ArticleInternational journal of environmental research and public health2022
Implicit Negativity Bias Leads to Greater Loss Aversion and Learning during Decision-Making.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Social working memory in adolescence.Child development · 2026Article
- Article
- Exploring deep brain stimulation willingness in Black individuals with Parkinson's disease.Equity neuroscience · 2025Article
- A human working memory advantage for social network information.Proceedings. Biological sciences · 2024Article
- An ecological assessment of decision-making under risk and ambiguity through the virtual serious game Kalliste Decision Task.Scientific reports · 2024Article
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Authors and funding
3 authors.
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Abstract
It is widely accepted there is the existence of negativity bias, a greater sensitivity to negative emotional stimuli compared with positive ones, but its effect on decision-making would depend on the context. In risky decisions, negativity bias could lead to non-rational choices by increasing loss aversion; yet in ambiguous decisions, it could favor reinforcement-learning and better decisions by increasing sensitivity to punishments. Nevertheless, these hypotheses have not been tested to date. Our aim was to fill this gap. Sixty-nine participants rated ambiguous emotional faces (from the NimStim set) as positive or negative to assess negativity bias. The implicit level of the bias was also obtained by tracking the mouse's trajectories when rating faces. Then, they performed both a risky and an ambiguous decision-making task. Participants displayed negativity bias, but only at the implicit level. In addition, this bias was associated with loss aversion in risky decisions, and with greater performance through the ambiguous decisional task. These results highlight the need to contextualize biases, rather than draw general conclusions about whether they are inherently good or bad.
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