A Primer on Reinforcement Learning in the Brain

Author(s):  
Elliot A. Ludvig ◽  
Marc G. Bellemare ◽  
Keir G. Pearson

In the last 15 years, there has been a flourishing of research into the neural basis of reinforcement learning, drawing together insights and findings from psychology, computer science, and neuroscience. This remarkable confluence of three fields has yielded a growing framework that begins to explain how animals and humans learn to make decisions in real time. Mastering the literature in this sub-field can be quite daunting as this task can require mastery of at least three different disciplines, each with its own jargon, perspectives, and shared background knowledge. In this chapter, the authors attempt to make this fascinating line of research more accessible to researchers in any of the constitutive sub-disciplines. To this end, the authors develop a primer for reinforcement learning in the brain that lays out in plain language many of the key ideas and concepts that underpin research in this area. This primer is embedded in a literature review that aims not to be comprehensive, but rather representative of the types of questions and answers that have arisen in the quest to understand reinforcement learning and its neural substrates. Drawing on the basic findings in this research enterprise, the authors conclude with some speculations about how these developments in computational neuroscience may influence future developments in Artificial Intelligence.

2008 ◽  
Vol 31 (5) ◽  
pp. 523-524
Author(s):  
Patricia M. Greenfield ◽  
Kristen Gillespie-Lynch

AbstractWe propose that some aspects of language – notably intersubjectivity – evolved to fit the brain, whereas other aspects – notably grammar – co-evolved with the brain. Cladistic analysis indicates that common basic structures of both action and grammar arose in phylogeny six million years ago and in ontogeny before age two, with a shared prefrontal neural substrate. In contrast, mirror neurons, found in both humans and monkeys, suggest that the neural basis for intersubjectivity evolved before language. Natural selection acts upon genes controlling the neural substrates of these phenotypic language functions.


Author(s):  
Patricia L Lockwood ◽  
Miriam C Klein-Flügge

Abstract Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.


2019 ◽  
Author(s):  
Patricia Lockwood ◽  
Miriam Klein-Flugge

Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalising and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.


1979 ◽  
Vol 1 (2) ◽  
pp. 129-150
Author(s):  
Alvar Ellegard

The last few years have seen a tremendous increase in studies concerning psycholinguistics. The increase is clearly due to many factors. The chief impetus was probably the revolution in linguistic thinking effected by generative grammar (Levelt 1974, I, Preface). Another was a change in the climate of opinion within psychology, away from behaviorism towards a more mentalistic outlook. A third factor are developments within computer science that have led to the establishment of a new field of research called artificial intelligence.


2009 ◽  
Vol 37 (1) ◽  
pp. 313-317 ◽  
Author(s):  
Chantal Martin-Soelch

The neural substrates of MDD (major depressive disorder) are complex and not yet fully understood. In the present review, I provide a short overview of the findings supporting the hypothesis of a dysfunctional dopamine system in the pathophysiology of depression. Because the mesocorticolimbic dopamine system is involved in reward processing, it has been hypothesized that a reduced function of this system could underlie the anhedonia and amotivation associated with depression. This hypothesis is supported by several observations providing indirect evidence for reduced central dopaminergic transmission in depression. However, some of the differences observed between controls and depressed patients in dopamine function seem to be specific to a subsample of patients, and influenced by the methods chosen. Studies that investigated the neural bases of some MDD behavioural symptoms showed that anhedonia, loss of motivation and the diminished ability to concentrate or make decisions could be associated with a blunted reaction to positive reinforcers and rewards on one side, and with a bias towards negative feedback on the other side. Only a few studies have investigated the neural basis of anhedonia and the responses to rewards in MDD subjects, mostly evidencing a blunted response to reward signals that was associated with reduced brain activation in regions associated with the brain reward system. In conclusion, there is evidence for a dysfunction of the dopamine system in depression and for blunted response to reward signals. However, the exact nature of this dysfunction is not yet clear and needs to be investigated in further studies.


2011 ◽  
Vol 21 (1) ◽  
pp. 5-14
Author(s):  
Christy L. Ludlow

The premise of this article is that increased understanding of the brain bases for normal speech and voice behavior will provide a sound foundation for developing therapeutic approaches to establish or re-establish these functions. The neural substrates involved in speech/voice behaviors, the types of muscle patterning for speech and voice, the brain networks involved and their regulation, and how they can be externally modulated for improving function will be addressed.


2020 ◽  
Author(s):  
Avishek Choudhury

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.


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