Unsupervised Thinking
a podcast about neuroscience, artificial intelligence and science more broadly

Wednesday, May 30, 2018

Episode 33: Predictive Coding

You may have heard of predictive coding; it's a theory that gets around. In fact, it's been used to understand everything from the retina to consciousness. So, before we get into the details, we start this episode by describing our impressions of predictive coding. Where have we encountered it? Has it influenced our work? Why do philosophers like it? And, finally, what does it actually mean? Eventually we settle on a two-tiered definition: "hard" predictive coding refers to a very specific hypothesis about how the brain calculates errors, and "soft" predictive coding refers to the general idea that the brain predicts things. We then get into how predictive coding relates to other theories, like Bayesian modeling. But like Bayesian models, which we've covered on a previous episode, predictive coding is prone to "just-so" stories. So we discuss what concrete predictions predictive coding can make, and whether the data supports them. Finally, Grace tries to describe the free energy principle, which extends predictive coding into a grand unified theory of the brain and beyond.

We read:
Whatever next? Predictive brains, situated agents, and the future of cognitive science

And mentioned:
Episode 15: "Just-so" stories in Bayesian modeling in psychology
Kok and Lange book chapter on predictive coding
NYU panel/debate on predictive coding

And our special guest was Alex Cayco-Gajic!

To listen to (or download) this episode, (right) click here

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com) 

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