Integrating insights from Psychology, Neuroscience and Artificial Intelligence to mechanistically understand visual attention.
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Deep neural networks (DNNs) are AI models that are good at recognizing objects. DNNs and the part of the brain that deals with visual information processing are remarkable similar. In this thesis it was investigated whether DNNs can be used to relate cognitive theories to (more) biologically plausible mechanisms that are important for these behaviors. Looking at three different forms of behavior (arousal, selective attention, and image recognition is a sequence of images) we show that DNNs can be used to bridge cognitive theories and brain mechanisms.
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