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Discrete Measurement of Sensory Information using Bayesian Networks
In principle, information theory can be used to measure the amount of information generated by sensory apparatus. This can be the basis for evaluating the viability of a cognitive model. In practice, however, such checks are rarely made due to the complexity of agent-level, informational analysis. Where it is the agent itself which is the `receiver', measurement of sensory information involves determining the way interpretive processes affect stimulus probabilities. No practical method for performing this type of analysis has been developed. The paper shows, however, that Bayesian networks can be adapted for this usage. Illustrative examples are given in three domains but the method is completely general and can be applied to any model which has a sensory component.
History
Publication status
- Published
Publisher
Cognitive Science SocietyPresentation Type
- paper
Event name
30th Annual Meeting of the Cognitive Science SocietyEvent location
Austin, TXEvent type
conferenceDepartment affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes