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Yael Niv

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https://www.readbyqxmd.com/read/27599017/the-effects-of-aging-on-the-interaction-between-reinforcement-learning-and-attention
#1
Angela Radulescu, Reka Daniel, Yael Niv
Reinforcement learning (RL) in complex environments relies on selective attention to uncover those aspects of the environment that are most predictive of reward. Whereas previous work has focused on age-related changes in RL, it is not known whether older adults learn differently from younger adults when selective attention is required. In 2 experiments, we examined how aging affects the interaction between RL and selective attention. Younger and older adults performed a learning task in which only 1 stimulus dimension was relevant to predicting reward, and within it, 1 "target" feature was the most rewarding...
September 5, 2016: Psychology and Aging
https://www.readbyqxmd.com/read/27588604/global-mapping-of-small-rna-target-interactions-in-bacteria
#2
Sahar Melamed, Asaf Peer, Raya Faigenbaum-Romm, Yair E Gatt, Niv Reiss, Amir Bar, Yael Altuvia, Liron Argaman, Hanah Margalit
Small RNAs (sRNAs) associated with the RNA chaperon protein Hfq are key posttranscriptional regulators of gene expression in bacteria. Deciphering the sRNA-target interactome is an essential step toward understanding the roles of sRNAs in the cellular networks. We developed a broadly applicable methodology termed RIL-seq (RNA interaction by ligation and sequencing), which integrates experimental and computational tools for in vivo transcriptome-wide identification of interactions involving Hfq-associated sRNAs...
September 1, 2016: Molecular Cell
https://www.readbyqxmd.com/read/27466328/a-probability-distribution-over-latent-causes-in-the-orbitofrontal-cortex
#3
Stephanie C Y Chan, Yael Niv, Kenneth A Norman
UNLABELLED: The orbitofrontal cortex (OFC) has been implicated in both the representation of "state," in studies of reinforcement learning and decision making, and also in the representation of "schemas," in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or "latent cause" that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes' rule to compute a posterior probability distribution over latent causes...
July 27, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/27408906/reinforcement-learning-with-marr
#4
Yael Niv, Angela Langdon
To many, the poster child for David Marr's famous three levels of scientific inquiry is reinforcement learning-a computational theory of reward optimization, which readily prescribes algorithmic solutions that evidence striking resemblance to signals found in the brain, suggesting a straightforward neural implementation. Here we review questions that remain open at each level of analysis, concluding that the path forward to their resolution calls for inspiration across levels, rather than a focus on mutual constraints...
October 2016: Current Opinion in Behavioral Sciences
https://www.readbyqxmd.com/read/27292535/temporal-specificity-of-reward-prediction-errors-signaled-by-putative-dopamine-neurons-in-rat-vta-depends-on-ventral-striatum
#5
Yuji K Takahashi, Angela J Langdon, Yael Niv, Geoffrey Schoenbaum
Dopamine neurons signal reward prediction errors. This requires accurate reward predictions. It has been suggested that the ventral striatum provides these predictions. Here we tested this hypothesis by recording from putative dopamine neurons in the VTA of rats performing a task in which prediction errors were induced by shifting reward timing or number. In controls, the neurons exhibited error signals in response to both manipulations. However, dopamine neurons in rats with ipsilateral ventral striatal lesions exhibited errors only to changes in number and failed to respond to changes in timing of reward...
July 6, 2016: Neuron
https://www.readbyqxmd.com/read/27249418/dyt1-dystonia-increases-risk-taking-in-humans
#6
David Arkadir, Angela Radulescu, Deborah Raymond, Naomi Lubarr, Susan B Bressman, Pietro Mazzoni, Yael Niv
It has been difficult to link synaptic modification to overt behavioral changes. Rodent models of DYT1 dystonia, a motor disorder caused by a single gene mutation, demonstrate increased long-term potentiation and decreased long-term depression in corticostriatal synapses. Computationally, such asymmetric learning predicts risk taking in probabilistic tasks. Here we demonstrate abnormal risk taking in DYT1 dystonia patients, which is correlated with disease severity, thereby supporting striatal plasticity in shaping choice behavior in humans...
2016: ELife
https://www.readbyqxmd.com/read/26687216/the-state-of-the-orbitofrontal-cortex
#7
COMMENT
Melissa J Sharpe, Andrew M Wikenheiser, Yael Niv, Geoffrey Schoenbaum
State representation is fundamental to behavior. However, identifying the true state of the world is challenging when explicit cues are ambiguous. Here, Bradfield and colleagues show that the medial OFC is critical for using associative information to discriminate ambiguous states.
December 16, 2015: Neuron
https://www.readbyqxmd.com/read/26545853/mood-as-representation-of-momentum
#8
REVIEW
Eran Eldar, Robb B Rutledge, Raymond J Dolan, Yael Niv
Experiences affect mood, which in turn affects subsequent experiences. Recent studies suggest two specific principles. First, mood depends on how recent reward outcomes differ from expectations. Second, mood biases the way we perceive outcomes (e.g., rewards), and this bias affects learning about those outcomes. We propose that this two-way interaction serves to mitigate inefficiencies in the application of reinforcement learning to real-world problems. Specifically, we propose that mood represents the overall momentum of recent outcomes, and its biasing influence on the perception of outcomes 'corrects' learning to account for environmental dependencies...
January 2016: Trends in Cognitive Sciences
https://www.readbyqxmd.com/read/26502337/recurrent-inactivating-rasa2-mutations-in-melanoma
#9
Rand Arafeh, Nouar Qutob, Rafi Emmanuel, Alona Keren-Paz, Jason Madore, Abdel Elkahloun, James S Wilmott, Jared J Gartner, Antonella Di Pizio, Sabina Winograd-Katz, Sivasish Sindiri, Ron Rotkopf, Ken Dutton-Regester, Peter Johansson, Antonia L Pritchard, Nicola Waddell, Victoria K Hill, Jimmy C Lin, Yael Hevroni, Steven A Rosenberg, Javed Khan, Shifra Ben-Dor, Masha Y Niv, Igor Ulitsky, Graham J Mann, Richard A Scolyer, Nicholas K Hayward, Yardena Samuels
Analysis of 501 melanoma exomes identified RASA2, encoding a RasGAP, as a tumor-suppressor gene mutated in 5% of melanomas. Recurrent loss-of-function mutations in RASA2 were found to increase RAS activation, melanoma cell growth and migration. RASA2 expression was lost in ≥30% of human melanomas and was associated with reduced patient survival. These findings identify RASA2 inactivation as a melanoma driver and highlight the importance of RasGAPs in cancer.
December 2015: Nature Genetics
https://www.readbyqxmd.com/read/26447572/rethinking-extinction
#10
REVIEW
Joseph E Dunsmoor, Yael Niv, Nathaniel Daw, Elizabeth A Phelps
Extinction serves as the leading theoretical framework and experimental model to describe how learned behaviors diminish through absence of anticipated reinforcement. In the past decade, extinction has moved beyond the realm of associative learning theory and behavioral experimentation in animals and has become a topic of considerable interest in the neuroscience of learning, memory, and emotion. Here, we review research and theories of extinction, both as a learning process and as a behavioral technique, and consider whether traditional understandings warrant a re-examination...
October 7, 2015: Neuron
https://www.readbyqxmd.com/read/26086934/is-model-fitting-necessary-for-model-based-fmri
#11
Robert C Wilson, Yael Niv
Model-based analysis of fMRI data is an important tool for investigating the computational role of different brain regions. With this method, theoretical models of behavior can be leveraged to find the brain structures underlying variables from specific algorithms, such as prediction errors in reinforcement learning. One potential weakness with this approach is that models often have free parameters and thus the results of the analysis may depend on how these free parameters are set. In this work we asked whether this hypothetical weakness is a problem in practice...
June 2015: PLoS Computational Biology
https://www.readbyqxmd.com/read/26019331/reinforcement-learning-in-multidimensional-environments-relies-on-attention-mechanisms
#12
RANDOMIZED CONTROLLED TRIAL
Yael Niv, Reka Daniel, Andra Geana, Samuel J Gershman, Yuan Chang Leong, Angela Radulescu, Robert C Wilson
In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this "representation learning" process is realized in humans...
May 27, 2015: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/25943767/causal-model-comparison-shows-that-human-representation-learning-is-not-bayesian
#13
COMPARATIVE STUDY
Andra Geana, Yael Niv
How do we learn what features of our multidimensional environment are relevant in a given task? To study the computational process underlying this type of "representation learning," we propose a novel method of causal model comparison. Participants played a probabilistic learning task that required them to identify one relevant feature among several irrelevant ones. To compare between two models of this learning process, we ran each model alongside the participant during task performance, making predictions regarding the values underlying the participant's choices in real time...
2014: Cold Spring Harbor Symposia on Quantitative Biology
https://www.readbyqxmd.com/read/25808176/novelty-and-inductive-generalization-in-human-reinforcement-learning
#14
Samuel J Gershman, Yael Niv
In reinforcement learning (RL), a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of RL in humans and animals...
July 2015: Topics in Cognitive Science
https://www.readbyqxmd.com/read/25804519/the-effect-of-non-obstetric-invasive-procedures-during-pregnancy-on-perinatal-outcomes
#15
COMPARATIVE STUDY
Polina Schwarzman, Yael Baumfeld, Zehavi Bar-Niv, Joel Baron, Salvatore Andrea Mastrolia, Eyal Sheiner, Moshe Mazor, Reli Hershkovitz, Adi Yehuda Weintraub
PURPOSE: To evaluate the effect of non-obstetric invasive procedure during pregnancy on perinatal outcome. METHODS: The present retrospective study investigated perinatal outcome in women that underwent an invasive procedure during one of their pregnancies (n = 61); perinatal outcome was compared to other pregnancies (without an invasive procedure) of the same patients (n = 122). RESULTS: Women with a non-obstetric invasive procedure during pregnancy delivered earlier than those in the comparison group (38...
September 2015: Archives of Gynecology and Obstetrics
https://www.readbyqxmd.com/read/25733879/how-to-divide-and-conquer-the-world-one-step-at-a-time
#16
COMMENT
Reka Daniel, Nicolas W Schuck, Yael Niv
No abstract text is available yet for this article.
March 10, 2015: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/25608088/interaction-between-emotional-state-and-learning-underlies-mood-instability
#17
Eran Eldar, Yael Niv
Intuitively, good and bad outcomes affect our emotional state, but whether the emotional state feeds back onto the perception of outcomes remains unknown. Here, we use behaviour and functional neuroimaging of human participants to investigate this bidirectional interaction, by comparing the evaluation of slot machines played before and after an emotion-impacting wheel-of-fortune draw. Results indicate that self-reported mood instability is associated with a positive-feedback effect of emotional state on the perception of outcomes...
2015: Nature Communications
https://www.readbyqxmd.com/read/25417104/transkingdom-control-of-microbiota-diurnal-oscillations-promotes-metabolic-homeostasis
#18
Christoph A Thaiss, David Zeevi, Maayan Levy, Gili Zilberman-Schapira, Jotham Suez, Anouk C Tengeler, Lior Abramson, Meirav N Katz, Tal Korem, Niv Zmora, Yael Kuperman, Inbal Biton, Shlomit Gilad, Alon Harmelin, Hagit Shapiro, Zamir Halpern, Eran Segal, Eran Elinav
All domains of life feature diverse molecular clock machineries that synchronize physiological processes to diurnal environmental fluctuations. However, no mechanisms are known to cross-regulate prokaryotic and eukaryotic circadian rhythms in multikingdom ecosystems. Here, we show that the intestinal microbiota, in both mice and humans, exhibits diurnal oscillations that are influenced by feeding rhythms, leading to time-specific compositional and functional profiles over the course of a day. Ablation of host molecular clock components or induction of jet lag leads to aberrant microbiota diurnal fluctuations and dysbiosis, driven by impaired feeding rhythmicity...
October 23, 2014: Cell
https://www.readbyqxmd.com/read/25375816/statistical-computations-underlying-the-dynamics-of-memory-updating
#19
Samuel J Gershman, Angela Radulescu, Kenneth A Norman, Yael Niv
Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly...
November 2014: PLoS Computational Biology
https://www.readbyqxmd.com/read/25282675/a-free-choice-premium-in-the-basal-ganglia
#20
COMMENT
Yael Niv, Angela Langdon, Angela Radulescu
Apparently, the act of free choice confers value: when selecting between an item that you had previously chosen and an identical item that you had been forced to take, the former is often preferred. What could be the neural underpinnings of this free-choice bias in decision making? An elegant study recently published in Neuron suggests that enhanced reward learning in the basal ganglia may be the culprit.
January 2015: Trends in Cognitive Sciences
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