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Excessive flexibility? Recurrent neural networks can accommodate individual differences in reinforcement learning through in-context adaptation
2 references·0 citing·12 second hopsampled
Excessive flexibility? Recurrent neural networks can accommodate individual differences in reinforcement learning through in-context adaptation (2024)Excessive flexibility? Recurrent neural n…Using deep learning to predict human decisions and using cognitive models to explain deep learning models (2022)Using deep learning to predic…Model-based estimation of subjective values using choice tasks with probabilistic feedback (2017)Model-based estimation of sub…Prospect Theory: An Analysis of Decision under Risk (1979)Prospect theory: An analysis of decision under risk (1988)Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria (1998)Psychiatric symptoms influence reward‐seeking and loss‐avoidance decision‐making through common and distinct computational processes (2021)Externally Provided Rewards Increase Internal Preference, but Not as Much as Preferred Ones Without Extrinsic Rewards (2024)Action repetition biases choice in context-dependent decision-making (2025)Judgment under Uncertainty: Heuristics and Biases (1974)Judgment under Uncertainty: Heuristics and Biases (1975)Machine behaviour (2019)Using recurrent neural network to estimate irreducible stochasticity in human choice-behavior (2023)Using recurrent neural network to estimate irreducible stochasticity in human choice behavior (2024)Using recurrent neural network to estimate irreducible stochasticity in human choice behavior (2024)
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References

  • 2022Using deep learning to predict human decisions and using cognitive models to explain deep learning models62 cited
  • 2017Model-based estimation of subjective values using choice tasks with probabilistic feedback24 cited

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