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https://www.readbyqxmd.com/read/29133424/dopamine-reward-prediction-error-signal-codes-the-temporal-evaluation-of-a-perceptual-decision-report
#1
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/29131512/the-role-of-the-anterior-cingulate-cortex-in-prediction-error-and-signaling-surprise
#2
William H Alexander, Joshua W Brown
In the past two decades, reinforcement learning (RL) has become a popular framework for understanding brain function. A key component of RL models, prediction error, has been associated with neural signals throughout the brain, including subcortical nuclei, primary sensory cortices, and prefrontal cortex. Depending on the location in which activity is observed, the functional interpretation of prediction error may change: Prediction errors may reflect a discrepancy in the anticipated and actual value of reward, a signal indicating the salience or novelty of a stimulus, and many other interpretations...
November 13, 2017: Topics in Cognitive Science
https://www.readbyqxmd.com/read/29126885/medial-frontal-cortex-response-to-unexpected-motivationally-salient-outcomes
#3
Heather E Soder, Geoffrey F Potts
The medial frontal cortex (MFC) plays a central role allocating resources to process salient information, in part by responding to prediction errors. While there is some recent debate, the feedback-related negativity (FRN) is thought to index a reward prediction error by signaling outcomes that are worse than expected. A recent study utilizing electric shock provided data inconsistent with these accounts and reported that the omission of both appetitive (money) and aversive outcomes (electric shocks) elicited a medial frontal negativity...
November 7, 2017: International Journal of Psychophysiology
https://www.readbyqxmd.com/read/29109253/a-transient-dopamine-signal-encodes-subjective-value-and-causally-influences-demand-in-an-economic-context
#4
Scott A Schelp, Katherine J Pultorak, Dylan R Rakowski, Devan M Gomez, Gregory Krzystyniak, Raibatak Das, Erik B Oleson
The mesolimbic dopamine system is strongly implicated in motivational processes. Currently accepted theories suggest that transient mesolimbic dopamine release events energize reward seeking and encode reward value. During the pursuit of reward, critical associations are formed between the reward and cues that predict its availability. Conditioned by these experiences, dopamine neurons begin to fire upon the earliest presentation of a cue, and again at the receipt of reward. The resulting dopamine concentration scales proportionally to the value of the reward...
November 6, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29103933/optogenetic-blockade-of-dopamine-transients-prevents-learning-induced-by-changes-in-reward-features
#5
Chun Yun Chang, Matthew Gardner, Maria Gonzalez Di Tillio, Geoffrey Schoenbaum
Prediction errors are critical for associative learning [1, 2]. Transient changes in dopamine neuron activity correlate with positive and negative reward prediction errors and can mimic their effects [3-15]. However, although causal studies show that dopamine transients of 1-2 s are sufficient to drive learning about reward, these studies do not address whether they are necessary (but see [11]). Further, the precise nature of this signal is not yet fully established. Although it has been equated with the cached-value error signal proposed to support model-free reinforcement learning, cached-value errors are typically confounded with errors in the prediction of reward features [16]...
November 20, 2017: Current Biology: CB
https://www.readbyqxmd.com/read/29096115/model-based-predictions-for-dopamine
#6
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/29089641/roles-of-dopamine-neurons-in-mediating-the-prediction-error-in-aversive-learning-in-insects
#7
Kanta Terao, Makoto Mizunami
In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. The prediction error theory has been proposed to account for the finding of a blocking phenomenon, in which pairing of a stimulus X with an unconditioned stimulus (US) could block subsequent association of a second stimulus Y to the US when the two stimuli were paired in compound with the same US. Evidence for this theory, however, has been imperfect since blocking can also be accounted for by competitive theories...
October 31, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29080714/hippocampal-morphology-mediates-biased-memories-of-chronic-pain
#8
Sara E Berger, Étienne Vachon-Presseau, Taha B Abdullah, Alex T Baria, Thomas J Schnitzer, A Vania Apkarian
Experiences and memories are often mismatched. While multiple studies have investigated psychological underpinnings of recall error with respect to emotional events, the neurobiological mechanisms underlying the divergence between experiences and memories remain relatively unexplored in the domain of chronic pain. Here we examined the discrepancy between experienced chronic low back pain (CBP) intensity (twice daily ratings) and remembered pain intensity (n = 48 subjects) relative to psychometric properties, hippocampus morphology, memory capabilities, and personality traits related to reward...
November 6, 2017: NeuroImage
https://www.readbyqxmd.com/read/29078084/strategic-adaptation-to-non-reward-prediction-error-qualities-and-irreducible-uncertainty-in-fmri
#9
Daniel S Kluger, Ricarda I Schubotz
Prediction errors are deemed necessary for the updating of internal models of the environment, prompting us to stop or assert current action plans and helping us to adapt to environmental features. The aim of the present study was twofold: First, we sought to determine the neural underpinnings of qualitatively different abstract prediction errors in a serial pattern detection task. Distinct frontoparietal components were found for sequential terminations (inferior frontal gyrus - IFG) and extensions (superior frontal sulcus, posterior cingulate cortex, and angular gyrus), respectively...
October 3, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/29076112/reward-and-value-coding-by-dopamine-neurons-in-non-human-primates
#10
REVIEW
Aydin Alikaya, Mackenzie Rack-Wildner, William R Stauffer
Rewards are fundamental to everyday life. They confer pleasure, support learning, and mediate decisions. Dopamine-releasing neurons in the midbrain are critical for reward processing. These neurons receive input from more than 30 brain areas and send widespread projections to the basal ganglia and frontal cortex. Their phasic responses are tuned to rewards. Specifically, dopamine signals code reward prediction error, the difference between received and predicted rewards. Decades of research in awake, behaving non-human primates (NHP), have shown the importance of these neural signals for learning and decision making...
October 26, 2017: Journal of Neural Transmission
https://www.readbyqxmd.com/read/29075184/an-update-on-the-role-of-serotonin-and-its-interplay-with-dopamine-for-reward
#11
REVIEW
Adrian G Fischer, Markus Ullsperger
The specific role of serotonin and its interplay with dopamine (DA) in adaptive, reward guided behavior as well as drug dependance, still remains elusive. Recently, novel methods allowed cell type specific anatomical, functional and interventional analyses of serotonergic and dopaminergic circuits, promising significant advancement in understanding their functional roles. Furthermore, it is increasingly recognized that co-release of neurotransmitters is functionally relevant, understanding of which is required in order to interpret results of pharmacological studies and their relationship to neural recordings...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/29064617/social-comparison-in-the-brain-a-coordinate-based-meta-analysis-of-functional-brain-imaging-studies-on-the-downward-and-upward-comparisons
#12
Yi Luo, Simon B Eickhoff, Sébastien Hétu, Chunliang Feng
Social comparison is ubiquitous across human societies with dramatic influence on people's well-being and decision making. Downward comparison (comparing to worse-off others) and upward comparison (comparing to better-off others) constitute two types of social comparisons that produce different neuropsychological consequences. Based on studies exploring neural signatures associated with downward and upward comparisons, the current study utilized a coordinate-based meta-analysis to provide a refinement of understanding about the underlying neural architecture of social comparison...
October 24, 2017: Human Brain Mapping
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
#13
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/29040494/beyond-reward-prediction-errors-human-striatum-updates-rule-values-during-learning
#14
Ian Ballard, Eric M Miller, Steven T Piantadosi, Noah D Goodman, Samuel M McClure
Humans naturally group the world into coherent categories defined by membership rules. Rules can be learned implicitly by building stimulus-response associations using reinforcement learning or by using explicit reasoning. We tested if the striatum, in which activation reliably scales with reward prediction error, would track prediction errors in a task that required explicit rule generation. Using functional magnetic resonance imaging during a categorization task, we show that striatal responses to feedback scale with a "surprise" signal derived from a Bayesian rule-learning model and are inconsistent with RL prediction error...
October 13, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/29035335/selectivity-enhancement-in-electronic-nose-based-on-an-optimized-dqn
#15
Yu Wang, Jianguo Xing, Shu Qian
In order to enhance the selectivity of metal oxide gas sensors, we use a flow modulation method to exploit transient sensor information. The method is based on modulating the flow of the carrier gas that brings the species to be measured into the sensor chamber. We present an active perception strategy by using a DQN which can optimize the flow modulation online. The advantage of DQN is not only that the classification accuracy is higher than traditional methods such as PCA, but also that it has a good adaptability under small samples and labeled data...
October 16, 2017: Sensors
https://www.readbyqxmd.com/read/29025688/altered-medial-frontal-feedback-learning-signals-in-anorexia-nervosa
#16
Fabio Bernardoni, Daniel Geisler, Joseph A King, Amir-Homayoun Javadi, Franziska Ritschel, Julia Murr, Andrea M F Reiter, Veit Rössner, Michael N Smolka, Stefan Kiebel, Stefan Ehrlich
BACKGROUND: In their relentless pursuit of thinness, individuals with anorexia nervosa (AN) engage in maladaptive behaviors (restrictive food choices and overexercising) that may originate in altered decision making and learning. METHODS: In this functional magnetic resonance imaging study, we employed computational modeling to elucidate the neural correlates of feedback learning and value-based decision making in 36 female patients with AN and 36 age-matched healthy volunteers (12-24 years)...
August 30, 2017: Biological Psychiatry
https://www.readbyqxmd.com/read/28986462/the-lateral-habenula-and-its-input-to-the-rostromedial-tegmental-nucleus-mediates-outcome-specific-conditioned-inhibition
#17
Vincent Laurent, Felix L Wong, Bernard W Balleine
Animals can readily learn that stimuli predict the absence of specific appetitive outcomes; however, the neural substrates underlying such outcome-specific conditioned inhibition remain largely unexplored. Here, we examined the involvement of the lateral habenula (LHb) and of its inputs onto the rostromedial tegmental nucleus (RMTg) in inhibitory learning using female and male rats as subjects. In these experiments, we used backward conditioning and contingency reversal to establish outcome-specific conditioned inhibitors for two distinct appetitive outcomes, and then assessed the effects of manipulations of the LHb and the LHb-RMTg pathway on that inhibitory encoding using the Pavlovian-instrumental transfer paradigm...
October 6, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28985550/the-many-worlds-hypothesis-of-dopamine-prediction-error-implications-of-a-parallel-circuit-architecture-in-the-basal-ganglia
#18
REVIEW
Brian Lau, Tiago Monteiro, Joseph J Paton
Computational models of reinforcement learning (RL) strive to produce behavior that maximises reward, and thus allow software or robots to behave adaptively [1]. At the core of RL models is a learned mapping between 'states'-situations or contexts that an agent might encounter in the world-and actions. A wealth of physiological and anatomical data suggests that the basal ganglia (BG) is important for learning these mappings [2,3]. However, the computations performed by specific circuits are unclear. In this brief review, we highlight recent work concerning the anatomy and physiology of BG circuits that suggest refinements in our understanding of computations performed by the basal ganglia...
October 3, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28981876/human-substantia-nigra-and-ventral-tegmental-area-involvement-in-computing-social-error-signals-during-the-ultimatum-game
#19
S Hétu, Y Luo, K D'Ardenne, T Lohrenz, P R Montague
Social norms play an essential role in our societies, and since the social environment is changing constantly, our internal models of it also need to change. In humans, there is mounting evidence that neural structures such as the insula and the ventral striatum are involved in detecting norm violation and updating internal models. However, because of methodological challenges, little is known about the possible involvement of midbrain structures in detecting norm violation and updating internal models of our norms...
August 17, 2017: Social Cognitive and Affective Neuroscience
https://www.readbyqxmd.com/read/28981514/a-unifying-bayesian-account-of-contextual-effects-in-value-based-choice
#20
Francesco Rigoli, Christoph Mathys, Karl J Friston, Raymond J Dolan
Empirical evidence suggests the incentive value of an option is affected by other options available during choice and by options presented in the past. These contextual effects are hard to reconcile with classical theories and have inspired accounts where contextual influences play a crucial role. However, each account only addresses one or the other of the empirical findings and a unifying perspective has been elusive. Here, we offer a unifying theory of context effects on incentive value attribution and choice based on normative Bayesian principles...
October 2017: PLoS Computational Biology
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