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Since this is a technically inclined audience, here's a tiny bit of mathematical background. When Ms. Das says that our brains constantly use prior information

Since this is a technically inclined audience, here's a tiny bit of mathematical background. When Ms. Das says that our brains constantly use prior information, she (probably) means prior in a certain specific, technical sense. Modern cognitive scientists often think about perception and cognition in probabilistic terms, so you might characterize the brain's task in interpreting that utterance as finding the most probable sentence (S) given the acoustic input (X), or P(S|X). Bayes' rule says you can write this expression as P(X|S)P(S)/Z, where Z is a normalizing constant (don't worry about it for now). Because these expressions are so common, we've come up with names for referring to their parts. The first part, P(X|S), is called the likelihood, which tells us how probable the input we experienced would be given a particular interpretation. For instance, if the sentence actually read "competition center" rather than "Constitution center", the sound in the recording would be less likely (although maybe still possible, aka non-zero probability, due to noise, speaker variance, etc). The second part, P(S), represents the prior probability of the sentence. Given our knowledge of English, some sentences are simply more likely than others. For instance, the sentence "colorless green ideas sleep furiously" is grammatically well-formed but tremendously unlikely. So, to conclude, when the presenter says we use prior information, she (probably, no pun intended) means that upon hearing what the correct interpretation should be, we increase the value of P(S), thereby allowing us to compute the proper perception. Here's a nice and fairly readable overview paper (with a whole section on prior knowledge) if you think this stuff is as cool as I do -- www.indiana.edu/~kruschke/articles/JacobsK2010.pdf *edits for clarity and formatting

deleted 12y ago
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troymc 12y ago
The Bayes Model is certainly a nice way to combine old and new information in an optimal sort of way. But is it really a good model of what goes on inside the brain? Where? How? It seems too clean, too pat, too optimal. Biology is messy. The paper says, "Bayesian models are not intended to provide mechanistic or process accounts of cognition." We deserve better.
- icelancer 12y ago
  What do you want, a signal decoder from the brain into an oscilloscope? We're just matching what we know best; Kalman filtering is a pretty good proxy for a lot of what we observe the brain doing as is Bayesian logic.
- aangjie 12y ago
  There are some scientists who argue for it. See https://probmods.org/ https://probmods.org/. I personally am not sure, as I haven't gone through these. But I am inclined to think Bayes Models might be the most useful models we currently have of what goes on inside the brain.
- beejiu 12y ago
  There's an interesting book called On Intelligence, by Jeff Hawkins, which tries to explain how intelligence works based on the "memory-prediction framework" (see Wikipedia). It's a very accessible book, and well worth a read if you have the time.
- chestervonwinch 12y ago
  Optimal in what sense? Bayes' rule is just an identity - a decomposition of one distribution into the product of two. Decomposing the posterior in this way says nothing about the complexity of the distribution itself.
- shmageggy 12y ago
  This is a great point and one that cognitive scientists have a successful and productive way to think about. What you are after, and rightfully so, is an algorithmic account of the mind. You'd like to know what steps our minds are actually taking to compute whatever it is computing. However, with something as mysterious and complex as the mind, even knowing what it is we're computing is far from trivial. That's where the account I sketched above comes in, usually referred to as a computational level explanation. It defines what the actual problem is that the mind is solving and gives mathematical constraints (and predictions) about behavior. The distinction between the two levels is subtle -- even published papers conflate or ignore it. However it's extremely important. Reverse-engineering the algorithm we use to perform that listnening/interpretation task is already extremely difficult, but it would be even harder without having any of the above formalization. The computational account sets up the goalposts, so to speak. Now, there's actually a third level as well. Even once you've written down your algorithmic-level account, that says nothing about how the brain implements that algorithm. That further explanation is referred to as the physical level, the level of implementation by neurons. If you want to read more, this paradigm is known as Marr's levels of analysis. https://www.google.com/search?q=marr's+levels https://www.google.com/search?q=marr's+levels
andyidsinga 12y ago
i did a little experiment : i paused the audio before the correct words were uttered ..and only having read the text of the correct words ahead of time i was able to hear them the second time i played the computer audio. fun

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troymc 12y ago

The Bayes Model is certainly a nice way to combine old and new information in an optimal sort of way. But is it really a good model of what goes on inside the brain? Where? How? It seems too clean, too pat, too optimal. Biology is messy. The paper says, "Bayesian models are not intended to provide mechanistic or process accounts of cognition." We deserve better.

icelancer 12y ago
What do you want, a signal decoder from the brain into an oscilloscope? We're just matching what we know best; Kalman filtering is a pretty good proxy for a lot of what we observe the brain doing as is Bayesian logic.
aangjie 12y ago
There are some scientists who argue for it. See https://probmods.org/ https://probmods.org/. I personally am not sure, as I haven't gone through these. But I am inclined to think Bayes Models might be the most useful models we currently have of what goes on inside the brain.
beejiu 12y ago
There's an interesting book called On Intelligence, by Jeff Hawkins, which tries to explain how intelligence works based on the "memory-prediction framework" (see Wikipedia). It's a very accessible book, and well worth a read if you have the time.
chestervonwinch 12y ago
Optimal in what sense? Bayes' rule is just an identity - a decomposition of one distribution into the product of two. Decomposing the posterior in this way says nothing about the complexity of the distribution itself.
shmageggy 12y ago
This is a great point and one that cognitive scientists have a successful and productive way to think about. What you are after, and rightfully so, is an algorithmic account of the mind. You'd like to know what steps our minds are actually taking to compute whatever it is computing. However, with something as mysterious and complex as the mind, even knowing what it is we're computing is far from trivial. That's where the account I sketched above comes in, usually referred to as a computational level explanation. It defines what the actual problem is that the mind is solving and gives mathematical constraints (and predictions) about behavior. The distinction between the two levels is subtle -- even published papers conflate or ignore it. However it's extremely important. Reverse-engineering the algorithm we use to perform that listnening/interpretation task is already extremely difficult, but it would be even harder without having any of the above formalization. The computational account sets up the goalposts, so to speak. Now, there's actually a third level as well. Even once you've written down your algorithmic-level account, that says nothing about how the brain implements that algorithm. That further explanation is referred to as the physical level, the level of implementation by neurons. If you want to read more, this paradigm is known as Marr's levels of analysis. https://www.google.com/search?q=marr's+levels https://www.google.com/search?q=marr's+levels

andyidsinga 12y ago

i did a little experiment : i paused the audio before the correct words were uttered ..and only having read the text of the correct words ahead of time i was able to hear them the second time i played the computer audio. fun