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dc.contributor.advisorBednar, Jim
dc.contributor.advisorHancock, Peter
dc.contributor.authorZhao, Chen
dc.date.accessioned2012-01-19T15:01:46Z
dc.date.available2012-01-19T15:01:46Z
dc.date.issued2011-11-24
dc.identifier.urihttp://hdl.handle.net/1842/5767
dc.description.abstractUnderstanding how human and animal visual systems work is an important and still largely unsolved problem. The neural mechanisms for low-level visual processing have been studied in detail, focusing on early visual areas. Much less is known about the neural basis of high-level perception, particularly in humans. An important issue is whether and how lessons learned from low-level studies, such as how neurons in the primary visual cortex respond to oriented edges, can be applied to understanding highlevel perception, such as human processing of faces. Visual aftereffects are a useful tool for investigating how stimuli are represented, because they reveal aspects of the underlying neural organisation. This thesis focuses on identifying neural mechanisms involved in high-level visual processing, by studying the relationship between low- and high-level visual aftereffects. Previous psychophysical studies have shown that humans exhibit reliable orientation (tilt) aftereffects, wherein prolonged exposure to an oriented visual pattern systematically biases perception of other orientations. Humans also show face identity aftereffects, wherein prolonged exposure to one face systematically biases perception of other faces. Despite these apparent similarities, previous studies have argued that the two effects reflect different mechanisms, in part because tilt aftereffects show a characteristic S-shaped curve, with the effect magnitude increasing and then decreasing with orientation difference, while face aftereffects appeared to increase monotonically (in various units of face morphing strengths) with difference from a norm (average) face. Using computational models of orientation and face processing in the visual cortex, I show that the same computational mechanisms derived from early cortical processing, applied to either orientation-selective or face-selective neurons, are sufficient to replicate both types of effects. However, the models predict that face aftereffects would also be S-shaped, if tested on a sufficiently wide range of face stimuli. Based on the modelling work, I designed psychophysical experiments to test this theory. An identical experimental paradigm was used to test both face gender and tilt aftereffects, with strikingly similar S-shape curves obtained for both conditions. Combined with the modelling results, this result provides evidence that low- and high level visual adaptation reflect similar neural mechanisms. Other psychophysical experiments have recently shown interactions between low and high-level aftereffects, whereby orientation and line curvature processing (in early visual area) can influence judgements of facial emotion (by high-level face-selective neurons). An extended multi-level version of the face processing model replicates this interaction across levels, but again predicts that the cross-level effects will show similar S-shaped aftereffect curves. Future psychophysical experiments can test these predictions. Together, these results help us to understand how stimuli are represented and processed at each level of the visual cortex. They suggest that similar adaptation mechanisms may underlie both high-level and low-level visual processing, which would allow us to apply much of what we know from low-level studies to help understand high-level processing.en
dc.contributor.sponsorMedical Research Council (MRC)en
dc.contributor.sponsorDTC and Institute for Adaptive and Neural Computationen
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.subjectaftereffectsen
dc.subjectvisual cortexen
dc.subjectface recognitionen
dc.subjectcomputational modelingen
dc.titleNeural mechanisms for face and orientation aftereffectsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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