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BI 146 Lauren Ross: Causal and Non-Causal Explanation

BI 146 Lauren Ross: Causal and Non-Causal Explanation

FromBrain Inspired


BI 146 Lauren Ross: Causal and Non-Causal Explanation

FromBrain Inspired

ratings:
Length:
83 minutes
Released:
Sep 7, 2022
Format:
Podcast episode

Description

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Lauren Ross is an Associate Professor at the University of California, Irvine. She studies and writes about causal and non-causal explanations in philosophy of science, including distinctions among causal structures. Throughout her work, Lauren employs Jame's Woodward's interventionist approach to causation, which Jim and I discussed in episode 145. In this episode, we discuss Jim's lasting impact on the philosophy of causation, the current dominance of mechanistic explanation and its relation to causation, and various causal structures of explanation, including pathways, cascades, topology, and constraints.



Lauren's website.Twitter: @ProfLaurenRossRelated papersA call for more clarity around causality in neuroscience.The explanatory nature of constraints: Law-based, mathematical, and causal.Causal Concepts in Biology: How Pathways Differ from Mechanisms and Why It Matters.Distinguishing topological and causal explanation.Multiple Realizability from a Causal Perspective.Cascade versus mechanism: The diversity of causal structure in science.



0:00 - Intro
2:46 - Lauren's background
10:14 - Jim Woodward legacy
15:37 - Golden era of causality
18:56 - Mechanistic explanation
28:51 - Pathways
31:41 - Cascades
36:25 - Topology
41:17 - Constraint
50:44 - Hierarchy of explanations
53:18 - Structure and function
57:49 - Brain and mind
1:01:28 - Reductionism
1:07:58 - Constraint again
1:14:38 - Multiple realizability
Released:
Sep 7, 2022
Format:
Podcast episode

Titles in the series (99)

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.