Uploaded November 2022 | Updated September 2026, 2 hours ago
Patreon for full episodes and Discord community:
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Free Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
Music by: The New Year: thenewyear.net
Show notes: braininspired.co/podcast/154
Anne Colins runs her Computational Cognitive Neuroscience Lab at the University of California, Berkley One of the things she's been working on for years is how our working memory plays a role in learning as well, and specifically how working memory and reinforcement learning interact to affect how we learn, depending on the nature of what we're trying to learn. We discuss that interaction specifically. We also discuss more broadly how segregated and how overlapping and interacting our cognitive functions are, what that implies about our natural tendency to think in dichotomies - like MF vs MB-RL, system-1 vs system-2, etc., and we dive into plenty other subjects, like how to possibly incorporate these ideas into AI.
0:00 - Intro
5:25 - Dimensionality of learning
11:19 - Modularity of function and computations
16:51 - Is working memory a thing?
19:33 - Model-free model-based dichotomy
30:40 - Working memory and RL
44:43 - How working memory and RL interact
50:50 - Working memory and attention
59:37 - Computations vs. implementations
1:03:25 - Interpreting results
1:08:00 - Working memory and AI
Patreon for full episodes and Discord community:
patreon.com/braininspired
Free Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
Music by: The New Year: thenewyear.net
Show notes: braininspired.co/podcast/154
Anne Colins runs her Computational Cognitive Neuroscience Lab at the University of California, Berkley One of the things she's been working on for years is how our working memory plays a role in learning as well, and specifically how working memory and reinforcement learning interact to affect how we learn, depending on the nature of what we're trying to learn. We discuss that interaction specifically. We also discuss more broadly how segregated and how overlapping and interacting our cognitive functions are, what that implies about our natural tendency to think in dichotomies - like MF vs MB-RL, system-1 vs system-2, etc., and we dive into plenty other subjects, like how to possibly incorporate these ideas into AI.
0:00 - Intro
5:25 - Dimensionality of learning
11:19 - Modularity of function and computations
16:51 - Is working memory a thing?
19:33 - Model-free model-based dichotomy
30:40 - Working memory and RL
44:43 - How working memory and RL interact
50:50 - Working memory and attention
59:37 - Computations vs. implementations
1:03:25 - Interpreting results
1:08:00 - Working memory and AI
