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Comparing the motivational value of rewards and losses in an EEG-pupillometry study.

Thomas CarstenMariam KostandyanCarsten N BoehlerRuth M Krebs
Published in: The European journal of neuroscience (2020)
We found earlier that performance-contingent rewards lead to faster performance than equivalent losses [Carsten, Hoofs, Boehler, & Krebs, 2019. Motivation Science, 5(3). http://dx.doi.org/10.1037/mot0000117]. Here, we further tested the hypothesis that motivation to gain rewards is higher than to avoid losses, even when incentive values are matched. As implicit markers of motivation, we assessed electroencephalography (EEG) focusing on the P3 after target and feedback onset, and the Feedback-Related Negativity (FRN), as well as simultaneously recorded pupil size. Comparing only reward and loss prospect trials in Experiment 1, we found no consistent differences in behavior and electrophysiological markers of motivation, although pupil data suggested higher arousal after feedback in potential-loss trials. Including additional no-incentive trials in Experiment 2, we found consistent evidence that motivation to gain rewards was higher than to avoid losses: In line with behavior, the target-P3 was most pronounced for reward-related stimuli, followed by loss and no-incentive ones. This same ranking was found in the P3 and the FRN after positive outcomes (i.e., reward, avoided loss, and correct feedback in no-incentive trials). Negative outcomes featured a different pattern in line with the pupil response, which suggests that losses are emotionally salient events, without invigorating behavior proportionally. In sum, these findings suggest that the motivation to gain rewards is more pronounced than motivation to avoid equivalent losses, at least in tasks promoting transient increases in attention triggered by incentive prospect. These motivational differences may arise as avoided losses are not profitable in the long term, in contrast to gained rewards.
Keyphrases
  • working memory
  • public health
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  • resting state
  • electronic health record
  • computed tomography
  • risk assessment
  • big data
  • prefrontal cortex
  • high density