Cognitive Processing

F.T. Arecchi
Complexity and adaptation:
a strategy common to scientific modeling and perception

Figures 1-4

 
Fig. 1. Knowledge interpretation: R=reality, M=symbol generator (measuring apparatus), S=symbol interpreter (model builder).
Lline A (Cartesian cut) M+R provide representations as symbol sequences, which are processed by S. S can be replaced by a Turing machine.
Line B (Realistic approach): S+M globally face R. Before producing outputs, M is readjusted among a class of possible settings by a pre-linguistic procedure not expressible within the formal language which later M provides to S.
The thick dashed line from S to M represents the feedback which readjusts the setting of M.

 

 

 

 

 

 

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Fig. 2. Feature binding: the lady and the cat are respectively represented by the mosaic of empty and filled circles, each one representing the receptive field of a neuron group in the visual cortex. Within each circle the processing refers to a specific detail (e.g. contour orientation). The relations between details are coded by the temporal correlation among neurons, as shown by the same sequences of electrical pulses for two filled circles or two empty circles. Neurons referring to the same individual (e.g. the cat) have synchronous discharges, whereas their spikes are uncorrelated with those referring to another individual (the lady).

 

 

 

 

 

 

 

 

 

 

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Fig. 3. On the left, Ehrenstein figure. We perceive an illusory circle, formed by the inner ends of the radial lines, which enclose an area which seems brighter. Such a perception disappears for a different organization of the lines (on the right).

 

 

 

 

 

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Fig. 4. ART = Adaptive Resonance Theory.
Role of bottom-up stimuli from the early visual stages an top-down signals due to expectations formulated by the semantic memory. The focal attention assures the matching (resonance) between the two streams.

 

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