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https://www.readbyqxmd.com/read/29233376/a-pharmacy-student-s-role-as-a-teaching-assistant-in-an-undergraduate-medicinal-chemistry-course-implementation-evaluation-and-unexpected-opportunities-for-educational-outreach
#1
Matthew J DellaVecchia, Alyssa M Claudio, Jamie L Fairclough
BACKGROUND AND PURPOSE: To describe 1) a pharmacy student's teaching assistant (TA) role in an undergraduate medicinal chemistry course, 2) an active learning module co-developed by the TA and instructor, and 3) the unexpected opportunities for pharmacy educational outreach that resulted from this collaboration. EDUCATIONAL ACTIVITY AND SETTING: Medicinal Chemistry (CHM3413) is an undergraduate course offered each fall at Palm Beach Atlantic University (PBA). As a TA for CHM3413, a pharmacy student from the Gregory School of Pharmacy (GSOP) at PBA co-developed and implemented an active learning module emphasizing foundational medicinal chemistry concepts as they pertain to performance enhancing drugs (PEDs)...
November 2017: Currents in Pharmacy Teaching & Learning
https://www.readbyqxmd.com/read/29230064/social-learning-may-lead-to-population-level-conformity-without-individual-level-frequency-bias
#2
Kimmo Eriksson, Daniel Cownden, Pontus Strimling
A requirement of culture, whether animal or human, is some degree of conformity of behavior within populations. Researchers of gene-culture coevolution have suggested that population level conformity may result from frequency-biased social learning: individuals sampling multiple role models and preferentially adopting the majority behavior in the sample. When learning from a single role model, frequency-bias is not possible. We show why a population-level trend, either conformist or anticonformist, may nonetheless be almost inevitable in a population of individuals that learn through social enhancement, that is, using observations of others' behavior to update their own probability of using a behavior in the future...
December 11, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29229706/causal-evidence-for-learning-dependent-frontal-lobe-contributions-to-cognitive-control
#3
Paul S Muhle-Karbe, Jiefeng Jiang, Tobias Egner
The lateral prefrontal cortex (LPFC) plays a central role in the prioritization of sensory input based on task-relevance. Such top-down control of perception is of fundamental importance in goal-directed behavior, but can also be costly when deployed excessively, necessitating a mechanism that regulates control engagement to align it with changing environmental demands. We have recently introduced the "flexible control model," which explains this regulation as resulting from a self-adjusting reinforcement-learning mechanism that infers latent statistical structure in dynamic task environments to predict forthcoming states...
December 11, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/29229650/improving-contraceptive-access-use-and-method-mix-by-task-sharing-implanon-insertion-to-frontline-health-workers-the-experience-of-the-integrated-family-health-program-in-ethiopia
#4
Yewondwossen Tilahun, Candace Lew, Bekele Belayihun, Kidest Lulu Hagos, Mengistu Asnake
In 2009, the Ethiopian Federal Ministry of Health launched an Implanon scale-up program with the goal of improving the availability of long-acting reversible contraceptive (LARC) methods at the community level. The Integrated Family Health Program (IFHP) supported the ministry to train Health Extension Workers (HEWs), a cadre of frontline health workers, on Implanon insertion. Prior to this task-sharing initiative, HEWs were only permitted to provide short-acting contraceptive methods; Implanon insertion services were only available at higher-level health facilities, such as health centers and above...
December 8, 2017: Global Health, Science and Practice
https://www.readbyqxmd.com/read/29225449/action-centered-contextual-bandits
#5
Kristjan Greenewald, Ambuj Tewari, Predrag Klasnja, Susan Murphy
Contextual bandits have become popular as they offer a middle ground between very simple approaches based on multi-armed bandits and very complex approaches using the full power of reinforcement learning. They have demonstrated success in web applications and have a rich body of associated theoretical guarantees. Linear models are well understood theoretically and preferred by practitioners because they are not only easily interpretable but also simple to implement and debug. Furthermore, if the linear model is true, we get very strong performance guarantees...
December 2017: Advances in Neural Information Processing Systems
https://www.readbyqxmd.com/read/29218570/pure-correlates-of-exploration-and-exploitation-in-the-human-brain
#6
Tommy C Blanchard, Samuel J Gershman
Balancing exploration and exploitation is a fundamental problem in reinforcement learning. Previous neuroimaging studies of the exploration-exploitation dilemma could not completely disentangle these two processes, making it difficult to unambiguously identify their neural signatures. We overcome this problem using a task in which subjects can either observe (pure exploration) or bet (pure exploitation). Insula and dorsal anterior cingulate cortex showed significantly greater activity on observe trials compared to bet trials, suggesting that these regions play a role in driving exploration...
December 7, 2017: Cognitive, Affective & Behavioral Neuroscience
https://www.readbyqxmd.com/read/29209058/learning-the-value-of-information-and-reward-over-time-when-solving-exploration-exploitation-problems
#7
Irene Cogliati Dezza, Angela J Yu, Axel Cleeremans, William Alexander
To flexibly adapt to the demands of their environment, animals are constantly exposed to the conflict resulting from having to choose between predictably rewarding familiar options (exploitation) and risky novel options, the value of which essentially consists of obtaining new information about the space of possible rewards (exploration). Despite extensive research, the mechanisms that subtend the manner in which animals solve this exploitation-exploration dilemma are still poorly understood. Here, we investigate human decision-making in a gambling task in which the informational value of each trial and the reward potential were separately manipulated...
December 5, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29173508/what-s-in-a-word-how-instructions-suggestions-and-social-information-change-pain-and-emotion
#8
REVIEW
Leonie Koban, Marieke Jepma, Stephan Geuter, Tor D Wager
Instructions, suggestions, and other types of social information can have powerful effects on pain and emotion. Prominent examples include observational learning, social influence, placebo, and hypnosis. These different phenomena and their underlying brain mechanisms have been studied in partially separate literatures, which we discuss, compare, and integrate in this review. Converging findings from these literatures suggest that (1) instructions and social information affect brain systems associated with the generation of pain and emotion, and with reinforcement learning, and that (2) these changes are mediated by alterations in prefrontal systems responsible for top-down control and the generation of affective meaning...
October 2017: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/29170069/a-common-neural-network-differentially-mediates-direct-and-social-fear-learning
#9
Björn Lindström, Jan Haaker, Andreas Olsson
Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning...
November 20, 2017: NeuroImage
https://www.readbyqxmd.com/read/29133424/dopamine-reward-prediction-error-signal-codes-the-temporal-evaluation-of-a-perceptual-decision-report
#10
Stefania Sarno, Victor de Lafuente, Ranulfo Romo, Néstor Parga
Learning to associate unambiguous sensory cues with rewarded choices is known to be mediated by dopamine (DA) neurons. However, little is known about how these neurons behave when choices rely on uncertain reward-predicting stimuli. To study this issue we reanalyzed DA recordings from monkeys engaged in the detection of weak tactile stimuli delivered at random times and formulated a reinforcement learning model based on belief states. Specifically, we investigated how the firing activity of DA neurons should behave if they were coding the error in the prediction of the total future reward when animals made decisions relying on uncertain sensory and temporal information...
November 13, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29121908/a-model-for-the-use-of-blended-learning-in-large-group-teaching-sessions
#11
Cristan Herbert, Gary M Velan, Wendy M Pryor, Rakesh K Kumar
BACKGROUND: Although blended learning has the potential to enhance the student experience, both in terms of engagement and flexibility, it can be difficult to effectively restructure existing courses. To achieve these goals for an introductory Pathology course, offered to more than 250 undergraduate students at UNSW Sydney, we devised a novel approach. METHODS: For each topic presented over 2-3 weeks, a single face-to-face overview lecture was retained. The remaining content that had previously been delivered as conventional lectures was converted into short (12-18 min) online modules...
November 9, 2017: BMC Medical Education
https://www.readbyqxmd.com/read/29121541/development-of-a-reinforcement-learning-based-evolutionary-fuzzy-rule-based-system-for-diabetes-diagnosis
#12
Fatemeh Mansourypoor, Shahrokh Asadi
The early diagnosis of disease is critical to preventing the occurrence of severe complications. Diabetes is a serious health problem. A variety of methods have been developed for diagnosing diabetes. The majority of these methods have been developed in a black-box manner, which cannot be used to explain the inference and diagnosis procedure. Therefore, it is essential to develop methods with high accuracy and interpretability. In this study, a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System (RLEFRBS) is developed for diabetes diagnosis...
October 31, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29108801/mechanisms-of-placebo-analgesia-a-dual-process-model-informed-by-insights-from-cross-species-comparisons
#13
REVIEW
Scott M Schafer, Stephan Geuter, Tor D Wager
Placebo treatments are pharmacologically inert, but are known to alleviate symptoms across a variety of clinical conditions. Associative learning and cognitive expectations both play important roles in placebo responses, however we are just beginning to understand how interactions between these processes lead to powerful effects. Here, we review the psychological principles underlying placebo effects and our current understanding of their brain bases, focusing on studies demonstrating both the importance of cognitive expectations and those that demonstrate expectancy-independent associative learning...
November 3, 2017: Progress in Neurobiology
https://www.readbyqxmd.com/read/29096115/model-based-predictions-for-dopamine
#14
REVIEW
Angela J Langdon, Melissa J Sharpe, Geoffrey Schoenbaum, Yael Niv
Phasic dopamine responses are thought to encode a prediction-error signal consistent with model-free reinforcement learning theories. However, a number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value. Here, we review a selection of these recent results and discuss the implications and complications of model-based predictions for computational theories of dopamine and learning...
October 30, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/29089575/projective-simulation-with-generalization
#15
Alexey A Melnikov, Adi Makmal, Vedran Dunjko, Hans J Briegel
The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments...
October 31, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29078094/task-complexity-moderates-the-influence-of-descriptions-in-decisions-from-experience
#16
Leonardo Weiss-Cohen, Emmanouil Konstantinidis, Maarten Speekenbrink, Nigel Harvey
Decisions-makers often have access to a combination of descriptive and experiential information, but limited research so far has explored decisions made using both. Three experiments explore the relationship between task complexity and the influence of descriptions. We show that in simple experience-based decision-making tasks, providing congruent descriptions has little influence on task performance in comparison to experience alone without descriptions, since learning via experience is relatively easy. In more complex tasks, which are slower and more demanding to learn experientially, descriptions have stronger influence and help participants identify their preferred choices...
November 5, 2017: Cognition
https://www.readbyqxmd.com/read/29071759/curiosity-based-learning-in-infants-a-neurocomputational-approach
#17
Katherine E Twomey, Gert Westermann
Infants are curious learners who drive their own cognitive development by imposing structure on their learning environment as they explore. Understanding the mechanisms by which infants structure their own learning is therefore critical to our understanding of development. Here we propose an explicit mechanism for intrinsically motivated information selection that maximizes learning. We first present a neurocomputational model of infant visual category learning, capturing existing empirical data on the role of environmental complexity on learning...
October 26, 2017: Developmental Science
https://www.readbyqxmd.com/read/29061387/adaptive-coordination-of-working-memory-and-reinforcement-learning-in-non-human-primates-performing-a-trial-and-error-problem-solving-task
#18
Guillaume Viejo, Benoît Girard, Emmanuel Procyk, Mehdi Khamassi
Accumulating evidence suggest that human behavior in trial-and-error learning tasks based on decisions between discrete actions may involve a combination of reinforcement learning (RL) and working-memory (WM). While the understanding of brain activity at stake in this type of tasks often involve the comparison with non-human primate neurophysiological results, it is not clear whether monkeys use similar combined RL and WM processes to solve these tasks. Here we analyzed the behavior of five monkeys with computational models combining RL and WM...
October 20, 2017: Behavioural Brain Research
https://www.readbyqxmd.com/read/29049406/distinct-prediction-errors-in-mesostriatal-circuits-of-the-human-brain-mediate-learning-about-the-values-of-both-states-and-actions-evidence-from-high-resolution-fmri
#19
Jaron T Colas, Wolfgang M Pauli, Tobias Larsen, J Michael Tyszka, John P O'Doherty
Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e...
October 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/29016461/social-learning-pathways-in-the-relation-between-parental-chronic-pain-and-daily-pain-severity-and-functional-impairment-in-adolescents-with-functional-abdominal-pain
#20
Amanda L Stone, Stephen Bruehl, Craig A Smith, Judy Garber, Lynn S Walker
Having a parent with chronic pain (CP) may confer greater risk for persistence of CP from childhood into young adulthood. Social learning, such as parental modeling and reinforcement, represents one plausible mechanism for the transmission of risk for CP from parents to offspring. Based on a 7-day pain diary in 154 pediatric patients with functional abdominal CP, we tested a model in which parental CP predicted adolescents' daily average CP severity and functional impairment (distal outcomes) via parental modeling of pain behaviors and parental reinforcement of adolescent's pain behaviors (mediators) and adolescents' cognitive appraisals of pain threat (proximal outcome representing adolescents' encoding of parents' behaviors)...
October 6, 2017: Pain
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