Abstract
We introduce a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of arousal systems that are relevant for consciousness. The three central components are an identification network, a localization network, and a working memory network. Attention serves as the interface between the inner and the external world. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception. It also compares external sensations to internal top-down expectations. The model is tested in a number of computer simulations that illustrate how it can operate as a component in various cognitive tasks including perception, the A-not-B test, delayed matching to sample, episodic recall, and vicarious trial and error.
Highlights
The main function of this memory system is to move some cognitive operations into an inner world, and more importantly, to allow the inner world of the cognitive system to coevolve with the external world in such a way that it can generate expectations as those involved in object permanence and the A-no-B test
We have introduced a memory model for robots that can account for many aspects of the presence of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries
An important aspect of the model is that the mechanisms that fill in sensations to generate perceptions can be detached from sensory input and run in isolation (Gärdenfors, 2003)
Summary
Consciousness is not unitary but involves several kinds of components. The most fundamental component may be the emotional tone of the current state of the mind (Damasio and Marg, 1995). The main function of this memory system is to move some cognitive operations into an inner world, and more importantly, to allow the inner world of the cognitive system to coevolve with the external world in such a way that it can generate expectations as those involved in object permanence and the A-no-B test. These expectations can be used in decision-making, to detect changes in the external world, and to direct attention. Our goal here is to show how the proposed memory model supports many cognitive functions that are central to a conscious intelligent robot and to suggest that the model could form an important component of a larger cognitive architecture that will be tested in a robot in the future
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