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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

For the three representative training sequences (a)–(c), the temporal profiles of the parametric bias, the motor outputs are plotted in the second row and the sensor inputs are plotted in the third row. The vertical dotted lines denote occurrence of segmentation when the primitive behaviors switched. The capital letters associated with each segment denote the abbreviation of the corresponding primitive behavior.
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Figure 7: For the three representative training sequences (a)–(c), the temporal profiles of the parametric bias, the motor outputs are plotted in the second row and the sensor inputs are plotted in the third row. The vertical dotted lines denote occurrence of segmentation when the primitive behaviors switched. The capital letters associated with each segment denote the abbreviation of the corresponding primitive behavior.

Mentions: Figure 7 shows how the PB sequences are generated in the learning results, for three of representative training sensory-motor sequences out of seven. The plots in the top row in this figure show the activation of four parametric bias units as a function of the time step; the activation values from 0.0 to 1.0 are represented using the gray scale from white to black, respectively. The plots in the second and the third rows represent the temporal profile of motor and sensor values for each training sequence. The vertical dotted lines indicate the occurrence of segmentation when the behavior sequence switches from one primitive to another in generating the training sequence. The capital letters associated with each segment denote the abbreviation of the corresponding primitive behavior. In this figure, it is observed that the switching of bit patterns in the parametric bias takes place mostly in synchronization with the segmentation points known from the training sequences although it is observed that some segments are fragmented. Our examinations for all the trained sequences showed that the bit patterns in the parametric bias correspond uniquely to primitive behaviors in a one-to-one relationship in most cases. They are shown in Figure 7 with the following abbreviations. AO, approach to object in the center from the right-hand side; PO, push object from the center to the left-hand side; TO, touch object; IC, perform inverse C shape; HO, go back to home position; CE, go to the center from the right-hand side; and C, perform C shape.


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

Tani J - Front Neurorobot (2007)

For the three representative training sequences (a)–(c), the temporal profiles of the parametric bias, the motor outputs are plotted in the second row and the sensor inputs are plotted in the third row. The vertical dotted lines denote occurrence of segmentation when the primitive behaviors switched. The capital letters associated with each segment denote the abbreviation of the corresponding primitive behavior.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: For the three representative training sequences (a)–(c), the temporal profiles of the parametric bias, the motor outputs are plotted in the second row and the sensor inputs are plotted in the third row. The vertical dotted lines denote occurrence of segmentation when the primitive behaviors switched. The capital letters associated with each segment denote the abbreviation of the corresponding primitive behavior.
Mentions: Figure 7 shows how the PB sequences are generated in the learning results, for three of representative training sensory-motor sequences out of seven. The plots in the top row in this figure show the activation of four parametric bias units as a function of the time step; the activation values from 0.0 to 1.0 are represented using the gray scale from white to black, respectively. The plots in the second and the third rows represent the temporal profile of motor and sensor values for each training sequence. The vertical dotted lines indicate the occurrence of segmentation when the behavior sequence switches from one primitive to another in generating the training sequence. The capital letters associated with each segment denote the abbreviation of the corresponding primitive behavior. In this figure, it is observed that the switching of bit patterns in the parametric bias takes place mostly in synchronization with the segmentation points known from the training sequences although it is observed that some segments are fragmented. Our examinations for all the trained sequences showed that the bit patterns in the parametric bias correspond uniquely to primitive behaviors in a one-to-one relationship in most cases. They are shown in Figure 7 with the following abbreviations. AO, approach to object in the center from the right-hand side; PO, push object from the center to the left-hand side; TO, touch object; IC, perform inverse C shape; HO, go back to home position; CE, go to the center from the right-hand side; and C, perform C shape.

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