Behavioral Learning Research Articles

Short-Term Effect of Attributional Versus Non-Attributional Negative Normative Feedback on Motor Tasks: A Double-Blind Study

Augmented feedback can alter motor performance. We examined if presenting attributional versus non-attributional negative normative feedback differently impacted short-term motor performance. With a double-blind experimental design, 49 students (36.7% female, Mage = 17.14 and SD = ±0.35) were assigned to the following two groups: G1: Attributional Negative Normative Feedback group (n = 24) and G2: Non-Attributional Negative Normative Feedback group (n = 25), with the dependent variable being the score obtained on a dart-throwing test. The results showed that those participants who received negative social comparative feedback presented in an attributional way (internal, controllable, and unstable) obtained higher scores in the dart throwing task than those who received negative social comparative feedback presented in a non-attributional way. Furthermore, these differences were maintained in the retention and transfer tests conducted 24 h after the practice phase. These findings have practical implications in motor behavior learning and performance.

초등학생의 학습 동기와 학습 의지의 관계

Objectives The purpose of this study is to explore the relationship between elementary school students' learning motivation and learning volition. Specifically, by analyzing the impact of learning motivation on learning volition, we identified the role of learning motivation as a strategy to enhance elementary students' learning volition. Additionally, we provided concrete recommendations divided into teacher, school, and parent levels. Methods To investigate the relationship between learning motivation and learning volition, 216 elementary school students were surveyed to measure these variables. Correlation analysis and regression analysis were conducted to analyze the impact of learning motivation on learning volition. Results The results of the regression analysis indicated that learning motivation has a significant effect on learning volition. In particular, among the sub-dimensions of learning motivation, achievement motivation and self-efficacy positively influenced the sub-dimensions of learning volition, which include behavioral control, learning effort, and learning perseverance. It was confirmed that the higher the learning motivation, the higher the learning volition. Conclusions This study suggests that enhancing learning motivation is important for improving elementary school students' learning volition. Specifically, teachers should provide personalized learning and emotional support to boost students' learning motivation and self-efficacy, and schools should establish support systems for customized educational programs and teacher capacity building to back this up. Parents should create an environment that enhances their children's learning motivation and supports self-directed learning. Through these efforts, concrete strategies to enhance students' learning volition should be sought.

Reward-driven cerebellar climbing fiber activity influences both neural and behavioral learning.

The cerebellum plays a key role in motor coordination and learning. In contrast with classical supervised learning models, recent work has revealed that CFs can signal reward-predictive information in some behaviors. This raises the question of whether CFs may also operate according to principles similar to those described by reinforcement learning models. To test how CFs operate during reward-guided behavior, and evaluate the role of reward-related CF activity in learning, we have measured CF responses in Purkinje cells of the lateral cerebellum during a Pavlovian task using 2-photon calcium imaging. Specifically, we have performed multi-stimulus experiments to determine whether CF activity meets the requirements of a reward prediction error (rPE) signal for transfer from an unexpected reward to a reward-predictive cue. We find that once CF activity is transferred to a conditioned stimulus, and there is no longer a response to reward, CFs cannot generate learned responses to a second conditioned stimulus that carries the same reward prediction. In addition, by expressing the inhibitory opsin GtACR2 in neurons of the inferior olive, and optically inhibiting these neurons across behavioral training at the time of unexpected reward, we find that the transfer of CF signals to the conditioned stimulus is impaired. Moreover, this optogenetic inhibition also impairs learning, resulting in a deficit in anticipatory lick timing. Together, these results indicate that CF signals can exhibit several characteristics in common with rPEs during reinforcement learning, and that the cerebellum can harness these learning signals to generate accurately timed motor behavior.

Self-Regulated Learning and Learning Outcomes in Undergraduate and Graduate Medical Education: A Meta-Analysis.

Self-regulated learning (SRL) plays a pivotal role in medical education. There is a pressing need for a meta-analytical review to comprehensively evaluate the effect sizes of SRL strategies across diverse learning outcomes and levels of medical trainees. A meta-analysis was executed by searching five databases and resulted in 61 studies that met our inclusion criteria. A three-level meta-analysis was performed to examine the association between SRL strategies and various levels of learning outcomes (i.e., affective, cognitive, and behavioral learning outcomes). Moderator analyses were conducted using meta-regression, considering factors such as types of learning outcomes, training levels, SRL subscales, and quality of the studies. The analysis yielded an average effect size of .212, using Pearson's correlation coefficient, demonstrating a positive and significant association between SRL strategies and overall learning outcomes. Although our moderator analyses indicated that SRL subscales and study quality did not significantly influence the relationship between SRL strategies and learning outcomes, SRL strategies had a more pronounced effect on affective outcomes than on test scores, behavioral outcomes, and mental health outcomes. In addition, the association between SRL strategies and learning outcomes were significantly higher among the clerkship phase of undergraduate medical education than the pre-clerkship phase.

Gender as a Moderator to Students’ Behavioral Intention Toward Online Learning

The study has used the theoretical constructs of unified theory of acceptance and use of technology (UTAUT) model and explored the student's intention to use online learning through EdTech apps. Specifically, the moderating effect of gender has been analyzed. Primary data has been collected from Indian students enrolled in online learning during the COVID-19 pandemic. The partial least squares multi-group analysis (PLS-MGA) technique has been used for data analysis. Results have indicated that self-management of learning, perceived-playfulness, effort-expectancy, and performance-expectancy positively influence the use of online learning, while social influence had insignificant influence. Further, the perceived-playfulness was found to have a high impact on the behavior intention of female students in India.

Machine learning based multi-objective optimization on shear behavior of the inter-module connection

Prefabricated prefinished volumetric construction (PPVC) has become a research hotspot in recent years. Inter-module connections have a crucial influence on the mechanical behavior of PPVC. However, current studies on shear behavior and optimization design method of the inter-module connection are insufficient. This paper investigated shear behavior and machine learning based optimization method of an innovative fully bolted liftable connection (FBLC) for PPVC. The failure mode, force transferring mechanism, and ultimate load bearing capacity of the FBLC under shear force were revealed by the shear behavior tests. Four specimens were tested and the design parameters included the strength and number of the long stay bolts. Subsequently, a refined finite element model (FEM) of the FBLC was established and validated with the ratios of the shear bearing capacity between the FEA and test results ranging from 0.99 to 1.10. Then, six mainstream machine learning algorithms were utilized to predict shear behavior of the FBLC. The Genetic Algorithm Optimized Neural Network (GANN) provided better prediction accuracy on the shear bearing capacity, with an improvement on R2 by 0.1% to 3% compared with other algorithms. Similarly, the Support Vector Regression (SVR) showed higher prediction accuracy on the ultimate displacement, improving R2 by 0.4% to 12.9% compared with other algorithms. A stacking algorithm combing the GANN and SVR was developed as the proxy model between the input variables and optimization metrics. In addition, the NSGA-II algorithm was linked to establish a multi-objective optimization method on shear behavior of the FBLC. The yield load, ultimate load and steel consumption were selected as the optimization objectives and the stacking algorithm was used as the proxy model. The Pareto optimal solution sets on the optimization objectives were explored by the NSGA-II algorithm and the optimization design method of the FBLC was established. Compared with the unoptimized specimen, the yield and ultimate shear bearing capacity of the optimized specimen were increased by 113.5% and 123.6%, respectively, with the steel consumption reduced by 26.3%. Finally, a four-story PPVC was established, and the static analysis was carried out under vertical load and wind load. The shear behavior of the FBLC and inter-story drift ratio of the PPVC before and after optimization were compared to verify the reliability of the optimization method.

Differential impact of optogenetic stimulation of direct and indirect pathways from dorsolateral and dorsomedial striatum on motor symptoms in Huntington's disease mice

The alterations in the basal ganglia circuitry are core pathological hallmark in Huntington's Disease (HD) and traditionally linked to its sever motor symptoms. Recently it was shown that optogenetic stimulation of cortical afferences to the striatum is able to reverse motor symptoms in HD mice. However, the specific contribution of the direct and indirect striatal output pathways from the dorsolateral (DLS) and dorsomedial striatum (DMS) to the motor phenotype is still not clear. Here, we aim to uncover the contributions of these striatal subcircuits to motor control in wild type (WT) and HD mice by using the symptomatic R6/1 mice. We systematically evaluated locomotion, exploratory behavior, and motor learning effects of the selective optogenetic stimulation of D1 or A2A expressing neurons (direct and indirect pathway, respectively), in DLS or DMS. Bilateral optogenetic stimulation of the direct pathway from DLS and the indirect pathway from DMS resulted in subtle locomotor enhancements, while unaltering exploratory behavior. Additionally, bilateral stimulation of the indirect pathway from the DLS improved performance in the accelerated rotarod task, suggesting a role in motor learning. In contrast, in HD mice, stimulation of these pathways did not modulate any of these behaviors. Overall, this study highlights that selective stimulation of direct and indirect pathways from DLS and DMS have subtle impact in locomotion, exploratory activity nor motor learning. The lack of responses in HD mice also suggests that strategies involving cortico-striatal circuits rather than striatal output circuits might be a better strategy for managing motor symptoms in movement disorders.

Brain and eye movement dynamics track the transition from learning to memory-guided action.

Learning never stops. As we navigate life, we continuously acquire and update knowledge to optimize memory-guided action, with a gradual shift from the former to the latter as we master our environment. How are these learning dynamics expressed in the brain and in behavioral patterns? Here, we devised a spatiotemporal image learning task ("Memory Arena") in which participants learn a set of 50 items to criterion across repeated exposure blocks. Critically, brief task-free periods between successive image presentations allowed us to assess multivariate electroencephalogram (EEG) patterns representing the previous and/or upcoming image identity, as well as anticipatory eye movements toward the upcoming image location. As expected, participants eventually met the performance criterion, albeit with different learning rates. During task-free periods, we were able to readily decode representations of both previous and upcoming image identities. Importantly though, decoding strength followed opposing slopes for previous vs. upcoming images across time, with a gradual decline of evidence for the previous image and a gradual increase of evidence for the upcoming image. Moreover, the ratio of upcoming vs. previous image evidence directly followed behavioral learning rates. Finally, eye movement data revealed that participants increasingly used the task-free period to anticipate upcoming image locations, with target-precision slopes paralleling both behavioral performance measures as well as EEG decodability of the upcoming image across time. Together, these results unveil the neural and behavioral dynamics underlying the gradual transition from learning to memory-guided action.

Causal representation learning through higher-level information extraction

The large gap between the generalization level of state-of-the-art machine learning and human learning systems calls for the development of artificial intelligence (AI) models that are truly inspired by human cognition. In tasks related to image analysis, searching for pixel-level regularities has reached a power of information extraction still far from what humans capture with image-based observations. This leads to poor generalization when even small shifts occur at the level of the observations. We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations, using models capable of combining symbolic manipulation, probabilistic reasoning and pattern recognition abilities. We briefly review and explore connections of research from machine learning, cognitive science and related fields of human behavior to support our perspective for the direction to more robust and human-like artificial learning systems.

Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias

We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54–8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.

The foundations and applications of teaching environmental problems: paradigms, learning domains, worldviews, and how they interact

While environmental problems are urgent in modern society, they are especially difficult to tackle because of their normative and politically controversial nature. Universities may choose different theoretical paradigms for the teaching of environmental problems. However, limited theoretical and/or practical analysis has been undertaken of the theoretical and normative paradigms underlying education on environmental problems. How do we know if educational approaches sufficiently equip students to deal with environmental issues? This article provides a taxonomy for mapping both environmental paradigms and learning outcomes, allowing for a thematic content analysis of the programs of a Dutch university that focuses on environmental problems. The main findings are that, while the course guides are overall highly internally coherent, there are disciplinary silos where the different disciplines teach from different paradigmatic presuppositions. Furthermore, the programs are very cognitive-centered, leaving little room for affective, social, and behavioral learning outcomes, despite the importance of these in higher education. This cognitive bias is relevant for all paradigmatic outlooks, but especially for the more mechanistically oriented paradigms. While understandable from the substance of these paradigms, multimodality in pedagogy and learning outcomes is needed for a comprehensive education.

Experimental Learning of a Hyperelastic Behavior with a Physics-Augmented Neural Network

Experimental Learning of a Hyperelastic Behavior with a Physics-Augmented Neural Network

A systematic review of research on emotional artificial intelligence in English language education

In learning English as a foreign language (EFL), students often experience foreign language anxiety. Artificial intelligence (AI) applications that provide emotional support and/or create emotional impacts on student learning, so-called emotional AI applications, have received increased attention. However, there is a lack of a systematic review of studies on emotional AI in EFL education. This paper presents a systematic review of research in this field. The results reveal five affordances of emotional AI in EFL education, namely (1) enabling human-like conversations, (2) providing personalized real-time feedback or instructions, (3) translating images into English text, (4) generating personalized learning content and tasks, and (5) recognizing and analyzing emotions. The first three affordances are more frequently used and have shown promising effects on improving students’ behavioral, cognitive, and affective learning outcomes. Moreover, the findings reveal that emotional support is often integrated with cognitive support; providing emotional support alone may not be enough to support student learning. Meanwhile, providing cognitive support alone can enhance both affective and cognitive learning outcomes. Finally, attention should be paid to the factors that might influence the adoption and effects of emotional AI in EFL education.

Reward timescale controls the rate of behavioural and dopaminergic learning.

Learning the causes of rewards is necessary for survival. Thus, it is critical to understand the mechanisms of such a vital biological process. Cue-reward learning is controlled by mesolimbic dopamine and improves with spacing of cue-reward pairings. However, whether a mathematical rule governs such improvements in learning rate, and if so, whether a unifying mechanism captures this rule and dopamine dynamics during learning remain unknown. Here, we investigate the behavioral, algorithmic, and dopaminergic mechanisms governing cue-reward learning rate. Across a range of conditions in mice, we show a strong, mathematically proportional relationship between both behavioral and dopaminergic learning rates and the duration between rewards. Due to this relationship, removing up to 19 out of 20 cue-reward pairings over a fixed duration has no influence on overall learning. These findings are explained by a dopamine-based model of retrospective learning, thereby providing a unified account of the biological mechanisms of learning.

Modeling Transformational Leadership Fits Organizational Citizenship Behavior Via Talent Management, Learning Organization, and Job Involvement

Modeling Transformational Leadership Fits Organizational Citizenship Behavior Via Talent Management, Learning Organization, and Job Involvement

Ameliorative potential of ethanol extract of Calyptrochilum emarginatum leaves on scopolamine-induced amnesia in male swiss mice

Ethnopharmacological relevanceCalyptrochilum emarginatum (Afzel. Ex Sw.) Schltr. (Orchidaceae) is a traditional medicinal plant known for its antimicrobial properties and efficacy in managing convulsive fever and menstrual disorders and addressing conditions such as malaria, tuberculosis, and cough. Aim of the studyThe study aims to examine the memory-enhancing and neuroprotective properties of ethanol extract of Calyptrochilum emarginatum leaves (EECEL) in scopolamine-induced amnesia mice model. Materials and methodsForty-two male mice were divided into six groups (n = 7). Group 1 served as control, administered distilled water (10 mL/kg, p. o), group 2 received scopolamine only (3 mg/kg, i. p.), groups 3 to 6 received pretreatments of EECEL (50, 100, and 200 mg/kg, p. o.) and donepezil (1 mg/kg, p. o.) 30 min before scopolamine (3 mg/kg), for seven days. Following treatments, behavioral (learning and memory) assessments were carried out, while biochemical (acetylcholinesterase activity, oxidative stress markers, inflammatory cytokines markers) and histological evaluations were done after euthanasia. ResultsScopolamine significantly impaired spatial, long term and recognition memory. Nevertheless, administration of EECEL (50, 100, and 200 mg/kg orally) enhanced memory function in mice, as observed in the Y maze [F (5, 30) = 20.23, p < 0.0001], Morris water maze [F (10, 90) = 3.105, p = 0.0019; [F (5, 30) = 21.13, p < 0.0001]], and novel object recognition tasks [F (5, 30) = 37.22, p < 0.0001)]. Scopolamine-treated mice exhibited significant dysfunction in the cholinergic system, as evidenced by elevated AChE activity [0.099 ± 0.005 vs. 0.063 ± 0.004 mol/min/g] with an elevation in oxidative stress. On the other hand, administration of EECEL counteracted these consequences by reducing AChE activity, mitigating oxidative damage, reducing pro-inflammatory cytokines, and preventing degeneration of neurons. ConclusionThe results demonstrated that EECEL effectively mitigates scopolamine-induced memory impairment via an oxido-inflammatory mechanism and modulation of the central cholinergic system.

A motivational-based learning model for mobile robots

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We introduced hedonic dimensions to explore the impact of preferences on decision-making and employed reinforcement learning to train our motivated-based agents. In our experiments, we deploy three agents with distinct energy decay rates, simulating different metabolic rates, within two diverse environments. We investigate the influence of these conditions on their strategies, movement patterns, and overall behavior. The findings reveal that agents excel at learning more effective strategies when the environment allows for choices that align with their metabolic requirements. Furthermore, we observe that incorporating pleasure as a component of the motivational mechanism affects behavior learning, particularly for agents with regular metabolisms depending on the environment. Our study also unveils that, when confronted with survival challenges, agents prioritize immediate needs over pleasure and equilibrium. These insights shed light on how robotic agents can adapt and make informed decisions in demanding scenarios, demonstrating the intricate interplay between motivation, pleasure, and environmental context in autonomous systems.

Technical Debt Monitoring Decision Making with Skin in the Game

Technical Debt Management (TDM) can suffer from unpredictability, communication gaps and the inaccessibility of relevant information, which hamper the effectiveness of its decision making. These issues can stem from division among decision-makers which takes root in unfair consequences of decisions among different decision-makers. One mitigation route is Skin in the Game thinking, which enforces transparency, fairness and shared responsibility during collective decision-making under uncertainty. This article illustrates characteristics which require Skin in the Game thinking in Technical Debt (TD) identification, measurement, prioritisation and monitoring. We point out crucial problems in TD monitoring rooted in asymmetric information and asymmetric payoff between different factions of decision-makers. A systematic TD monitoring method is presented to mitigate the said problems. The method leverages Replicator Dynamics and Behavioural Learning. The method supports decision-makers with automated TD monitoring decisions; it informs decision-makers when human interventions are required. Two publicly available industrial projects with a non-trivial number of TD and timestamps are utilised to evaluate the application of our method. Mann–Whitney U hypothesis tests are conducted on samples of decisions from our method and the baseline. The statistical evidence indicates that our method can produce cost-effective and contextual TD monitoring decisions.

Implementation of the Acute InpatientMedicine-High Reliability, Learning Environment, and Workforce Development Initiative (AIM-HI) in rural Veterans Health Administration hospitals: A mixed methods evaluation protocol.

Few rural hospital medicine programs include workforce development training that provides social and professional support for interdisciplinary teams. Even fewer include training that creates supportive learning environments that result in higher staff satisfaction, lower burnout, and reduced turnover. The Acute Inpatient Medicine-High Reliability, Learning Environment, and Workforce Development Initiative (AIM-HI) aims to create supportive learning environments in Veterans Health Administration (VA) rural hospital medicine teams. AIM-HI is a type II hybrid implementation study utilizing a convergent mixed methods approach to evaluate the Relational Playbook, a workforce development intervention, and three implementation strategies: behavioral nudges, learning and leadership collaboratives, and leadership coaching. AIM-HI implementation will occur in waves, enrolling additional hospitals every 12 months. In the first wave, AIM-HI will be implemented at three tertiary VA hospitals that treat at least 1000 rural Veterans annually and have an active inpatient hospital medicine program. The primary outcomes in year 1 will be the acceptability, appropriateness, and feasibility of AIM-HI assessed through participant surveys and interviews. In subsequent years, trends in the learning environment, job satisfaction, burnout, and turnover scores will be assessed using a linear mixed-effect model. The anticipated impact of AIM-HI is to evaluate the utility of the implementation strategies and assess trends in Playbook intervention outcomes. The Playbook has strong face validity; however, before large-scale adoption across the VA enterprise, it is essential to establish the acceptability, appropriateness, and feasibility of the Playbook and implementation strategies, as well as to gather data on AIM-HI effectiveness.

Avoiding “random acts of guidance”: Strategies for middle grades school counselors that lead to lasting learning

In a successful middle school, school counselors support young adolescents’ academic progress and personal wellbeing through a variety of methods. Classroom instruction, or classroom guidance, offers one method of providing such support and features the benefit of allowing middle grades school counselors to reach all students in their schools. However, school counselors often face challenges in designing effective classroom instruction that causes deep, long-term learning and allows students to apply new concepts or skills to their own lives. To achieve lasting learning of key mindsets and behaviors, school counselors’ lesson planning must be rooted in the science of how people learn. This article presents four research-based “laws of learning” that will help middle level school counselors avoid creating classroom instruction lessons with unintended barriers to learning in the form of strategies that contradict learning science.