TY - JOUR
T1 - Reinforcement learning with Marr
AU - Niv, Yael
AU - Langdon, Angela
N1 - Funding Information:
We are grateful to Gecia Bravo-Hermsdorff, Mingbo Cai, Andra Geana, Nina Rouhani, Nico Schuck and Yeon Soon Shin for valuable comments on this manuscript. This work was funded by the Human Frontier Science Program Organization and by NIMH grant R01MH098861 .
Publisher Copyright:
© 2016 Elsevier Ltd.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.cobeha.2016.04.005
DO - 10.1016/j.cobeha.2016.04.005
M3 - Review article
C2 - 27408906
AN - SCOPUS:84973375982
SN - 2352-1546
VL - 11
SP - 67
EP - 73
JO - Current Opinion in Behavioral Sciences
JF - Current Opinion in Behavioral Sciences
ER -