Funding

In our everyday interactions we encounter a plethora of novel situations in different social contexts that require prompt decisions for successful actions. Extracting the key information from the highly complex input of the natural world, integrating information into coherent evidence, and deciding how to interpret it is a computationally challenging task that is far from understood. The goal of the proposed collaboration is to advance our understanding of the neural basis of learning for efficient decisions in complex, novel and uncertain environments by bringing together interdisciplinary expertise in advanced computational, behavioural and brain imaging methods.

We aim to develop new tools for cognitive ageing research by combining methods from mathematics, computer science, psychology, and neuroscience. In particular, we will combine advanced mathematical approaches (i.e. machine learning) for the analysis of biological data (behavioural performance, functional brain activations) with multimodal brain imaging techniques (structural MR, functional MRI, EEG) and behavioural methods. This integration of advanced measurement and analysis methods will allow us to develop new sensitive tools for studying the variability of cognitive ageing across individuals from rapid decline to sustained high levels of performance. Our methods and findings will provide new insights in understanding life-long learning and cortical plasticity and will be potentially useful for early diagnosis and intervention in normal and pathological ageing.

Our everyday decisions are determined by knowledge about our environments and abstract rules that allow us to interpret novel experiences in different contexts. We will examine changes in brain structure and function that underlie our ability to learn decision rules across the life span. We will combine mathematical, behavioural and non-invasive imaging methods that allow us to study the human brain in vivo. Our findings will provide novel insights in understanding the mechanisms that mediate rapid cognitive decline in some adults while graceful ageing in others and have potential implications for diagnosis and intervention in neurodegenerative disorders common in ageing.

Our ability to extract abstract information from our experiences and group it into meaningful units (categories) is a fundamental cognitive skill for interpreting the complex environments we inhabit. How does the human brain learn about the regularities and context of novel perceptual experiences that have not been honed by evolution and development and decide on their interpretation and classification? We propose a novel interdisciplinary approach that integrates advanced multimodal imaging (fMRI, MEG, EEG) methods and state-of-the art machine learning algorithms to examine the neural architecture that underlies classification learning and decisions in the human brain. We aim to a) create an electrical-haemodynamic signal space in which neuronal assemblies and their interactions can be characterised, and b) to develop a unified algorithmic method for efficiently analyzing neural imaging and behavioural data. In particular, we will use machine pattern classifiers to define perceptual decision images that reveal the critical stimulus features on which the observers base their perceptual classifications, and neural decision images that reveal the neural selectivity, plasticity and dynamics with which these features are encoded and learnt by the human brain. Our methodological and theoretical developments will provide a) novel and sensitive tools for the assessment of the behavioural and neural signatures of perceptual decisions in neuroscience, and b) novel challenges and insights in machine learning for the optimisation of biologically-constrained algorithms with direct applications for expert recognition systems. Further, our findings will advance our understanding of the link between sensory input, neural code and human behaviour and have potential applications for studying the development of perceptual decision processes across the life span, and their impairment and potential for recovery of function in ageing and disorders of visual and social cognition.

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 into global perceptual units (objects). This process of perceptual integration is a challenging operation for the visual system as objects are often camouflaged in the cluttered environments we inhabit. The extent of the difficulty of this problem is highlighted by the fact that perceptual integration processes develop slowly (between 5 and 14 years of age) and are severely disrupted by visual deficits early in life. 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. The aim of the proposed research is to use human psychophysics and brain imaging to provide significant new insight into the neural plasticity mechanisms that support behavioural improvement in perceptual integration and visual recognition.