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model-based reinforcement learning

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https://www.readbyqxmd.com/read/28077716/the-attraction-effect-modulates-reward-prediction-errors-and-intertemporal-choices
#1
Sebastian Gluth, Jared M Hotaling, Jörg Rieskamp
: Classical economic theory contends that the utility of a choice option should be independent of other options. This view is challenged by the attraction effect, in which the relative preference between two options is altered by the addition of a third, asymmetrically dominated option. Here, we leveraged the attraction effect in the context of intertemporal choices to test whether both decisions and reward prediction errors (RPE) in the absence of choice violate the independence of irrelevant alternatives principle...
January 11, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28065344/abai-s-moc-assessment-of-knowledge-program-matures-adding-value-with-continuous-learning-and-assessment
#2
REVIEW
David I Bernstein, Stephen I Wasserman, William P Thompson, Theodore M Freeman
Rapid changes in modern medicine along with advances in the science of learning and memory have necessitated a shift in the way physician knowledge is assessed. Physician recertification beyond initial certification has historically consisted of retaining large amounts of knowledge over a long time span. The adult learning theory has shown that the maintenance and improvement of our knowledge base is more effective by being exposed to new concepts at regular intervals throughout one's career and reinforcing these concepts on an ongoing basis...
January 2017: Journal of Allergy and Clinical Immunology in Practice
https://www.readbyqxmd.com/read/28047608/su-d-brb-05-quantum-learning-for-knowledge-based-response-adaptive-radiotherapy
#3
I El Naqa, R Ten
PURPOSE: There is tremendous excitement in radiotherapy about applying data-driven methods to develop personalized clinical decisions for real-time response-based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics-inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient's characteristics and optimize outcomes...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28018206/the-role-of-multiple-neuromodulators-in-reinforcement-learning-that-is-based-on-competition-between-eligibility-traces
#4
Marco A Huertas, Sarah E Schwettmann, Harel Z Shouval
The ability to maximize reward and avoid punishment is essential for animal survival. Reinforcement learning (RL) refers to the algorithms used by biological or artificial systems to learn how to maximize reward or avoid negative outcomes based on past experiences. While RL is also important in machine learning, the types of mechanistic constraints encountered by biological machinery might be different than those for artificial systems. Two major problems encountered by RL are how to relate a stimulus with a reinforcing signal that is delayed in time (temporal credit assignment), and how to stop learning once the target behaviors are attained (stopping rule)...
2016: Frontiers in Synaptic Neuroscience
https://www.readbyqxmd.com/read/28018203/computational-properties-of-the-hippocampus-increase-the-efficiency-of-goal-directed-foraging-through-hierarchical-reinforcement-learning
#5
Eric Chalmers, Artur Luczak, Aaron J Gruber
The mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL) to guide "goal-directed" behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animals' ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a flexible MBRL mechanism for spatial navigation that is computationally efficient and can adapt quickly to change...
2016: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/27966103/the-drift-diffusion-model-as-the-choice-rule-in-reinforcement-learning
#6
Mads Lund Pedersen, Michael J Frank, Guido Biele
Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics...
December 13, 2016: Psychonomic Bulletin & Review
https://www.readbyqxmd.com/read/27916841/market-model-for-resource-allocation-in-emerging-sensor-networks-with-reinforcement-learning
#7
Yue Zhang, Bin Song, Ying Zhang, Xiaojiang Du, Mohsen Guizani
Emerging sensor networks (ESNs) are an inevitable trend with the development of the Internet of Things (IoT), and intend to connect almost every intelligent device. Therefore, it is critical to study resource allocation in such an environment, due to the concern of efficiency, especially when resources are limited. By viewing ESNs as multi-agent environments, we model them with an agent-based modelling (ABM) method and deal with resource allocation problems with market models, after describing users' patterns...
November 29, 2016: Sensors
https://www.readbyqxmd.com/read/27909102/the-attraction-effect-modulates-reward-prediction-errors-and-intertemporal-choices
#8
Sebastian Gluth, Jared M Hotaling, Jörg Rieskamp
: Classical economic theory contends that the utility of a choice option should be independent of other options. This view is challenged by the attraction effect, in which the relative preference between two options is altered by the addition of a third, asymmetrically dominated option. Here, we leveraged the attraction effect in the context of intertemporal choices to test whether both decisions and reward prediction errors (RPE)-in the absence of choice-violate the independence of irrelevant alternatives principle...
December 1, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/27893230/ecological-momentary-assessment-of-negative-symptoms-in-schizophrenia-relationships-to-effort-based-decision-making-and-reinforcement-learning
#9
Erin K Moran, Adam J Culbreth, Deanna M Barch
Negative symptoms are a core clinical feature of schizophrenia, but conceptual and methodological problems with current instruments can make their assessment challenging. One hypothesis is that current symptom assessments may be influenced by impairments in memory and may not be fully reflective of actual functioning outside of the laboratory. The present study sought to investigate the validity of assessing negative symptoms using ecological momentary assessment (EMA). Participants with schizophrenia (N = 31) completed electronic questionnaires on smartphones 4 times a day for 1 week...
November 28, 2016: Journal of Abnormal Psychology
https://www.readbyqxmd.com/read/27876690/-mommy-blogs-and-the-vaccination-exemption-narrative-results-from-a-machine-learning-approach-for-story-aggregation-on-parenting-social-media-sites
#10
Timothy R Tangherlini, Vwani Roychowdhury, Beth Glenn, Catherine M Crespi, Roja Bandari, Akshay Wadia, Misagh Falahi, Ehsan Ebrahimzadeh, Roshan Bastani
BACKGROUND: Social media offer an unprecedented opportunity to explore how people talk about health care at a very large scale. Numerous studies have shown the importance of websites with user forums for people seeking information related to health. Parents turn to some of these sites, colloquially referred to as "mommy blogs," to share concerns about children's health care, including vaccination. Although substantial work has considered the role of social media, particularly Twitter, in discussions of vaccination and other health care-related issues, there has been little work on describing the underlying structure of these discussions and the role of persuasive storytelling, particularly on sites with no limits on post length...
November 22, 2016: JMIR Public Health and Surveillance
https://www.readbyqxmd.com/read/27870610/neural-circuits-trained-with-standard-reinforcement-learning-can-accumulate-probabilistic-information-during-decision-making
#11
Nils Kurzawa, Christopher Summerfield, Rafal Bogacz
Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given the two options. Several models have been proposed for how neural circuits can learn these log-likelihood ratios from experience, but all of these models introduced novel and specially dedicated synaptic plasticity rules...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27852776/somatic-and-reinforcement-based-plasticity-in-the-initial-stages-of-human-motor-learning
#12
Ananda Sidarta, Shahabeddin Vahdat, Nicolò F Bernardi, David J Ostry
: As one learns to dance or play tennis, the desired somatosensory state is typically unknown. Trial and error is important as motor behavior is shaped by successful and unsuccessful movements. As an experimental model, we designed a task in which human participants make reaching movements to a hidden target and receive positive reinforcement when successful. We identified somatic and reinforcement-based sources of plasticity on the basis of changes in functional connectivity using resting-state fMRI before and after learning...
November 16, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/27833398/intersections-of-critical-systems-thinking-and-community-based-participatory-research-a-learning-organization-example-with-the-autistic-community
#13
Dora M Raymaker
Critical systems thinking (CST) and community based participatory research (CBPR) are distinct approaches to inquiry which share a primary commitment to holism and human emancipation, as well as common grounding in critical theory and emancipatory and pragmatic philosophy. This paper explores their intersections and complements on a historical, philosophical, and theoretical level, and then proposes a hybrid approach achieved by applying CBPR's principles and considerations for operationalizing emancipatory practice to traditional systems thinking frameworks and practices...
October 2016: Systemic Practice and Action Research
https://www.readbyqxmd.com/read/27825732/cognitive-components-underpinning-the-development-of-model-based-learning
#14
Tracey C S Potter, Nessa V Bryce, Catherine A Hartley
Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice...
October 29, 2016: Developmental Cognitive Neuroscience
https://www.readbyqxmd.com/read/27797550/the-cognitive-architecture-of-anxiety-like-behavioral-inhibition
#15
Dominik R Bach
The combination of reward and potential threat is termed approach/avoidance conflict and elicits specific behaviors, including passive avoidance and behavioral inhibition (BI). Anxiety-relieving drugs reduce these behaviors, and a rich psychological literature has addressed how personality traits dominated by BI predispose for anxiety disorders. Yet, a formal understanding of the cognitive inference and planning processes underlying anxiety-like BI is lacking. Here, we present and empirically test such formalization in the terminology of reinforcement learning...
January 2017: Journal of Experimental Psychology. Human Perception and Performance
https://www.readbyqxmd.com/read/27797151/interrogating-feature-learning-models-to-discover-insights-into-the-development-of-human-expertise-in-a-real-time-dynamic-decision-making-task
#16
Catherine Sibert, Wayne D Gray, John K Lindstedt
Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, (a) choosing the goal or objective function that will maximize performance and (b)a feature-based analysis of the current game board to determine where to place the currently falling zoid (i.e., Tetris piece) so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning (CERL) models (Szita & Lorincz, 2006) to determine whether different goals result in different feature weights...
October 31, 2016: Topics in Cognitive Science
https://www.readbyqxmd.com/read/27793098/-proactive-use-of-cue-context-congruence-for-building-reinforcement-learning-s-reward-function
#17
Judit Zsuga, Klara Biro, Gabor Tajti, Magdolna Emma Szilasi, Csaba Papp, Bela Juhasz, Rudolf Gesztelyi
BACKGROUND: Reinforcement learning is a fundamental form of learning that may be formalized using the Bellman equation. Accordingly an agent determines the state value as the sum of immediate reward and of the discounted value of future states. Thus the value of state is determined by agent related attributes (action set, policy, discount factor) and the agent's knowledge of the environment embodied by the reward function and hidden environmental factors given by the transition probability...
October 28, 2016: BMC Neuroscience
https://www.readbyqxmd.com/read/27781362/spared-internal-but-impaired-external-reward-prediction-error-signals-in-major-depressive-disorder-during-reinforcement-learning
#18
Jasmina Bakic, Gilles Pourtois, Marieke Jepma, Romain Duprat, Rudi De Raedt, Chris Baeken
BACKGROUND: Major depressive disorder (MDD) creates debilitating effects on a wide range of cognitive functions, including reinforcement learning (RL). In this study, we sought to assess whether reward processing as such, or alternatively the complex interplay between motivation and reward might potentially account for the abnormal reward-based learning in MDD. METHODS: A total of 35 treatment resistant MDD patients and 44 age matched healthy controls (HCs) performed a standard probabilistic learning task...
October 26, 2016: Depression and Anxiety
https://www.readbyqxmd.com/read/27754316/a-novel-dynamic-spectrum-access-framework-based-on-reinforcement-learning-for-cognitive-radio-sensor-networks
#19
Yun Lin, Chao Wang, Jiaxing Wang, Zheng Dou
Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks...
October 12, 2016: Sensors
https://www.readbyqxmd.com/read/27713407/striatal-prediction-errors-support-dynamic-control-of-declarative-memory-decisions
#20
Jason M Scimeca, Perri L Katzman, David Badre
Adaptive memory requires context-dependent control over how information is retrieved, evaluated and used to guide action, yet the signals that drive adjustments to memory decisions remain unknown. Here we show that prediction errors (PEs) coded by the striatum support control over memory decisions. Human participants completed a recognition memory test that incorporated biased feedback to influence participants' recognition criterion. Using model-based fMRI, we find that PEs-the deviation between the outcome and expected value of a memory decision-correlate with striatal activity and predict individuals' final criterion...
October 7, 2016: Nature Communications
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