Rational Analysis

Anderson proposes that the best method for analyzing human cognitive behaviors lies in the analysis of the task rather than in attempting to analyze the methods used by the human to solve the problem.
Principle of Rationality: The cognitive system optimizes the adaptation of the behavior of the organism.
In support he quotes Marr:

An algorithm is likely understood more readily by understanding the nature of the problem being solved than by examining the mechanism (and the hardware) in which it is solved. (p27)
He implies that researchers have confused the analysis of tasks with the analysis of mechanisms because of the existence of signature data, a subject-universal invariant measure of performance for some task or group of tasks. Anderson argues that the appearance of these data have been taken as evidence indicating constraints on the architecture of human cognition, while he believes that the data indicate constraints of the task.

Anderson proposes three advantages that rational analysis provides:

  1. An understanding of the nature of the problem can provide strong guidance in the proposal of possible mechanisms.
  2. The task domain provides rationale for constraining the architecture.
  3. Mechanism-focused modeling faces critical indeterminacies that can affect computation or memory mechanisms such as serial versus parallel processing. Analysis of the task domain need not consider these directly.
Anderson states that to properly analyze the task domain from the perspective of the agent, one must also consider:
  1. Cost of computation in the behavior.
  2. That the agent may have adapted to an environment significantly different from the environment in which it is being tested.
  3. Performance measures must be aligned with the goals of the agent to ensure that the appropriate optimization problem is proposed.
Using these caveats Anderson proposes the following recipe for rational analysis:
  1. Precisely specify the goals of the agent.
  2. Develop a formal model of the environment to which the agent is adapted.
  3. Make the minimal assumptions about computational costs.
  4. Derive the optimal behavior of the agent considering (1)-(3).
  5. Examine the literature to see if the behaviors of the agent reproduce empirical human data.
  6. If predictions are off, iterate.
Anderson uses this rational analysis on three signature problems:
  1. Power law of learning
  2. Fan effect
  3. Categorization
In summary, Anderson believes that the mechanism-focused approaches to cognition are doomed by the identifiability problem: the mechanism of cognition is not uniquely defined by the task plus environment. More assumptions must be made to determine the mechanism of cognition than are required to analyze the task domain. Furthermore, the analysis of the task domain, properly constrained and oriented, reproduces the signature data found in the human psychological literature and is, therefore, sufficient for the de facto goals of AI.

Anderson says that cognitive architectures provide a notation for expressing the behavior, but the statement of the information processing problem in the task domain is the key to reproducing the signature data.

Compare Simon's critique.

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