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Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making
12 references·0 citing·49 second hop
Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making (2021)Input-dependent noise can explain magnitu…Magnitude-sensitive reaction times reveal non-linear time costs in multi-alternative decision-making (2022)Magnitude-sensitive reaction …Sensitivity of reaction time to the magnitude of rewards reveals the cost-structure of time (2019)Sensitivity of reaction time …Comparison of magnitude-sensitive sequential sampling models in a simulation-based study (2019)Comparison of magnitude-sensi…Comment on ‘Optimal Policy for Multi-Alternative Decisions’ (2019)Comment on ‘Optimal Policy fo…Gaze Amplifies Value in Decision Making (2018)Gaze Amplifies Value in Decis…Evidence for the speed–value trade-off: Human and monkey decision making is magnitude sensitive. (2017)Evidence for the speed–value …Strength and weight: The determinants of choice and confidence (2016)Strength and weight: The dete…Optimal policy for value-based decision-making (2016)Optimal policy for value-base…Absolutely relative or relatively absolute: violations of value invariance in human decision making (2015)Absolutely relative or relati…Optimal decision making in heterogeneous and biased environments (2014)Optimal decision making in he…Normalization is a general neural mechanism for context-dependent decision making (2013)Normalization is a general ne…The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure (2010)The Drift Diffusion Model can…Context-Dependent Preferences (1993)Multialternative decision field theory: A dynamic connectionst model of decision making. (2001)Loss Aversion and Inhibition in Dynamical Models of Multialternative Choice. 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(1993)HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python (2013)Ownership, Learning, and Beliefs (2019)Value-based attention but not divisive normalization influences decisions with multiple alternatives (2020)Prospect Theory: An Analysis of Decision under Risk (1979)Prospect theory: An analysis of decision under risk (1988)Advances in prospect theory: Cumulative representation of uncertainty (1992)Dorsolateral prefrontal cortex plays causal role in probability weighting during risky choice (2022)Sequential delay and probability discounting tasks in mice reveal anchoring effects partially attributable to decision noise (2022)Time Spent Thinking in Online Chess Reflects the Value of Computation (2025)The Neural Basis of Loss Aversion in Decision-Making Under Risk (2007)Neuroeconomics: Decision Making and the Brain (2009)Neuroeconomic Foundations of Economic Choice—Recent Advances (2011)The Dynamics of Decision Making in Risky Choice: An Eye-Tracking Analysis (2012)Dietary Self-Control Is Related to the Speed With Which Attributes of Healthfulness and Tastiness Are Processed (2014)The evolution of decision rules in complex environments (2014)High-value decisions are made quickly, with no consistent effect on accuracy (2025)
this work references cited by second hop

References

  • 2013Normalization is a general neural mechanism for context-dependent decision making341 cited
  • 2010The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure321 cited
  • 2016Optimal policy for value-based decision-making210 cited
  • 2018Gaze Amplifies Value in Decision Making204 cited
  • 2015Absolutely relative or relatively absolute: violations of value invariance in human decision making90 cited
  • 2014Optimal decision making in heterogeneous and biased environments83 cited
  • 2017Evidence for the speed–value trade-off: Human and monkey decision making is magnitude sensitive.76 cited
  • 2016Strength and weight: The determinants of choice and confidence41 cited
  • 2019Sensitivity of reaction time to the magnitude of rewards reveals the cost-structure of time21 cited
  • 2022Magnitude-sensitive reaction times reveal non-linear time costs in multi-alternative decision-making8 cited
  • 2019Comment on ‘Optimal Policy for Multi-Alternative Decisions’8 cited
  • 2019Comparison of magnitude-sensitive sequential sampling models in a simulation-based study6 cited

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