Alison Gopnik on Child Development, Elderhood, Caregiving, and A.I.
COMPLEXITY
Is There a Relationship Between Learning and the Environment?
Insects are not terribly smart even as insects go. But there is nevertheless a difference between the ones who were just relying on their innate reflexes, find a leaf that's green and just plant your eggs there. And it turns out that that's correlated with how long a period of maturity they have. Even for butterflies, they're producing fewer young and giving them a longer chance to mature when they rely on learning.
Humans have an unusually long childhood — and an unusually long elderhood past the age of reproductive activity. Why do we spend so much time playing and exploring, caregiving and reflecting, learning and transmitting? What were the evolutionary circumstances that led to our unique life history among the primates? What use is the undisciplined child brain with its tendencies to drift, scatter, and explore in a world that adults understand in such very different terms? And what can we transpose from the study of human cognition as a developmental, stage- wise process to the refinement and application of machine learning technologies?
Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.
This week we talk to SFI External Professor Alison Gopnik, Professor of Psychology and Affiliate Professor of Philosophy at the University of California Berkeley, author of numerous books on psych, cognitive science, childhood development. She writes a column at The Wall Street Journal, alternating with Robert Sapolsky. Slate said that Gopnik is “where to go if you want to get into the head of a baby.” In our conversation we discuss the tension between exploration and exploitation, the curious evolutionary origins of human cognition, the value of old age, and she provides a sober counterpoint about life in the age of large language machine learning models.
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Mentioned & Related Links:
Alison Gopnik’s Google Scholar page
Explanation as Orgasm
by Alison Gopnik
Twitter thread for Gopnik’s latest SFI Seminar on machine learning and child development
Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood
by Gopnik et al.
Pretense, Counterfactuals, and Bayesian Causal Models: Why What Is Not Real Really Matters
by Deena Weisberg & Alison Gopnik
Childhood as a solution to explore–exploit tensions
by Alison Gopnik
The Origins of Common Sense in Humans and Machines
by Kevin A Smith, Eliza Kosoy, Alison Gopnik, Deepak Pathak, Alan Fern, Joshua B Tenenbaum, & Tomer Ullman
What Does “Mind-Wandering” Mean to the Folk? An Empirical Investigation
by Zachary C. Irving, Aaron Glasser, Alison Gopnik, Verity Pinter, Chandra Sripada
Models of Human Scientific Discovery
by Robert Goldstone, Alison Gopnik, Paul Thagard, Tomer Ullman
Love Lets Us Learn: Psychological Science Makes the Case for Policies That Help Children
by Alison Gopnik at APS
Our Favorite New Things Are the Old Ones
by Alison Gopnik at The Wall Street Journal
An exchange of letters on the role of noise in collective intelligence
by Daniel Kahneman, David Krakauer, Olivier Sibony, Cass Sunstein, & David Wolpert#DEVOBIAS2018 on SFI Twitter
Coarse-graining as a downward causation mechanism
by Jessica Flack
Complexity 90: Caleb Scharf on The Ascent of Information: Life in The Human Dataome
Complexity 15: R. Maria del-Rio Chanona on Modeling Labor Markets & Tech Unemployment
Learning through the grapevine and the impact of the breadth and depth of social networks
by Matthew Jackson, Suraj Malladi, & David McAdams
The coming battle for the COVID-19 narrative
by Wendy Carlin & Sam Bowles
Complexity 97: Glen Weyl & Cris Moore on Plurality, Governance, and Decentralized Society
Derek Thompson at The Atlantic on the forces slowing innovation at scale (citing Chu & Evans)
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