Estimating the subjective perception of object size and position through brain imaging and psychophysics

Abstract

Perception is subjective and context-dependent. Size and position perception are no exceptions. Studies have shown that apparent object size is represented by the retinotopic location of peak response in V1. Such representation is likely supported by a combination of V1 architecture and top-down driven retinotopic reorganisation. Are apparent object size and position encoded via a common mechanism? Using functional magnetic resonance imaging and a model-based reconstruction technique, the first part of this thesis sets out to test if retinotopic encoding of size percepts can be generalised to apparent position representation and whether neural signatures could be used to predict an individual’s perceptual experience. Here, I present evidence that static apparent position – induced by a dot-variant Muller-Lyer illusion – is represented retinotopically in V1. However, there is mixed evidence for retinotopic representation of motion-induced position shifts (e.g. curveball illusion) in early visual areas. My findings could be reconciled by assuming dual representation of veridical and percept-based information in early visual areas, which is consistent with the larger framework of predictive coding. The second part of the thesis sets out to compare different psychophysical methods for measuring size perception in the Ebbinghaus illusion. Consistent with the idea that psychophysical methods are not equally susceptible to cognitive factors, my experiments reveal a consistent discrepancy in illusion magnitude estimates between a traditional forced choice (2AFC) task and a novel perceptual matching (PM) task – a variant of a comparison-of-comparisons (CoC) task, a design widely seen as the gold standard in psychophysics. Further investigation reveals the difference was not driven by greater 2AFC susceptibility to cognitive factors, but a tendency for PM to skew illusion magnitude estimates towards the underlying stimulus distribution. I show that this dependency can be largely corrected using adaptive stimulus sampling

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