Abstract
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.
Original language | English (US) |
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Pages (from-to) | 67-73 |
Number of pages | 7 |
Journal | Current Opinion in Behavioral Sciences |
Volume | 11 |
DOIs | |
State | Published - Oct 1 2016 |
All Science Journal Classification (ASJC) codes
- Psychiatry and Mental health
- Cognitive Neuroscience
- Behavioral Neuroscience