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Causal Inference About Good and Bad Outcomes.

ABSTRACT: People learn differently from good and bad outcomes. We argue that valence-dependent learning asymmetries are partly driven by beliefs about the causal structure of the environment. If hidden causes can intervene to generate bad (or good) outcomes, then a rational observer will assign blame (or credit) to these hidden causes, rather than to the stable outcome distribution. Thus, a rational observer should learn less from bad outcomes when they are likely to have been generated by a hidden cause, and this pattern should reverse when hidden causes are likely to generate good outcomes. To test this hypothesis, we conducted two experiments ( N = 80, N = 255) in which we explicitly manipulated the behavior of hidden agents. This gave rise to both kinds of learning asymmetries in the same paradigm, as predicted by a novel Bayesian model. These results provide a mechanistic framework for understanding how causal attributions contribute to biased learning.

SUBMITTER: Dorfman HM

PROVIDER: S-EPMC6472176 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Causal Inference About Good and Bad Outcomes.

Dorfman Hayley M HM Bhui Rahul R Hughes Brent L BL Gershman Samuel J SJ

Psychological science 20190213 4

People learn differently from good and bad outcomes. We argue that valence-dependent learning asymmetries are partly driven by beliefs about the causal structure of the environment. If hidden causes can intervene to generate bad (or good) outcomes, then a rational observer will assign blame (or credit) to these hidden causes, rather than to the stable outcome distribution. Thus, a rational observer should learn less from bad outcomes when they are likely to have been generated by a hidden cause, ...[more]

PMID: 30759048

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Project description:BackgroundInsulin shares a limited physiological concentration range with other endocrine hormones. Not only too low, but also too high systemic insulin levels are detrimental for body functions.Main bodyThe physiological function and clinical relevance of insulin are usually seen in association with its role in maintaining glucose homeostasis. However, insulin is an anabolic hormone which stimulates a large number of cellular responses. Not only too low, but also excess insulin concentrations are detrimental to the physiological balance. Although the glucoregulatory activity of insulin is mitigated during hyperinsulinemia by dampening the efficiency of insulin signaling ("insulin resistance"), this is not the case for most other hormonal actions of insulin, including the promotion of protein synthesis, de novo lipogenesis, and cell proliferation; the inhibition of lipolysis, of autophagy-dependent cellular turnover, and of nuclear factor E2-related factor-2 (Nrf2)-dependent antioxidative; and other defense mechanisms. Hence, there is no general insulin resistance but selective impairment of insulin signaling which causes less glucose uptake from the blood and reduced activation of endothelial NO synthase (eNOS). Because of the largely unrestricted insulin signaling, hyperinsulinemia increases the risk of obesity, type 2 diabetes, and cardiovascular disease and decreases health span and life expectancy. In epidemiological studies, high-dose insulin therapy is associated with an increased risk of cardiovascular disease. Randomized controlled trials of insulin treatment did not observe any effect on disease risk, but these trials only studied low insulin doses up to 40 IU/day. Proof for a causal link between elevated insulin levels and cardiovascular disease risk comes from Mendelian randomization studies comparing individuals with genetically controlled low or high insulin production.ConclusionsThe detrimental actions of prolonged high insulin concentrations, seen also in cell culture, argue in favor of a lifestyle that limits circadian insulin levels. The health risks associated with hyperinsulinemia may have implications for treatment regimens used in type 2 diabetes.

Dataset Information

Causal Inference About Good and Bad Outcomes.

Publications

Causal Inference About Good and Bad Outcomes.

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