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On the interactions between top-down anticipation and bottom-up regression.

Tani J - Front Neurorobot (2007)

Bottom Line: This paper discusses the importance of anticipation and regression in modeling cognitive behavior.The meanings of these cognitive functions are explained by describing our proposed neural network model which has been implemented on a set of cognitive robotics experiments.The reviews of these experiments suggest that the essences of embodied cognition may reside in the phenomena of the break-down between the top-down anticipation and the bottom-up regression and in its recovery process.

View Article: PubMed Central - PubMed

Affiliation: Brain Science Institute, RIKEN Japan.

ABSTRACT
This paper discusses the importance of anticipation and regression in modeling cognitive behavior. The meanings of these cognitive functions are explained by describing our proposed neural network model which has been implemented on a set of cognitive robotics experiments. The reviews of these experiments suggest that the essences of embodied cognition may reside in the phenomena of the break-down between the top-down anticipation and the bottom-up regression and in its recovery process.

No MeSH data available.


Related in: MedlinePlus

(a) Behavior system with anticipation and regression mechanism. (b) Extension to multiple levels. m(t) and s(t) represent the current motor and sensory patterns. p represents the internal parameter which corresponds to the PB vector in the later sections.
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Figure 2: (a) Behavior system with anticipation and regression mechanism. (b) Extension to multiple levels. m(t) and s(t) represent the current motor and sensory patterns. p represents the internal parameter which corresponds to the PB vector in the later sections.

Mentions: Figure 2a illustrates our ideas. The system has a motor generation part and a sensory anticipation part both of which are modulated by the internal parameters. The motor generation part is just a sensory-motor mapping function with the internal parameter. We can think of generating specific sensory-motor sequence patterns by setting their corresponding values into the internal parameters. This is like as if a specific behavior primitive or motor schema (Arbib, 1981) in repertory were retrieved by using a key of this internal parameter. The anticipation part is realized by a forward model (Demiris and Hayes, 2002; Kawato et al., 1987; Tani, 1996; Werbos, 1990; Wolpert and Kawato, 1998) where the sensory inputs of the next time step are anticipated based on the current motor outputs and the internal parameters. Then, one step after, the gap between the anticipation and the reality comes out as the error. This error is utilized to modulate the internal parameters by means of the inverse dynamics computation of minimizing the error through the forward model. This is the regression which proceeds in real time while the system interacts with the environment. It is assumed that the internal parameters can modulate only slowly compared to the time constant of the system. The modulation of the internal parameters means that the current behavior primitive is switched to others because it does not fit with the current sensation of the environment.


On the interactions between top-down anticipation and bottom-up regression.

Tani J - Front Neurorobot (2007)

(a) Behavior system with anticipation and regression mechanism. (b) Extension to multiple levels. m(t) and s(t) represent the current motor and sensory patterns. p represents the internal parameter which corresponds to the PB vector in the later sections.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2533585&req=5

Figure 2: (a) Behavior system with anticipation and regression mechanism. (b) Extension to multiple levels. m(t) and s(t) represent the current motor and sensory patterns. p represents the internal parameter which corresponds to the PB vector in the later sections.
Mentions: Figure 2a illustrates our ideas. The system has a motor generation part and a sensory anticipation part both of which are modulated by the internal parameters. The motor generation part is just a sensory-motor mapping function with the internal parameter. We can think of generating specific sensory-motor sequence patterns by setting their corresponding values into the internal parameters. This is like as if a specific behavior primitive or motor schema (Arbib, 1981) in repertory were retrieved by using a key of this internal parameter. The anticipation part is realized by a forward model (Demiris and Hayes, 2002; Kawato et al., 1987; Tani, 1996; Werbos, 1990; Wolpert and Kawato, 1998) where the sensory inputs of the next time step are anticipated based on the current motor outputs and the internal parameters. Then, one step after, the gap between the anticipation and the reality comes out as the error. This error is utilized to modulate the internal parameters by means of the inverse dynamics computation of minimizing the error through the forward model. This is the regression which proceeds in real time while the system interacts with the environment. It is assumed that the internal parameters can modulate only slowly compared to the time constant of the system. The modulation of the internal parameters means that the current behavior primitive is switched to others because it does not fit with the current sensation of the environment.

Bottom Line: This paper discusses the importance of anticipation and regression in modeling cognitive behavior.The meanings of these cognitive functions are explained by describing our proposed neural network model which has been implemented on a set of cognitive robotics experiments.The reviews of these experiments suggest that the essences of embodied cognition may reside in the phenomena of the break-down between the top-down anticipation and the bottom-up regression and in its recovery process.

View Article: PubMed Central - PubMed

Affiliation: Brain Science Institute, RIKEN Japan.

ABSTRACT
This paper discusses the importance of anticipation and regression in modeling cognitive behavior. The meanings of these cognitive functions are explained by describing our proposed neural network model which has been implemented on a set of cognitive robotics experiments. The reviews of these experiments suggest that the essences of embodied cognition may reside in the phenomena of the break-down between the top-down anticipation and the bottom-up regression and in its recovery process.

No MeSH data available.


Related in: MedlinePlus