Any image can be described in terms of its statistics (i.e. quantitative parameters calculated from the image, for example RMS-contrast, the skewness of image brightness distribution, and slope constant of an average amplitude spectrum). It was previously shown that insect and vertebrate visual systems are optimised to the statistics common among natural scenes. However, the exact mechanisms of this process are still unclear and need further investigation. This thesis presents the results of examining links between some image statistics and visual responses in humans and hoverflies. It was found that while image statistics do not play the main role when hoverflies (Eristalis tenax and Episyrphus balteatus) chose what flowers to feed on, there is a link between hoverfly (Episyrphus balteatus) active behaviours and image statistics. There is a significant difference in the slope constant of the average amplitude spectrum, RMS contrast and skewness of brightness distribution between photos of areas where hoverflies were hovering or flying. These photos were also used to create a prediction model of hoverfly behaviour. After model validation, it was concluded that photos of both the ground and the surround should be used for best prediction of behaviour. The best predictor was skewness of image brightness distribution. By using a trackball setup, the optomotor response in walking hoverflies (Eristalis tenax) was found to be influenced by the slope constant of an average amplitude spectrum. Intracellular recording showed that the higher-order neuron cSIFE (The centrifugal stationary inhibited flicker excited) in the hoverfly (Eristalis tenax) lobula plate was inhibited by a range of natural scenes and that this inhibition was strongest in a response to visual stimuli with the slope constant of an average amplitude spectrum of 1, which is the typical value for natural environments. Based on the results of psychophysics study in human subjects it was found that sleep deprivation affects human perception of naturalistic slope constants differently for different image categories (“food” and “real world scenes”). These results help provide a better understanding of the link between visual processes and the spatial statistics of natural scenes.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-328041 |
| Date | January 2017 |
| Creators | Dyakova, Olga |
| Publisher | Uppsala universitet, Fysiologi, Flinders University, Uppsala |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, 1651-6206 ; 1355 |
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