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Julia Trommershauser, Pascal Mamassian, Laurence T. Maloney; Preference in a stochastic visual cognitive task with probability information learned through experience. Journal of Vision 2008;8(6):469. doi: 10.1167/8.6.469.
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© ARVO (1962-2015); The Authors (2016-present)
Purpose: Subjects' use of probability information in decision making under risk (DMR) is markedly distorted. Recent research hints that probability information learned through experience (Hertwig et al, 2004, Psych. Science) is less distorted. Here we studied visual decision making in a stochastic visual aiming task in which subjects learned the behavior of “stochastic bullets” through experience. The trajectory of the bullet was simulated by a random walk with fixed Gaussian noise with either high or low variance. Variance was coded by the color of the bullet. During a training phase (300 trials) subjects could learn the probabilities of hitting targets of various sizes with either bullet type.
Methods: In the main experiment, subjects were presented with pairs of possible targets differing in width and now assigned monetary values measured in points. They could choose to shoot at one or the other but not both. The widths of the zones were adjusted so that the subject could win points Oi with probability pi, i = 1,2 by choosing to aim at zone i. In eight conditions, we varied the probability p2 of incurring outcome O2 between 0.8, 0.6, 0.4 and 0.2 (low variance), and 0.63, 0.48, 0.28 and 0.15 (high variance) by varying the size of one of the targets. We used two different sets of rewards (O1 = 500 and O2 = 100; and O1 = 400 and O2 = 200), varied across two sessions. The overall score was converted into a bonus paid at the end of the experiment. Six subjects completed the experiment.
Results: We tested whether expected utility theory (EUT) could predict subjects' choices. We found that, even though probability information was learned through experience, subjects' probability ratios were not constant over the range of target widths tested and thus inconsistent with EUT.
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