A Recurrent Temporal Model for Semantic Levels Categorization Based on Human Visual System

Humans can categorize an object in different semantic levels. For example, a dog can be categorized as an animal (superordinate), a terrestrial animal (basic), or a dog (subordinate). Recent studies have shown that the duration of stimulus presentation can affect the mechanism of categorization in the brain. Rapid stimulus presentation will not allow top-down influences to be applied on the visual cortex, whereas in the nonrapid, top-down influences can be established and the final result will be different. In this paper, a spiking recurrent temporal model based on the human visual system for semantic levels of categorization is introduced. We showed that the categorization problem for up-right and inverted images can be solved without taking advantage of feedback, but for the occlusion and deletion problems, top-down feedback is necessary. The proposed computational model has three feedback paths that express the effects of expectation and the perceptual task, and it is described by the type of problem that the model seeks to solve and the level of categorization. Depending on the semantic level of the asked question, the model changes its neuronal structure and connections. Another application of recursive paths is solving the expectation effect problem, that is, compensating the reduce in firing rate by the top-down influences due to the available features in the object. In addition, in this paper, a psychophysical experiment is performed and top-down influences are investigated through this experiment. In this experiment, by top-down influences, the speed and accuracy of the categorization of the subjects increased for all three categorization levels. In both the presence and absence of top-down influences, the remarkable point is the superordinate advantage.