comprised of a sample of 168 Secondary School Class-two Physics students purposively selected and treated to 40 minutes tutorials class on the concept of motion. The instrument used consisted in two parts: PowerPoint Computer-simulated visual models which constituted the treatment; and printed materials used to elicit responses to the treatment. There were two activity groups: Experimental group; and Control group. The research study revealed that New Kind of Visual Model instructional strategy contributed positively to learning achievement in physics; result also show that there is significant effect of treatment on students’ retention of learned materials; and has positive significant effect on students’ learning attitude. New Kind of Visual Model instructional strategy will reduce the effects of novelty in method of instruction in all areas of teaching-learning, and may constitute a basis for the use of conceptualism in Computer-aided physics education.

Supporting learners at different stages of learning is essential to achieve positive learning, critical thinking, technical and problem solving skills, and gainful employment upon graduation. Collaboration is critical to providing strong foundational educational support to all learners as they advance to higher level of learning. More important is the need to promote collaboration among educators and other professionals across the Science, Technology, Engineering, and Mathematics (STEM) fields who educate the learners throughout their academic pursuit in their respective institutions of learning. To reap the value in diverse teams, the promotion of emergent interdependence fosters seamless collaborative activities across STEM disciplines. For industrial technology programs to prepare students with skills necessary to supervise and manage the future workforce of any organization successfully, necessary tools must be utilized for the success of the collaborative effort. This paper addresses leadership and knowledge sharing among collaborators, the educational aspects of research facilities and research clusters as some of the tools necessary to develop program through collaboration in STEM fields.

Blocking the ghrelin receptor type 1a in the rat brain impairs memory encoding

Place cells in the hippocampus can maintain multiple representations of a single environment and respond to physical and/or trajectory changes by remapping. Within the hippocampus there are anatomical, electrophysiological, and behavioral dissociations between the dorsal and ventral hippocampus and within dorsal CA1. Arc expression was used to measure the recruitment of ensembles across different hippocampal subregions in rats trained to utilize two different cognitive strategies while traversing an identical trajectory. This behavioral paradigm allowed for the measurement of remapping in the absence of changes in external cues, trajectory traversed (future/past), running speed, motivation, or different stages of learning. Changes in task demands induced remapping in only some hippocampal regions: reorganization of cell ensembles was observed in dorsal CA1 but not in dorsal CA3. Moreover, a gradient was found in the degree of remapping within dorsal CA1 that corresponds to entorhinal connectivity to this region. Remapping was not seen in the ventral hippocampus: neither ventral CA1 nor CA3 exhibited ensemble changes with different cognitive demands. This contrasts with findings of remapping in both the dorsal and ventral dentate gyrus using this task. The results suggest that the dorsal pole of the hippocampus is more sensitive to changes in task demands.

This study examines the relationship between anxiety and learning outcomes in English language acquisition among Arab Palestinian students attending Israeli colleges. Anxiety, widely acknowledged as a significant barrier in educational settings—particularly in foreign language learning—often contributes to negative attitudes toward the learning process. Adopting a qualitative, inductive methodology, this research investigates the multifaceted aspects of anxiety affecting the targeted demographic. A sample of 67 participants completed a modified version of the Foreign Language Classroom Anxiety Scale (FLCAS) questionnaire, providing comprehensive data for analysis. The study begins by assessing the prevalence of anxiety among Arab Palestinian students, uncovering varying levels of anxiety across different stages of learning. Through extensive analysis, the research explores the sources and impacts of anxiety, as well as strategies for its management, offering detailed insights into its dynamics. Key findings highlight the critical roles of personal resources, teaching methods, and the classroom environment in shaping anxiety levels. Moreover, the study identifies distinct effects of anxiety on junior, intermediate, and senior students, emphasizing the unique challenges faced by learners at each level. These insights enhance understanding of the interplay between anxiety and English language acquisition, offering practical recommendations for educational interventions to foster supportive, anxiety-reducing learning environments. The findings of this research align with the results of a study conducted by Hyderpour et al., which demonstrated a correlation between test anxiety and performance in reading compre-hension tests within the context of English for General Purposes (EGP).

This paper describes a knowledge-based architecture, called curriculum tree, for building intelligent tutoring systems. Primarily based on the subject domain knowledge structure, the architecture naturally incorporates the global curriculum planning and monitors the local learning activities. The curriculum tree can also be viewed as a structure of various teaching knowledge at different stages of learning. By adopting rule inheritance, the architecture allows additional additivity and flexibility for developing an intelligent tutoring system incrementally as well as efficiency for running rules in each learning episode. Thus, curriculum tree is an architecture towards building large scale intelligent tutoring systems. In this paper, we shall also discuss how the curriculum tree architecture is used in building Integration-Kid, a Learning Companion System which is a particularly complex type of intelligent tutoring system, in the domain of learning indefinite integration.KeywordsLeaf NodeIntelligent Tutoring SystemSchedule RuleCurriculum TreeSchedule NodeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper we present an active learning approach used to create an annotated corpus of literal and nonliteral usages of verbs. The model uses nearly unsupervised word-sense disambiguation and clustering techniques. We report on experiments in which a human expert is asked to correct system predictions in different stages of learning: (i) after the last iteration when the clustering step has converged, or (ii) during each iteration of the clustering algorithm. The model obtains an f-score of 53.8% on a dataset in which literal/nonliteral usages of 25 verbs were annotated by human experts. In comparison, the same model augmented with active learning obtains 64.91%. We also measure the number of examples required when model confidence is used to select examples for human correction as compared to random selection. The results of this active learning system have been compiled into a freely available annotated corpus of literal/nonliteral usage of verbs in context.

The extinction of learned fear is a hippocampus-dependent process thought to embody new learning rather than erasure of the original fear memory, although it is unknown how these competing contextual memories are represented in the hippocampus. We previously demonstrated that contextual fear conditioning results in hippocampal place cell remapping and long-term stabilization of novel representations. Here we report that extinction learning also induces place cell remapping in C57BL/6 mice. Specifically, we observed cells that preferentially remapped during different stages of learning. While some cells remapped in both fear conditioning and extinction, others responded predominantly during extinction, which may serve to modify previous representations as well as encode new safe associations. Additionally, we found cells that remapped primarily during fear conditioning, which could facilitate reacquisition of the original fear association. Moreover, we also observed cells that were stable throughout learning, which may serve to encode the static aspects of the environment. The short-term remapping observed during extinction was not found in animals that did not undergo fear conditioning, or when extinction was conducted outside of the conditioning context. Finally, conditioning and extinction produced an increase in spike phase locking to the theta and gamma frequencies. However, the degree of remapping seen during conditioning and extinction only correlated with gamma synchronization. Our results suggest that the extinction learning is a complex process that involves both modification of pre-existing memories and formation of new ones, and these traces coexist within the same hippocampal representation.