Coherent visual perception

Glass Pattern

The detection and recognition of visual objects is a vital skill for our interactions in the world. To achieve it, the brain has to group local image features to global meaningful objects. This perceptual organisation is a challenging operation for the visual system as objects are often camouflaged in cluttered scenes and their image properties (e.g. position, orientation, size) may change as we interact in complex environments. Our work combines behavioural and fMRI measurements to study: a) the integration of global shapes from local features, b) the construction of shape representations selective to combinations of visual features and tolerant to image changes that are critical for object recognition, and c) the role of learning in coherent shape perception. This work will advance our understanding of the link between structure, function and behaviour in the intact brain, provide new insights into the re-organisation and potential recovery of function in the impaired brain, and have potential applications in the design of rehabilitation programmes and artificial vision systems.

Learning to see in noise


Recent behavioural studies have shown that learning can be a key facilitator in perceptual integration for the detection and recognition of objects in cluttered scenes. Further, neurophysiological studies suggest that learning enhances the sensitivity of neural processing. However, little is known about the role of learning in shaping perceptual integration and visual recognition processes across stages of visual analysis in the human brain. In this work, we use human psychophysics and brain imaging to understand the neural plasticity mechanisms that support behavioural improvement in perceptual integration and visual recognition. We ask: How does the human visual brain learn objects in natural cluttered scenes? Does the human visual brain take advantage of natural image regularities (e.g. grouping of elements with similar orientation) that determine the distinctiveness of targets when learning novel objects in cluttered scenes? Does learning facilitate perceptual integration and shape detection in the absence of regularities that usually mediate the grouping of shape contours in natural images? This work will provide a) significant insights into the role of learning in shaping brain functions that mediate key perceptual and cognitive abilities, and b) the foundation for studying the role of learning in visual or cognitive deficits that impair these functions.

Transforming local disparities to global 3D shape

Example Stimuli

Responses to binocular disparity are widespread throughout the visual, temporal and parietal cortices. However, the circuits that transform local binocular disparities to coherent three-dimensional (3D) representations remain unknown. We use human fMRI to test for cortical areas that represent global 3D surfaces as opposed to local disparities. We measured fMRI responses while participants viewed random dot stereograms depicting slanted planes. Using multi-voxel pattern classification analysis, we find that (i) slant can be decoded reliably across visual areas and (ii) there is pronounced fMRI pattern-tuning for slant in early visual (V1, V2) and dorsal (V3, V3A) areas. Furthermore, we find that fMRI responses in area V3A are highly similar when the same slant is depicted, irrespective of changes in spatial extent or overall disparity of the stimuli. However, this is not true in areas V1 and V2. Moreover, opposite slants evoke highly dissimilar responses in area V3A, while there is some similarity between responses in V1. Together these results suggest V1 and V2 responses relate to low-level features (disparity-defined edges and distribution of disparities) while responses in V3A are compatible with the representation of surface slant.

"Coarse" vs "Fine" Representations in the Human Brain

Example Stimuli

"Coarse" and "Fine" discrimination tasks have been used to show that different neural networks can be engaged for the perception of a particular visual cue. For example, studies of disparity processing in the macaque have shown that Coarse depth elicits activity in the dorsal brain regions while Fine depth elicits activity in mainly ventral regions. Current projects combine behavioural, fMRI, and rTMS techniques to investigate Coarse vs Fine processing of a variety of visual cues (e.g., disparity, motion, orientation). We ask questions such as: How are Coarse and Fine discriminations represented in the human brain? What happens with perceptual learning? More specifically, is there a transfer of learning across tasks or across visual cues? Is there a reorganization of brain networks responsible for processing Coarse and Fine tasks after training? This work will enable a better understanding of the specificity and plasticity of our visual system.

Functional and chemical markers of brain plasticity

Example Stimuli

In this research we combine magnetic resonance spectroscopy (MRS), functional magnetic resonance imaging (fMRI) and psychophysics to better characterise the neural correlates of brain plasticity. In particular we are interested in the role of the chief inhibitory neurotransmitter, GABA, during visual learning tasks. By accurately measuring neurotransmitter quantities in brain regions that are active during a task we expect to show the contribution of inhibitory activation. This will complement fMRI studies. We use experimental pulse sequences and novel time courses in our experiments and collaborate with the Brain Tumour Research Group at the Childrens Hospital.

Learning improves internal shape representation revealed by fMRI classification image analysis

Example Stimuli

Classification image analysis has been used successfully for extracting the critical image features that observers use when making perceptual judgments. Using this approach, previous studies have shown that learning re-tunes decision templates and shapes task-relevant features. Although classi?cation images have been used widely in psychophysics, the application of this technique to fMRI studies has been limited due to noise in single-trial fMRI responses. In this study, we aim to develop a novel methodology that uses multi-voxel pattern analysis to extract classi?cation images from fMRI data and reveal the neural representation of behaviorally relevant features. We investigate how learning re-tunes the representation of visual features in the human visual cortex according to their behavioral relevance for perceptual decisions. This work will provide 1) an effective way to extract classification images from fMRI data; 2) significant insights into the role of learning in tuning feature representations in the human brain.

Categorical decisions and learning in the ageing brain

Example Stimuli

Interactions in our complex environments entail prompt decisions for successful actions in novel situations. We examine the links between brain structure and function that mediate the ability of older adults to interpret, learn and categorise novel sensory experiences. Despite significant progress in understanding how the human brain ages at the anatomical and cellular levels, much less is known about the relationship between structural and neural changes that underlie the ageing of cognitive abilities and determine an individuals functional, rather than chronological, age. A core challenge in human cognitive ageing is to understand the mechanisms that lead to rapid cognitive decline in some older adults while others maintain high levels of cognitive performance. This study aims to: a) combine behavioural methods with multimodal imaging techniques (structural MRI, functional MRI, EEG) to relate cognitive performance in ageing to changes in brain morphometry and function, b) develop and validate novel analysis methods for behavioural and imaging data based on advanced mathematical techniques (i.e. machine learning) that provide powerful tools for studying individual variability in cognitive processes across participants. Our findings will advance our understanding of life-long learning and cortical plasticity and have implications for quality of life, early diagnosis and intervention in normal and pathological ageing.