Publications by authors named "Elise Payzan-LeNestour"

Continuing to gamble despite harmful consequences has plagued human life in many ways, from loss-chasing in problem gamblers to reckless investing during stock market bubbles. Here, we propose that these anomalies in human behavior can sometimes reflect Pavlovian perturbations on instrumental behavior. To show this, we combined key elements of Pavlovian psychology literature and standard economic theory into a single model.

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Research in the field of multisensory perception shows that what we hear can influence what we see in a wide range of perceptual tasks. It is however unknown whether this extends to the visual perception of risk, despite the importance of the question in many applied domains where properly assessing risk is crucial, starting with financial trading. To fill this knowledge gap, we ran interviews with professional traders and conducted three laboratory studies using judgments of financial asset risk as a testbed.

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In many contexts, decision-making requires an accurate representation of outcome variance-otherwise known as "risk" in economics. Conventional economic theory assumes this representation to be perfect, thereby focusing on risk preferences rather than risk perception per se [1-3] (but see [4]). However, humans often misrepresent their physical environment.

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Uncertainty is an inherent property of the environment and a central feature of models of decision-making and learning. Theoretical propositions suggest that one form, unexpected uncertainty, may be used to rapidly adapt to changes in the environment, while being influenced by two other forms: risk and estimation uncertainty. While previous studies have reported neural representations of estimation uncertainty and risk, relatively little is known about unexpected uncertainty.

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Little is known about how humans solve the exploitation/exploration trade-off. In particular, the evidence for uncertainty-driven exploration is mixed. The current study proposes a novel hypothesis of exploration that helps reconcile prior findings that may seem contradictory at first.

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Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty.

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