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model-based reinforcement learning

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https://www.readbyqxmd.com/read/27909102/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...
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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...
October 31, 2016: 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
#9
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
#10
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
#11
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
#12
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
#13
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
https://www.readbyqxmd.com/read/27704531/greedy-outcome-weighted-tree-learning-of-optimal-personalized-treatment-rules
#14
Ruoqing Zhu, Ying-Qi Zhao, Guanhua Chen, Shuangge Ma, Hongyu Zhao
We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree algorithm to pursue signals in a high-dimensional space. In the first step, we transform the treatment selection problem into a weighted classification problem that can utilize tree-based methods. In the second step, we adopt a newly proposed tree-based method, known as reinforcement learning trees, to detect features involved in the optimal treatment rules and to construct binary splitting rules...
October 4, 2016: Biometrics
https://www.readbyqxmd.com/read/27687119/learning-reward-and-decision-making
#15
John P O'Doherty, Jeffrey Cockburn, Wolfgang M Pauli
In this review, we summarize findings supporting the existence of multiple behavioral strategies for controlling reward-related behavior, including a dichotomy between the goal-directed or model-based system and the habitual or model-free system in the domain of instrumental conditioning and a similar dichotomy in the realm of Pavlovian conditioning. We evaluate evidence from neuroscience supporting the existence of at least partly distinct neuronal substrates contributing to the key computations necessary for the function of these different control systems...
September 28, 2016: Annual Review of Psychology
https://www.readbyqxmd.com/read/27652228/psychosexual-therapy-for-delayed-ejaculation-based-on-the-sexual-tipping-point-model
#16
REVIEW
Michael A Perelman
The Sexual Tipping Point(®) (STP) model is an integrated approach to the etiology, diagnosis and treatment of men with delayed ejaculation (DE), including all subtypes manifesting ejaculatory delay or absence [registered trademark owned by the MAP Educational Fund, a 501(c)(3) public charity]. A single pathogenetic pathway does not exist for sexual disorders generally and that is also true for DE specifically. Men with DE have various bio-psychosocial-behavioral & cultural predisposing, precipitating, maintaining, and contextual factors which trigger, reinforce, or worsen the probability of DE occurring...
August 2016: Translational Andrology and Urology
https://www.readbyqxmd.com/read/27647895/evolutionary-learning-of-adaptation-to-varying-environments-through-a-transgenerational-feedback
#17
BingKan Xue, Stanislas Leibler
Organisms can adapt to a randomly varying environment by creating phenotypic diversity in their population, a phenomenon often referred to as "bet hedging." The favorable level of phenotypic diversity depends on the statistics of environmental variations over timescales of many generations. Could organisms gather such long-term environmental information to adjust their phenotypic diversity? We show that this process can be achieved through a simple and general learning mechanism based on a transgenerational feedback: The phenotype of the parent is progressively reinforced in the distribution of phenotypes among the offspring...
October 4, 2016: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/27639720/from-free-energy-to-expected-energy-improving-energy-based-value-function-approximation-in-reinforcement-learning
#18
Stefan Elfwing, Eiji Uchibe, Kenji Doya
Free-energy based reinforcement learning (FERL) was proposed for learning in high-dimensional state and action spaces. However, the FERL method does only really work well with binary, or close to binary, state input, where the number of active states is fewer than the number of non-active states. In the FERL method, the value function is approximated by the negative free energy of a restricted Boltzmann machine (RBM). In our earlier study, we demonstrated that the performance and the robustness of the FERL method can be improved by scaling the free energy by a constant that is related to the size of network...
August 26, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27639719/model-based-reinforcement-learning-with-dimension-reduction
#19
Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derives the optimal policy using the transition model. However, learning an accurate transition model in high-dimensional environments requires a large amount of data which is difficult to obtain. To overcome this difficulty, in this paper, we propose to combine model-based reinforcement learning with the recently developed least-squares conditional entropy (LSCE) method, which simultaneously performs transition model estimation and dimension reduction...
August 24, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27628616/reversal-learning-strategy-in-adolescence-is-associated-with-prefrontal-cortex-activation
#20
Rebecca Boehme, Robert C Lorenz, Tobias Gleich, Lydia Romund, Patricia Pelz, Sabrina Golde, Eva Flemming, Andrew Wold, Lorenz Deserno, Joachim Behr, Diana Raufelder, Andreas Heinz, Anne Beck
Adolescence is a critical maturation period for human cognitive control and executive function. In this study, a large sample of adolescents (n = 85) performed a reversal learning task during functional magnetic resonance imaging. We analyzed behavioral data using a reinforcement learning model to provide individually fitted parameters and imaging data with regard to reward prediction errors (PE). Following a model-based approach, we formed two groups depending on whether individuals tended to update expectations predominantly for the chosen stimulus or also for the unchosen one...
September 15, 2016: European Journal of Neuroscience
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