Computational implications of gestalt theory: The role of feedback in visual processing

Neurophysiological investigations of the visual system by way of single-cell recordings have revealed a hierarchical architecture in which lower level areas, such as the primary visual cortex, contain cells that respond to simple features, while higher level areas contain cells that respond to higher order features apparently composed of combinations of lower level features. This architecture seems to suggest a feed-forward processing strategy in which visual information progresses from lower to higher visual areas. However there is other evidence, both neurophysiological and phenomenal, that suggests a more parallel processing strategy in biological vision, in which top-down feedback plays a significant role. In fact Gestalt theory suggests that visual perception involves a process of emergence, i.e. a dynamic relaxation of multiple constraints throughout the system simultaneously, so that the final percept represents a stable state, or energy minimum of the dynamic system as a whole. A Multi-Level Reciprocal Feedback (MLRF) model is proposed to resolve the apparently contradictory concepts, by proposing a hierarchical visual architecture whose different levels are connected by bi-directional feed-forward and feedback pathways, where the computational transformation performed by the feedback pathway between levels in the hiararchy is a kind of inverse of the transformation performed by the corresponding feed-forward processing stream. This alternative paradigm of perceptual computation accounts in general terms for a number of visual illusory effects, and offers a computational specification for the generative, or constructive aspect of perceptual processing revealed by Gestalt theory
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Jean Bullier (2001). Feedback Connections and Conscious Vision. Trends in Cognitive Sciences 5 (9):369-370.

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