Peter Tse: The Neuroscience of Consciousness and Free Will

Peter Tse: The Neuroscience of Consciousness and Free Will

Author: Daniel Bashir December 14, 2023 Duration: 2:24:04
The Gradient: Perspectives on AI
TechnologyScience

In episode 102 of The Gradient Podcast, Daniel Bashir speaks to Peter Tse.

Professor Tse is a Professor of Cognitive Neuroscience and chair of the department of Psychological and Brain Sciences at Dartmouth College. His research focuses on using brain and behavioral data to constrain models of the neural bases of attention and consciousness, unconscious processing that precedes and constructs consciousness, mental causation, and human capacities for imagination and creativity. He is especially interested in the processing that goes into the construction of conscious experience between retinal activation at time 0 and seeing an event about a third of a second later.

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Outline:

* (00:00) Intro

* (01:45) Prof. Tse’s background

* (03:25) Early experiences in physics/math and philosophy of physics

* (06:10) Choosing to study neuroscience

* (07:15) Prof Tse’s commitments about determinism

* (10:00) Quantum theory and determinism

* (13:45) Biases/preferences in choosing theories

* (20:41) Falsifiability and scientific questions, transition from physics to neuroscience

* (30:50) How neuroscience is unusual among the sciences

* (33:20) Neuroscience and subjectivity

* (34:30) Reductionism

* (37:30) Gestalt psychology

* (41:30) Introspection in neuroscience

* (45:30) The preconscious buffer and construction of conscious experience, color constancy

* (53:00) Perceptual and cognitive inference

* (55:00) AI systems and intrinsic meaning

* (57:15) Information vs. meaning

* (1:01:45) Consciousness and representation of bodily states

* (1:05:10) Our second-order free will

* (1:07:20) Jaegwon Kim’s exclusion argument

* (1:11:45) Why Kim thought his own argument was wrong

* (1:15:00) Resistance and counterarguments to Kim

* (1:19:45) Criterial causation

* (1:23:00) How neurons evaluate inputs criterially

* (1:24:00) Concept neurons in the hippocampus

* (1:31:57) Criterial causation and physicalism, mental causation

* (1:40:10) Daniel makes another attempt to push back 🤡

* (1:45:47) More on AI

* (1:47:05) Prof Tse’s perspective on modern AI systems, differences with human cognition

* (2:17:25) Consciousness, attention, spirituality

* (2:20:10) Prof Tse’s hopes for AI

* (2:23:30) Outro

Links:

* Professor Tse’s homepage

* Papers

* Vision/Perception

* Perceptual learning based on the learning of diagnostic features

* Complete mergeability and amodal completion

* Attention

* How Attention Can Alter Appearances

* How Top-down Attention Alters Bottom-up preconscious operations

* Consciousness

* Network structure and dynamics of the mental workspace

* On free will

* NDPR review of “Neural Basis of Free Will”

* Kripke’s Category Error

* Ontological Indeterminism undermines Kim’s Exclusion Argument



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Hosted by Daniel Bashir, The Gradient: Perspectives on AI moves beyond surface-level headlines to explore the intricate machinery and human ideas shaping artificial intelligence. Each episode is built on a foundation of deep research, leading to conversations that are both technically substantive and broadly accessible. You'll hear from researchers, engineers, and philosophers who are actively building and critiquing our technological future, discussing not just how AI systems work, but the larger implications of their integration into society. This isn't about speculative hype; it's a grounded examination of real progress, persistent challenges, and ethical considerations from those on the front lines. The discussions peel back layers on topics like model architecture, policy, and the fundamental science behind the algorithms becoming part of our daily lives. For anyone curious about the substance behind the buzz-whether you have a technical background or are simply keen to understand a defining technology of our age-this podcast offers a crucial and thoughtful resource. Tune in for a consistently detailed and nuanced take that treats artificial intelligence with the complexity it deserves.
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