Reward and Decision Making in Corticobasal Ganglia Networks

Front Cover
Bernard Balleine
Wiley, Jul 10, 2007 - Education - 379 pages
The neural bases of decision-making processes are currently generating considerable experimental and theoretical interest. Several recent developments in neuroscience, psychology, and economics have helped to focus thinking on this issue: the computational description of cortico-striatal networks in terms of reinforcement learning models, recognition that midbrain dopaminergic activity could reflect an error correction learning signal, improvements in imaging technology, the recognition that multiple controllers of actions and of values contribute to the development of adaptive behavior, and the recognition that theories of value derived from economics can provide a principled means of incorporating intangible factors such as risk, uncertainty, and temporal discounting into computational models of neural system.

The contributions to this volume are forward-looking assessments of the current and future issues likely to be faced by researchers in this area. Four current issues are specifically addressed:

- the degree to which distinct behavioral and psychological capacities map onto discrete neural systems;
- the relationship between midbrain dopamine activity and reward in terms of phasic and tonic activity, prefrontal and striatal targets, and whether and what limits exist in terms of learning;
- whether there are multiple prediction error signals distinguishing between, for example, reward and punishment, instrumental versus Pavlovian conditioning, goals and habits, learning with different discount factors, real versus fictive learning; and
- the relationship between economics and neuroscience, which has recently emerged in a marriage to form the new field of neuroeconomics. Will this marriage be productive for the long term or swiftly head for divorce?

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Learning about Multiple Attributes of Reward in Pavlovian Conditioning
Stay or Should I Go? Transformation of TimeDiscounted Rewards
ModelBased fMRI and Its Application to Reward Learning and Decision

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