2021
DOI: 10.1371/journal.pone.0245890
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Isomorphic decisional biases across perceptual tasks

Abstract: Humans adjust their behavioral strategies to maximize rewards. However, in the laboratory, human decisional biases exist and persist in two alternative tasks, even when this behavior leads to a loss in utilities. Such biases constitute the tendency to choose one action over others and emerge from a combination of external and internal factors that are specific for each individual. Here, we explored the idea that internally-mediated decisional biases should stably occur and, hence, be reflected across multiple … Show more

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Cited by 10 publications
(25 citation statements)
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“…Next, we quantified a moving average of the participants' % correct sidechoices (the blue line corresponds to group averaged choices, span = 40 trials, Figure 2H). As expected, participants exhibited various learning rates based on how they used the feedback information (Pulcu & Browning, 2017;Trevino et al, 2021). We grouped the learning curves by reinforcement conditions and found no net left versus right bias (Kruskal-Wallis multicomparison test, p < .001; Figure 2H).…”
Section: Multimetric Behavioral Profiles Extracted From a Reversal-le...supporting
confidence: 62%
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“…Next, we quantified a moving average of the participants' % correct sidechoices (the blue line corresponds to group averaged choices, span = 40 trials, Figure 2H). As expected, participants exhibited various learning rates based on how they used the feedback information (Pulcu & Browning, 2017;Trevino et al, 2021). We grouped the learning curves by reinforcement conditions and found no net left versus right bias (Kruskal-Wallis multicomparison test, p < .001; Figure 2H).…”
Section: Multimetric Behavioral Profiles Extracted From a Reversal-le...supporting
confidence: 62%
“…Thus, the BL algorithm computes a threedimensional density function of r, v, and k (the rate of change of v) for every trial. r (or v) is a weighted mean of the marginal distribution integrated by v (or r) and k. This ideal-observer model takes the reward rate from previous observations and transforms the agent's belief about the task's state by updating representations of reward and volatility through multivariate probability distributions (Behrens et al, 2007;Trevino et al, 2021). A complete breakdown of the BL equations and methods can be found in the original publication's supplementary material (Behrens et al, 2007).…”
Section: Multimetric Behavioral Profilementioning
confidence: 99%
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