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Two additional regressors were included in each condition in the form of parametric modulators predictive of (1) amount of sure win magnitude (SW) offered on each trial, and (2) decision weights, w(P), reflective of subjective distortions of actual probabilities as estimated by the behavioral decision-making model outlined above.
We take this difference as the main determinant of our behavioral decision-making model.: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0004957.e001.PNG" class= inline-graphic"/> where, on a given trial, P is the probability of winning the lottery, SW reflects the value of the sure win and γ reflects the curvature of the utility function.
These findings are interpreted in the context of dual process models of behavioral decision making.
Journal of Behavioral Decision Making.
Journal of Behavioral Decision Making, 15, 263-290.
Journal of Behavioral Decision Making, 19(1), 1-26.
Journal of Behavioral Decision Making, 13, 179-190.
Journal of Behavioral Decision Making, 5, 187-200.
Journal of Behavioral Decision Making, 5, 169-186.
The second leads from decision theory to decision analysis and behavioral decision research.
Generative codels, conditional probabilistic models, decision models.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com