Robot Self-Recognition Using Conditional Probability-Based Contingency

As robots become more sophisticated and pervasive, they will be forced to operate in more dynamic and social environments. In order to develop a theory of mind to account for the intents, beliefs, and motivations of other individuals, a robot needs to be able to distinguish between another entity and itself. One proposed method of learning the difference between self and other is to use contingency, the time dependence of perception and action. Watson suggested contingency as a method used by infants when learning to detect self. He outlined four general methods for detecting contingency: contiguity, temporal correlation, conditional probability, and causal implication. For our experiment, we chose to implement Watson's conditional probability method of contingency detection. Conditional probability keeps track of instances in which the behavior occurs and the stimulus does not, versus instances when the stimulus occurs but the behavior does not.

Authors

• Kevin M. Godby Jesse A. Lane

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