Data_Sheet_1_Decision Making Profile of Positive and Negative Anticipatory Skin Conductance Responders in an Unlimited-Time Version of the IGT.xlsx (532.43 kB)
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Data_Sheet_1_Decision Making Profile of Positive and Negative Anticipatory Skin Conductance Responders in an Unlimited-Time Version of the IGT.xlsx

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posted on 15.10.2019, 07:29 by Ana Merchán-Clavellino, María P. Salguero-Alcañiz, Fernando Barbosa, Jose R. Alameda-Bailén

Based on the somatic marker hypothesis (Damasio, 1994), many studies have examined whether or not physiological responses are “somatic markers” that implicitly guide the decision making process. Vegetative or motor reactions that are produced by negative or positive stimuli generate a series of somatic markers. So, when a similar stimuli is encountered in the future, these somatic marks will facilitate favorable decisions and inhibit the disadvantageous ones (Martínez-Selva et al., 2006). The most widely studied physiological responses, as indicators of these markers, are heart rate and the skin conductance response (Damasio, 1994; Bechara et al., 1996). The Iowa Gambling Task (IGT) has been the most widely used tool in this research. The common IGT protocol for psychophysiological studies comprises limited inter-trial intervals, and does not distinguish participants as a function of relevant physiological traits, such as the anticipatory skin conductance response (aSCR). The objectives of this work were to determine whether “somatic markers” guide the decision making process without time restrictions and to examine the effects of opposite aSCR profiles on this process. Participants were 29 healthy subjects, divided into two groups according to positive (+) and negative (−) aSCR. Two different data analysis strategies were applied: firstly, gambling indices were computed and, secondly, we examined the parameters of the probabilistic Prospect Valence Learning (PVL) model in three versions: maximum likelihood estimation (MLE), PVL-Delta and PVL-Decay simulations with Hierarchical Bayesian analysis (HBA) for parameter estimation. The results show a significant group effect in gambling indices, with the aSCR+ group presenting lower risk in the decision making process than the aSCR− group. Significant differences were also observed in the Utility parameter of MLE-PVL, with the aSCR− group have low sensitivity to feedback outcomes, than aSRC+ group. However, data from the PVL simulations do not show significant group differences and, in both cases, the utility value denotes low sensitivity to feedback outcomes.

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