You aren't at the mercy of your emotions -- your brain creates them | Lisa Feldman Barrett
TED1 HN recommenders · 2018–2019
3.2M views · talk · evergreen · Uploaded 2018-01-23
Psychology professor Lisa Feldman Barrett presents her research on what emotions are, arguing that the brain constructs them rather than reading them off faces. She says this means we have more control over our emotions than we think.
HN commenters mention it as an accessible introduction to the theory of constructed emotions.
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3 comments from Hacker News
I agree because unfortunately scientists do not know very much yet. I like the talks by Lisa Feldman Barrett although it might seem to contradict what I wrote. [LINK] [LINK] [LINK] [LINK] [VIDEO]
I am surprised that the article does not mention the word epigenetics. - Epigenetics and the influence of our genes | Courtney Griffins [LINK] But there are also other, probably more important, ways to "inherit": - Placebo Effect VS No-Cebo Effect [LINK] - Dr. Bruce Lipton Explains HOW WE ARE PROGRAMMED AT BIRTH (an eye opening video) [LINK] - Biology of Belief - by Bruce Lipton (full documentary) [LINK] - Depression and anxiety: Have we gotten it wrong? | UpFront [LINK] - Causes of Psychosis [LINK] - 2011 - Liz Mullinar - Treating the core problem of childhood trauma. [LINK] About brain circuits regarding emotions: - You aren't at the mercy of your emotions -- your brain creates them | Lisa Feldman Barrett [VIDEO] - Cultivating Wisdom: The Power Of Mood | Lisa Feldman Barrett [LINK]
Great to see these views are becoming more mainstream. The talk does not mention it, but it's likely convergent with neuroscience ideas of: - Bayesian Brain - Theory of constructed emotions ( [VIDEO] ) - Free energy principle and Active Inference ( [LINK] ) A good overview is also here: [LINK] The issue to be reconciled is though that some of those ideas are talking about "keeping uncertainty in the sweet spot, not too high or low" while others about "minimising uncertainty/prediction error". I think the difference will turn out to be only in relation to how far of a future/prediction we are talking about. So optimising for long-term vs the short-term.