Interactive Cognition (9/14/2016)

Elements of Interactive Cognition (Perception):

  1. Environment
  2. Sequential Parsing
  3. Prediction
  4. Interaction with other systems
  5. Recurrent (connections)
  6. Memory
  7. Parallel/Distributed Processing: not clear whether this is for computational advantages or for some other reasons

Formal Models:

  1. Environment:
    • Labeled examples: a sequence of (X1, l(x1)), … (Xn, l(xn))
    • Two models:
      (a) PAC (probably approximately correct):
      Assume \exists distribution D (unknown) on input examples x \in \mathbb{X} . After seeing m labeled examples (x, l(x)) where x ~ D, a (\epsilon, \delta) -PAC algorithm finds a hypothesis h with prob. 1-\delta such that: P_{x \sim D}(h(y) \ne l(y)) \leq \epsilon
      (on a new input y ~ D, we should be able to guess l(y))
      (b) Mistake-bound:
      Given an example x, you predict l(x) right away, and are then told the correct answer. The complexity of an algorithm in the mistake-bound model is m, if for any sequence of examples (of any length), the total number of mistakes made by the algorithm is at most m.
  2. Prediction:
    1. Max Likelihood Estimators: find a model that most likely generated the data
    2. Bayesian Estimation: use priors to make decisions
  3. Recurrent (connections):
    • Recurrent Neural Network (RNN): contains connections that are not feed-forward:

Project Example:

Invariant: Parsing a letter

  1. Environment and Sequential Parsing: I see a letter as a sequence of strokes
  2. Prediction: As someone starts writing a stroke, you can predict which letter it is
  3. Interaction with other systems:
    • Motor System
    • Sound
  4. Recurrent: Use feedback from “learned” letters to predict
  5. Parallel/Distributed Processing:

Author: Suk Hwan Hong

Georgia Tech

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