Neural Mechanism of Spatial Frequency Representation in Face Categorization: an ECoG Study*

2010 ◽  
Vol 37 (7) ◽  
pp. 786-793 ◽  
Author(s):  
Liang SHI ◽  
Rui-Jie WU ◽  
Cui-Ping XU ◽  
Shou-Wen ZHANG ◽  
Hong-Wei ZHU ◽  
...  
1988 ◽  
Vol 14 (1) ◽  
pp. 37-68 ◽  
Author(s):  
Lowell D Jacobson ◽  
Harry Wechsler

Perception ◽  
1972 ◽  
Vol 1 (1) ◽  
pp. 111-119 ◽  
Author(s):  
C Blakemore ◽  
E T Garner ◽  
J A Sweet

Under appropriate conditions, with good depth cues, the perception of the bar width or spatial frequency of a pattern of black and white stripes (a grating) shows excellent size constancy. Two gratings at different distances look similar in spatial frequency when the actual width, not the angular width, of their stripes is the same. Adaptation to a high-contrast grating causes a rise in the threshold contrast for detecting gratings of similar orientation and spatial frequency. This aftereffect transfers from one eye to the other, so it probably depends on binocular orientation-selective neurones in the visual cortex. With the adapting grating at three times the distance of the test grating the maximum elevation of threshold occurs for exactly the same angular spatial frequency as that of the adapting pattern. Therefore the neural mechanism for size-constancy scaling probably occurs after the visual cortex, perhaps in the inferotemporal cortex.


2019 ◽  
Author(s):  
Huatian Wang ◽  
Qinbing Fu ◽  
Hongxin Wang ◽  
Paul Baxter ◽  
Jigen Peng ◽  
...  

AbstractWe present a new angular velocity estimation model for explaining the honeybee’s flight behaviours of tunnel centring and terrain following, capable of reproducing observations of the large independence to the spatial frequency and contrast of the gratings in visually guide flights of honeybees. The model combines both temporal and texture information to decode the angular velocity well. The angular velocity estimation of the model is little affected by the spatial frequency and contrast in synthetic grating experiments. The model is also tested behaviourally in Unity with the tunnel centring and terrain following paradigms. Together with the proposed angular velocity based control algorithms, the virtual bee navigates well in a patterned tunnel and can keep a certain distance from undulating ground with gratings in a series of controlled trials. The results coincide with both neuron spike recordings and behavioural path recordings of honeybees, demonstrating that the model can explain how visual motion is detected in the bee brain.Author summaryBoth behavioural and electro-physiological experiments indicate that honeybees can estimate the angular velocity of image motion in their retinas to control their flights, while the neural mechanism behind has not been fully understood. In this paper, we present a new model based on previous experiments and models aiming to reproduce similar behaviours as real honeybees in tunnel centring and terrain following simulations. The model shows a large spatial frequency independence which outperforms the previous model, and our model generally reproduces the wanted behaviours in simulations.


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