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Much of what we know is learned from what we see. Our early experience is awash with sensory input; before we can even speak we are observing and learning from the world around us. Focusing on the emergence of semantic knowledge from visual experience, I will review where the fields of cognitive neuroscience, psychology and computer vision stand in terms of accounting for our robust visual understanding. Deep learning networks continue to excel as models for human vision, but are they truly accounting for the wealth of semantic information present in our neural representations? The human vision literature is expansive, but are we missing key insights from naturalistic, contextualised experiments that will delineate how to build robust semantic knowledge? Finally, I argue that progress can be made in both computer and biological vision research by looking for inspiration in the most efficient learning systems that we know of: the human infant. Through an exploration of the downfalls of current vision research and computational modelling I discuss how infant-inspired machine learning just might improve our current best models of the brain and, in turn, enlighten our knowledge of how abstract semantic knowledge is built from the physical world.

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Human experience is defined by our marked ability to learn about the world and to make meaningful interactions with the things around us. As we grow and develop, we learn that a dog is a friendly animal that is similar yet distinct from a cat. That a tiger – although a cat – is dangerous and to be feared; that it is expected to appear amongst trees in the jungle. We somehow master the art of using a knife, whose function can change multiple times during as simple a task of making a sandwich as we cut bread, slice meat and scoop and spread butter. All of these are examples of knowledge acquired through life, and this must be housed in some way within the neurocognitive processes of our brain. Questions on the nature of knowledge go back as far as history can track, and the conversation spans multiple research fields from philosophy and psychology to cognitive science, neuroscience and computer intelligence. In this essay, I will present an integrative account of the research into human knowledge acquisition, discussing various ideas and models from a range of disciplines. What insights can we get from philosophical theories of knowledge, and on the other end, what evidence do we have for neural mechanisms of knowledge? The question of knowledge itself is a vast topic that is impossible to cover completely in one coherent review, but the aim of this work is to provide an overview and introduction to the key ideas spanning across different fields’ exploration of this puzzling topic.

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This is something a bit different. Written as part of a broad curriculum module in English Literature, I discuss here how DeLillo succeeds in calling out the capitalistic values in the modern world of technology - even before the internet existed. Ever the neuroscientist, I examine how his use of language awakens our senses to ensure we feel disturbed and engulfed by the paranoia that comes with being surrounded by technological White Noise. The points I make in this essay are interesting when considered in the context of my own research and remind me that efforts in advancing technology and artificial intelligence must always be considered with ethics and welfare in mind.

“White Noise was first published in 1985, on the brink of the information age and prior to the world of the internet in which we live today. However, through White Noise DeLillo still managed to foresee and call out the capital values of the modern world of technology as they were emerging 30 years ago … The narrative is a truly sensory experience, but one that is not natural or typical. This awakening of the senses in such a familiarly unfamiliar way evokes an eerie sense of unease within the reader. Throughout the novel, there is a bombardment of raucous sound and technological white noise.”