Learned Behavior Research Articles

New Neural Network Corresponding to the Evolution Process of the Brain

In this paper, the logic is developed assuming that all parts of the brain are composed of a combination of modules that basically have the same structure. The fundamental function is the feeding behavior searching for food while avoiding the dangers. This is most necessary function of animals in the early stages of evolution and the basis of time series data processing. The module is presented by a neural network with learning capabilities based on Hebb's law and is called the basic unit. The basic units are placed on layers and the information between the layers is bidirectional. This new neural network is an extension of the traditional neural network that evolved from pattern recognition. The biggest feature is that in the process of processing time series data, the activated part in the neural network changes according to the context structure of the data. Predicts events from the context of learned behavior and selects best way. It is important to incorporate higher levels of intelligence such as learning, imitation functions furthermore long-term memory and object symbolization. A new neural network that deals the "descriptive world" that expresses past and future events to the neural network that deals the "real world" related to the familiar events is added. The scheme of neural network's function is shown using concept of category theory

Changing memories on the fly: the neural circuits of memory re-evaluation in Drosophila melanogaster.

Associative learning leads to modifications in neural networks to assign valence to sensory cues. These changes not only allow the expression of learned behavior but also modulate subsequent learning events. In the brain of the adult fruit fly, Drosophila melanogaster, olfactory memories are established as dopamine-driven plasticity in the output of a highly recurrent network, the mushroom body. Recent findings have highlighted how these changes in the network can steer the strengthening, weakening and formation of parallel memories when flies are exposed to subsequent training trials, conflicting situations or the reversal of contingencies. Together, these processes provide an initial understanding of how learned information can be used to guide the re-evaluation of memories.

A multidimensional repertory grid as a graphic organizer for implementing digital games to promote students’ learning performances and behaviors

AbstractDigital game‐based learning (DGBL) is a frequently adopted mode for facilitating teaching and learning, where learners are immersed in game contexts to engage in gameplay and to construct meaningful knowledge. However, without guidance, students might have difficulties organizing what they have experienced in gaming contexts. Thus, in this research, a multidimensional repertory grid (MDRG) approach was proposed, and a digital game was implemented accordingly. Moreover, an experiment was executed in a geography course to examine the effectiveness of the strategy. A total of 83 high school students (15.5 years old on average) participated in the experiment. The experimental group learned with the multidimensional repertory grid‐based educational game, while the control group learned with the conventional gaming mode. The analysis results revealed that the experimental group had better learning achievement as well as higher learning motivation, self‐efficacy and metacognition awareness. In addition, the behavioral analysis and interview results revealed that those learning with the proposed strategy had a greater tendency to promote higher order thinking. Accordingly, the limitations and suggestions for future research are provided. Practitioner NotesWhat is already known about this topic Digital game‐based learning is an effective teaching approach that enables learners to effectively acquire knowledge in joyful contexts, hence enhancing their learning achievement and learning motivation. Suitable strategies should be embedded in games to enable students to have a higher level of thinking ability and to improve their academic performance. In view of guiding students to observe from multi‐dimensional perspectives, a repertory grid is believed to be a suitable tool since it enables students to observe different learning objects according to different aspects and elements. What this paper adds A multi‐dimensional repertory grid‐based educational game (MDRG game) was developed to facilitate students’ learning achievement, learning motivation, and self‐efficacy as well as their metacognition awareness. In addition to promoting students’ learning outcomes, students’ learning behavioral transition diagrams were presented and interview results were analyzed in this study. Implications for practice and/or policy Traditional graphs or tables could help students memorize and understand features of learning objects; however, it is not easy to promote students’ higher order thinking skills of making comparisons and reflections from multiple dimensions. The multi‐dimensional repertory grid‐based feedback provides students with systematically organized information to make comparisons and reflections, which furthermore enhances students’ higher order thinking competences. Embedding suitable strategies (e.g., multi‐dimensional repertory grid‐based feedback) in games can effectively improve students’ learning performance without increasing their cognitive load.

Entrepreneurship resilience and Iranian organizations: application of the fuzzy DANP technique

PurposeThe purpose of this paper is to find and prioritize human factors affecting entrepreneurial resilience.Design/methodology/approachThe statistical population consists of prominent Iranian university professors in this field, and the statistical sample is ten of them randomly. A researcher-made questionnaire was used for data collection. After a comprehensive review of the theoretical foundations, the research model was formed with 5 main indices and 21 sub-indices. Fuzzy decision-making trial and evaluation laboratory (DEMATEL)-based (DANP) technique and MATLAB software was used for analysis.FindingsIndicators of Values and Beliefs (A3) and Motivation Index (E5) as Influential Indicators and indicators of personal attributes (S1), formal and informal relationships (R2) and human capital (C4) are effective indicators of entrepreneurial resilience. In the final rankings, formal and informal relationships had the highest weight with 0.263 and the lowest with priority and motivation index with 0.080. In addition to the final rankings of the sub-indices, the indicators of first-hand experience, recognition of opportunities and consulting services were given the highest weight.Practical implicationsThis study proposes that resilience is a real-life process and not just a list of each characteristic. All human beings have an innate ability to be resilient, but resilience is a learned and learned behavior, and the emphasis of experts is on the learning of various resilience skills.Originality/valueThis study contributes to the field of entrepreneurship by examining the institutional backgrounds of entrepreneurship resilience.

Investigating perceptions of ‘dose creep’ amongst student radiographers: A grounded theory study

IntroductionThe dose creep phenomenon is now a widely recognized concept in diagnostic radiography in light of recent technological advancements transnationally. However, this still remains underexplored amongst radiography students preparing to enter the radiography profession. In response, this study explores the perceptions of dose creep amongst undergraduate student radiographers. MethodsThe methodological approach utilized in this research study was grounded theory. The qualitative approach aimed to uncover findings from a higher education institution in Australia. Six students were recruited and took part in semi-structured interviews. This enabled the exploration of previously uncovered data, leading to the construction of original theory within the clinical and academic environment. The data analysis employed was constant comparative analysis (CCA). ResultsA number of insights emerged from the qualitative data set. For instance, the radiography students understanding of the term ‘dose creep’ and decision making leading to dose creep in the clinical environment is captured. This is further supported with assessment of image evaluation determining appropriate exposure factor selection and future impact upon graduation as diagnostic radiographers. The findings identify some important learning needs and actions for both clinical and academic settings which may help foster good use of X-ray exposures. ConclusionThis paper concludes by affirming some challenges surrounding optimal exposure selection and the known phenomenon, dose creep. Further, this study identifies the importance of learning and teaching in the clinical environment whereby learned behaviour leads to suboptimum practices. Implications for practiceThis study advances the existing evidence base by providing a unique lens into the knowledge and understanding of dose creep amongst radiography students in both academic and clinical contexts. It is anticipated this paper will help practitioners and educators better understand potential instances of dose creep within the clinical environment.

Reduction of contextual control of conditioned responses by extended Pavlovian training in an insect

The effect of repetitive training on learned behavior has been an important subject in neuroscience. In instrumental conditioning in mammals, learned action early in training is often goal-driven and controlled by outcome expectancy, but as training progresses, it becomes more habitual and insensitive to outcome devaluation. Similarly, we recently showed in Pavlovian conditioning in crickets (Gryllus bimaculatus) that a conditioned response (CR) is initially sensitive to devaluation of the unconditioned stimulus but becomes insensitive to it after extended training. It is known that habitual responses after extended instrumental training are characterized by a higher context specificity than are initial goal-directed actions in mammals. In this study, we investigated whether this is applicable to Pavlovian conditioning in crickets. In crickets that received a standard amount of training to associate an odor with water reward under illumination, CR under illumination was stronger than that in the dark. In crickets that received extended training under illumination, on the other hand, the level of CR did not differ in different light conditions. Further experiments confirmed that context specificity decreases with the development of behavioral automaticity by extended training, as opposed to findings in instrumental training in mammals. We conclude that the nature of habitual behaviors after extended training differs in different learning systems of animals.

Beyond Bizarre: Nature, Culture and the Spectacular Failure of B.F. Skinner’s Pigeon-Guided Missiles

The article uses posthumanism and animal studies as a framework for making sense of B.F. Skinner’s wartime project of training pigeons to guide missiles, with emphasis on explaining the negative response of the donors and the public. The article first considers the hypothesis that the donors’ incredulity was evoked by the species of the animal. During World War II the United States began a massive program for the training of dogs for the military, and the campaign received unanimously positive publicity in the media. Possibly, thus, dogs were perceived as capable of bravery and sacrifice while pigeons were not. However, messenger pigeons had been traditionally incorporated into the war machine and were perceived as heroic. Thus, the analysis moves on to suggest that the perception of the project as ridiculous was related to the type of behavior performed by the animals: a behavior perceived as trained (artificially acquired) and not instinctive. The analysis then shifts into how the distinction between what is perceived as instinctive (natural) and learned (artificial) behavior influences the reception of different performances involving animals. Performances built around “natural” behaviors generate much stronger positive responses, even if the naturalness of these behaviors is a carefully crafted effect.

Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways

The learning of motor skills unfolds over multiple timescales, with rapid initial gains in performance followed by a longer period in which the behavior becomes more refined, habitual, and automatized. While recent lesion and inactivation experiments have provided hints about how various brain areas might contribute to such learning, their precise roles and the neural mechanisms underlying them are not well understood. In this work, we propose neural- and circuit-level mechanisms by which motor cortex, thalamus, and striatum support motor learning. In this model, the combination of fast cortical learning and slow subcortical learning gives rise to a covert learning process through which control of behavior is gradually transferred from cortical to subcortical circuits, while protecting learned behaviors that are practiced repeatedly against overwriting by future learning. Together, these results point to a new computational role for thalamus in motor learning and, more broadly, provide a framework for understanding the neural basis of habit formation and the automatization of behavior through practice.

Why are smartphones disruptive? An empirical study of smartphone use in real-life contexts

Notifications are one of the core functionalities of smartphones. Previous research suggests they can be a major disruption to the professional and private lives of users. This paper presents evidence from a mixed-methods study using first-person wearable video cameras, comprising 200 h of audio-visual first-person, and self-confrontation interview footage with 1130 unique smartphone interactions (N = 37 users), to situate and analyse the disruptiveness of notifications in real-world contexts. We show how smartphone interactions are driven by a complex set of routines and habits users develop over time. We furthermore observe that while the duration of interactions varies, the intervals between interactions remain largely invariant across different activity and location contexts, and for being alone or in the company of others. Importantly, we find that 89% of smartphone interactions are initiated by users, not by notifications. Overall this suggests that the disruptiveness of smartphones is rooted within learned user behaviours, not devices.

Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection using chest X-rays (CXRs). Specifically, our contribution highlights significant benefits obtained through (i) pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; (ii) using ensembles of the fine-tuned models to further improve performance over individual constituent models; (iii) performing statistical analyses at various learning stages for validating results; (iv) interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; and, (v) analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We find that ensemble approaches markedly improved classification and localization performance, and that inter-reader variability and performance level assessment helps guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.

The effect of body mass index on hippocampal morphology and memory performance in late childhood and adolescence.

Childhood obesity is associated with negative physiological and cognitive health outcomes. The hippocampus is a diverse subcortical structure involved in learned feeding behaviors and energy regulation, and research has shown that the hippocampus is vulnerable to the effects of excess adiposity. Previous studies have demonstrated reduced hippocampal volume in overweight and obese children; however, it is unclear if certain subregions are selectively affected. The purpose of this study was to determine how excess body weight influences regional hippocampal surface morphology and memory performance in a large cross-sectional cohort of 588 children and adolescents between 8.33 and 19.92 years of age using body mass index expressed as a percentage of the 95th percentile cutoff (%BMIp95). We demonstrate %BMIp95 is associated with reduced radial thickness in the superior anterior region of the left hippocampus, and this relationship is predominantly driven by children younger than 14 years. We also found %BMIp95 was associated with worse performance on a spatial episodic memory task and this relationship was partially mediated by the radial thickness of the significant shape cluster. These results demonstrate the differential influence of excess body weight on regional hippocampal structure and hippocampal-dependent behavior in children and adolescents.

Use of explicit processes during a visually guided locomotor learning task predicts 24-h retention after stroke.

Implicit and explicit processes can occur within a single locomotor learning task. The combination of these learning processes may impact how individuals acquire/retain the task. Because these learning processes rely on distinct neural pathways, neurological conditions may selectively impact the processes that occur, thus, impacting learning and retention. Thus, our purpose was to examine the contribution of implicit and explicit processes during a visually guided walking task and characterize the relationship between explicit processes and performance/retention in stroke survivors and age-matched healthy adults. Twenty chronic stroke survivors and twenty healthy adults participated in a 2-day treadmill study. Day 1 included baseline, acquisition1, catch, acquisition2, and immediate retention phases, and day 2 included 24-h retention. During acquisition phases, subjects learned to take a longer step with one leg through distorted visual feedback. During catch and retention phases, visual feedback was removed and subjects were instructed to walk normally (catch) or how they walked during the acquisition phases (retention). Change in step length from baseline to catch represented implicit processes. Change in step length from catch to the end of acquisition2 represented explicit processes. A mixed ANOVA found no difference in the type of learning between groups (P = 0.74). There was a significant relationship between explicit processes and 24-h retention in stroke survivors (r = 0.47, P = 0.04) but not in healthy adults (r = 0.34, P = 0.15). These results suggest that stroke may not affect the underlying learning mechanisms used during locomotor learning, but that these mechanisms impact how well stroke survivors retain the new walking pattern.NEW & NOTEWORTHY This study found that stroke survivors used implicit and explicit processes similar to age-matched healthy adults during a visually guided locomotion learning task. The amount of explicit processes was related to how well stroke survivors retained the new walking pattern but not to how well they performed during the task. This work illustrates the importance of understanding the underlying learning mechanisms to maximize retention of a newly learned motor behavior.

A cerebello-olivary signal for negative prediction error is sufficient to cause extinction of associative motor learning.

The brain generates negative prediction error (NPE) signals to trigger extinction, a type of inhibitory learning that is responsible for suppressing learned behaviors when they are no longer useful. Neurons encoding NPE have been reported in multiple brain regions. Here, we use an optogenetic approach to demonstrate that GABAergic cerebello-olivary neurons can generate a powerful NPE signal, capable of causing extinction of conditioned motor responses on its own.

Self-Management as an Evidence-Based Practice for Students with Autism Spectrum Disorder الإدارة الذاتیة کممارسة قائمة على الأدلة للطلاب ذوی اضطراب طیف التوحد

Individuals with autism spectrum disorder (ASD) have difficulty generalizing learned behavior to varied environments with independence. Self-management for students with ASD is important both as a management tool and as a means to enhance students’ quality of life by empowering them to control their own behavior. This paper reviews the effects of self-management as a positive intervention used to improve the social, behavioral, and academic performance of children diagnosed with ASD from early childhood through high school يواجه الطلاب المصابون باضطراب طيف التوحد (ASD) صعوبة في تعميم السلوک المکتسب على بيئات متنوعة تتمتع بالاستقلالية. تعد الإدارة الذاتية للطلاب المصابين بالتوحد مهمة کأداة إدارية ووسيلة لتحسين جودة حياة الطلاب من خلال تمکينهم من التحکم في سلوکهم. تستعرض هذه الورقة آثار الإدارة الذاتية کتدخل إيجابي يستخدم لتحسين الأداء الاجتماعي والسلوکي والأکاديمي للطلاب المصابين بالتوحد من الطفولة المبکرة حتى المرحلة الثانوية

A DESCRIPTIVE STUDY TO ASSESS SELF-ESTEEM AMONG ADOLESCENT GIRLS STUDYING IN CO-EDUCATIONAL SCHOOLS

Biopsychosocial environment in school plays a vital role in development of students. Self-esteem is a multidimensional and context dependent learned behavior pattern that reflects an individual’s evaluation of past behavior and experiences, influence individual’s current behavior and predict individual’s future behavior. A descriptive study was conducted to assess the self- esteem among adolescent girls studying in co-educational schools. Total 140 adolescent girls of selected coeducational schools were selected by stratified random sampling.

Training older adults with virtual reality use to improve collision-avoidance behavior when walking through an aperture

Many older adults perform collision-avoidance behavior either insufficiently (i.e., frequent collision) or inefficiently (i.e., exaggerated behavior to ensure collision-avoidance). The present study examined whether a training system using virtual reality (VR) simulation enhanced older adults’ collision-avoidance behavior in response to a VR image of an aperture during real walking. Twenty-five (n = 13 intervention group and n = 12 control group) older individuals participated. During training, a VR image of walking through an aperture was projected onto a large screen. Participants in the intervention group tried to avoid virtual collision with the minimum body rotation required to walk on the spot through a variety of narrow apertures. Participants in the control group remained without body rotation while walking on the spot through a wide aperture. A comparison between pre-test and post-test performances in the real environment indicated that after the training, significantly smaller body rotation angles were observed in the intervention group. This suggests that the training led participants to modify their behavior to try to move efficiently during real walking. However, although not significant, collision rates also tended to be greater, suggesting that, at least for some participants, the modification required to avoid collision was too difficult. Transfer of the learned behavior using the VR environment to real walking is discussed.

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

The majority of older people wish to live independently at home as long as possible despite having a range of age-related conditions including cognitive impairment. To facilitate this, there has been an extensive focus on exploring the capability of new technologies with limited success. This paper investigates whether MS Kinect (a motion-based sensing 3-D scanner device) within the MiiHome (My Intelligent Home) project in conjunction with other sensory data, machine learning and big data techniques can assist in the diagnosis and prognosis of cognitive impairment and hence prolong independent living. A pool of Kinect devices and various sensors powered by minicomputers providing internet connectivity are being installed in up to 200 homes. This enables continuous remote monitoring of elderly residents living alone. Passive and off-the-shelf sensor technologies were chosen to implement data acquisition specifically from sources that are part of the fabric of the homes, so that no extra effort is required from the participants. Various constraints including environmental, geometrical and big data were identified and appropriately dealt with. A visualization tool (MAGID) was developed for validation and verification of numerous behavioural activities. Then, a subset of data, from twelve pensioners aged over 65 with age-related cognitive decline and frailty, were collected over a period of 6 months. These data were subjected to several machine learning algorithms (multilayer perceptron neural network, neuro-fuzzy and deep learning) for classification and to extract routine behavioural patterns. These patterns were then analysed further to ascertain any health-related information and their attributes. For the first time, important routine behaviour related to Activities of Daily Living (ADL) of elderly people with cognitive and physical decline has been learnt by machine learning techniques from selected sample data obtained by MS Kinect. Medically important behaviour, e.g. eating, walking, sitting, was best learnt by deep learning with accuracy of 99.30% during training stage and average error rate of 1.83% with maximum of 12.98% during the implementation phase. Observations obtained from the application of the above learnt behaviours are presented as trends over a period of time. These trends, supplemented by other sensory signals, have provided a clearer picture of physical (in)activities (including falls) of the pensioners. The calculated behavioural attributes related to key indicators of health events can be used to model the trajectory of health status related to cognitive decline in a home setting. These results, based on a small number of elderly residents over a short period of time, imply that within the results obtained from the MiiHome project, it is possible to find indicators of cognitive decline. However, further studies are needed for full clinical validation of these indications in conjunction with assessment of cognitive decline of the participants.

Psychological Dimensions of Juvenile Delinquency

This essay is written to make you understand the psychology of a juvenile delinquent in comparison to the individual that is considered to be normal or considered an adult. In this paper, everything would be discussed about the mindset of a juvenile from when he commits a crime, why he does it, and what is the theory which led him to commit a crime. Every child is not a criminal, the circumstances, the timeline, the parenting, society and there are many other aspects which turns a child into a criminal and to understand these we will be researching into many different sources of statistics. There will be a portion dedicated to evaluating the brain itself and which are affected during certain activities. There are also some laid out theories which state that delinquency is heredity or is a learned behaviour and most importantly we will look into criminal psychology. Ultimately the paper will conclude reminding the reader of the most valuable statistics to suggest that there are many contributions and aspects to juvenile delinquency and that must be taken into account because if we can understand these aspects then we can help in calming them by getting them the help they need.

Navigation of Three Cooperative Object-Transportation Robots Using a Multistage Evolutionary Fuzzy Control Approach.

This article proposes a new multistage evolutionary fuzzy control configuration and navigation of three-wheeled robots cooperatively carrying an overhead object in unknown environments. Based on the divide-and-conquer technique, this article proposes a stage-by-stage evolutionary obstacle boundary following (OBF) fuzzy control of each of the three robots through multiobjective continuous ant colony optimization. In the first stage, a set of evolutionary nondominated fuzzy controllers (FCs) for a single robot (a leader robot) in the execution of the OBF behavior is learned. In the second stage, a follower robot is controlled by two evolutionary FCs in combination with a switched compensation FC so that the leader and follower robots can cooperatively transport an object while executing the OBF behavior along obstacles containing corners with right angles. In the third stage, the third robot functions as an accompanying robot and is learned to enter into a predicted triangular formation with the leader-follower robots to transport a larger object while executing the OBF behavior. In the navigation of the three object-transportation robots, a new cooperative behavior supervisor is proposed to coordinate the learned OBF behavior and a target seeking behavior. Successful navigations in simulations and experiments verify the effectiveness of the multistage evolutionary fuzzy control approach and navigation scheme.

Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations

Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of a value function expressed as a numeric table or a function approximator. The learned behavior is then derived using a greedy policy with respect to this value function. Nevertheless, sometimes the learned policy does not meet expectations, and the task of authoring is difficult and unsafe because the modification of one value or parameter in the learned value function has unpredictable consequences in the space of the policies it represents. This invalidates direct manipulation of the learned value function as a method to modify the derived behaviors. In this paper, we propose the use of Inverse Reinforcement Learning to incorporate real behavior traces in the learning process to shape the learned behaviors, thus increasing their trustworthiness (in terms of conformance to reality). To do so, we adapt the Inverse Reinforcement Learning framework to the navigation problem domain. Specifically, we use Soft Q-learning, an algorithm based on the maximum causal entropy principle, with MARL-Ped (a Reinforcement Learning-based pedestrian simulator) to include information from trajectories of real pedestrians in the process of learning how to navigate inside a virtual 3D space that represents the real environment. A comparison with the behaviors learned using a Reinforcement Learning classic algorithm (Sarsa(λ)) shows that the Inverse Reinforcement Learning behaviors adjust significantly better to the real trajectories.