ArticleScientific reports2019
Predicting the behavioural tendency of loss aversion.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Health-Related, Social and Cognitive Factors Explaining Gambling Addiction.Healthcare (Basel, Switzerland) · 2023Article
- The neural dynamics of loss aversion.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- The influence of ergodicity on risk affinity of timed and non-timed respondents.Scientific reports · 2022Article
- How Cues of Being Watched Promote Risk Seeking in Fund Investment in Older Adults.Frontiers in psychology · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Loss aversion manifests itself in rejecting a gamble of gaining or losing the same amount of money with equal chance. Although loss aversion is a well-known and intensively studied phenomenon, whether individual differences in behavioural loss aversion can be predicted using scalp potentials (EEG) remains unclear. The current study measured scalp potential when subjects played a series of fair coin-toss games in three situations (high, medium and low loss conditions). We found that most people chose to bet in the low loss condition and avoided to bet in the high loss condition. However, their betting behaviour in the medium loss condition was variable, reflecting their different behavioural tendency of loss aversion. We found that late positive complex (LPC) significantly increased when subjects accepted gambles in the low loss condition (implying potential loss), relative to when they rejected gambles in the high loss condition (implying no gain and no loss), which is consistent with the previous finding that emotional stimuli can induce enhanced LPC. We further found that the difference in the scalp potentials from the above two conditions could predict behavioural tendency of loss aversion in the medium loss condition across subjects. This result demonstrated how behavioural tendency in a situation can be predicted using EEG from other situations.
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