Deep neural network models of visual cognition

Can deep convolutional neural networks (DCNNs) be used as models of visual cognition to capture the interaction between sensation and cognition during object recognition? In this thesis, I evaluated this question for three different modulations of object recognition: arousal state, spatial attention, and previous visual experience, while building on extensive prior research on object recognition in the visual ventral stream and deep neural networks.

For each of these factors, the leading goal was to formulate the interaction between object recognition and cognitive modulation in an image-computable, mechanistic model that reproduces key characteristics of human behaviour. In Chapter 2 of this thesis, I used this approach to investigate how a global gain modulation — previously linked to changes in arousal state — may affect visual processing and recognition performance on a series of tasks with varying difficulty. In Chapter 3, I assessed the efficacy of different attention mechanisms at implementing selective processing during object recognition within naturalistic scenes. In Chapter 4 and 5, I delved into the consequences of dynamic sensory experience for object recognition, examining how different neural mechanisms, such as lateral recurrence and sensory adaptation, may support our ability to recognize objects across a wide range of timescales and during various dynamic tasks. Chapter 6 and 7 summarize and discuss the opportunities and challenges afforded by DCNNs as models of visual cognition. Overall, the mechanistic insights presented in this thesis provide encouraging examples of the usage of DCNNs as a linking framework between sensation and cognitive modulation.