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From nodes to edge, see how connectivity gives rise to conscious experience and learning.

2 Lessons

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48min

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Examine the nuances of unconscious and conscious states, the relationship between dreams and waking moments, and what the recent science tells us about different brain specializations.

You’ve had the dream that felt more real than your actual morning. You’ve caught yourself half-awake, wondering if you’re still dreaming, and perhaps you’ve yet to find a satisfying answer for why that line is so blurry.

Many explanations of consciousness skip past that blur. They focus on determining what consciousness is — a definition, a location, a dial to turn up or down. This conversation asks a different question entirely: where is the threshold? Where is the actual moment a brain crosses from not-conscious into conscious — in sleep, in early development, maybe even in the womb.

From nodes to edge, see how connectivity gives rise to conscious experience and learning.

In one continuous conversation, developmental neuropsychiatrist Dr. Jay Giedd sits down with psychobiologist Bernard Baars to work through that threshold from every angle they can find: what sleep and dreaming reveal about a brain running its predictions unchecked, why perception was built for survival rather than accuracy, and why raw connectivity — even at a scale now matched by the World Wide Web — still isn’t enough to wake anything up.

The real answer, they find, isn’t in the number of connections at all. It’s in the traffic moving through them.

This is a compelling discussion between two experts honest enough to even say, “I don’t know”, and then keep working the question anyway, because as Giedd puts it, good science raises more questions than it answers, and consciousness is the best example of that there is.

Press play and explore where the threshold sits.

Neural Traffic Flow in the Brain

Bernard Baars welcomes developmental neuropsychiatrist Dr. Jay Giedd, Professor of Psychiatry at UC San Diego School of Medicine. Their discussion touches on the nuances of unconscious and conscious states, the relationship between dreams and waking moments, and what the recent science tells us about different brain specializations.

LEARNING OUTCOMES: After completing this course you will gain the skills to

Question the simple "on/off" model of consciousness. Use evidence like the brain's near-constant energy consumption during sleep to evaluate claims that waking and sleeping represent a clean binary between conscious and unconscious states.
Apply the "prediction machine" framework to your own dreams. Explain why dreaming can feel real despite defying physics, using the idea that waking consciousness is a continuously corrected prediction, while dreaming runs that same predictive model with little sensory correction.
Evaluate perception as a survival tool rather than an accuracy tool. Recognize when a perceptual "error" — like seeing danger where there is none — reflects a system optimized for the cost of being wrong, not a flaw to be corrected.
Assess claims about machine or artificial consciousness using connection count alone. Explain why systems that already match or exceed the brain's roughly 10^13 connections (such as the web) haven't produced anything resembling consciousness, and identify what additional factors beyond raw quantity such claims need to address.
Distinguish anatomical connectivity from functional connectivity. Separate the question of how a brain is physically wired (the "street map") from how it communicates moment to moment (the "traffic") and choose the right investigative tool (EEG, MEG, or fMRI) for each kind of question.
Apply the "nodes and edges" framework across domains. Use this portable, graph-theory-based language to describe connectivity in brains, cities, social networks, or any other complex system: recognizing what counts as a node and what counts as an edge in each case.
Explain why "good enough and adaptable" beats "perfect and fixed" in living systems. Apply the immunology-to-consciousness argument: degenerate, flexible matching outperforms precise, rigid matching, to evaluate claims about brains, immune systems, or any other selectional system.
Identify when apparent purpose or recognition may not require consciousness. Apply the caution modeled by the twin-fetus example: checking whether an underlying capacity, like memory, is actually present before attributing conscious behavior to a system, biological or artificial.

Who is this course for?

This course is for you if you are interested in, or teach, or study any of the following subjects: Psychology, Neuroscience and Education, Cognitive Science, Health Education, or Philosophy. And anyone working in AI who wants to understand the biological basis of consciousness before assuming machine minds.

Trusted by academics

“Baars was just waaaay ahead of everyone else in approaching questions concerning consciousness as scientific questions. He's done vastly more than metaphor-slinging philosophers to make progress. His preference for testable hypotheses as opposed to dead-end thought-experiments have made a major difference in shifting the topic to a secure knowledge base from which real progress can be made.”

“Eyebrows are no longer raised even among neuroscientists when the topic of consciousness is discussed, largely due to authors like Bernard Baars. His works explain many insights of the complex workings of the brain.”

“Bernard Baars is both one of the pioneers of the cognitive study of consciousness and currently one of its most important contributors.”

“Bernard Baars is the leading thinker behind the Global Workspace theory of consciousness that has been recently championed by top neuroscientists, such as Stanislas Dehaene.”