From Our Neurons to Yours "The Emergent Mind: How Intelligence Arises in People and Machines" | Jay McClelland
Nov 26, 2025
Jay McClelland, a leading cognitive scientist and Stanford professor, discusses the fascinating intersection of human and artificial intelligence. He explores how neural networks echo our brain’s workings and critiques traditional cognitive models. McClelland reveals insights on how children efficiently learn complex categories and how language shapes understanding. He suggests that coherent thought emerges from interactions, contrasting this with the limitations of current AI. His insights advocate for neuroscience-inspired advancements in building smarter machines.
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Neurons As Population Computation
- A neural network models the brain as interconnected units that sum graded inputs and produce graded, noisy outputs.
- Jay McClelland frames neurons as population-level computing elements whose collective activation creates cognition.
Waterfall Metaphor For Distributed Processing
- McClelland uses a waterfall image to illustrate distributed activation shaping cognition rather than single streams.
- The pattern of flow, not any one stream, determines the pools and the mind's behavior.
Mutual Constraints Replace Stage Theories
- Rummelhart and colleagues rejected strictly sequential stage models for cognition in favor of mutual interdependence across representations.
- Bidirectional interactions let evidence for words and letters mutually constrain each other during perception.
