GANCAT: Generative Adversarial Networks for CATegorization

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When choosing experimental stimuli, cognitive scientists often face a tension between experimental control and ecological validity. While simple stimuli provide rigorous control, their lack of complexity leaves an explanatory gap between laboratory and real-life conditions. Generative Adversarial Networks for CATegorization (GANCAT) helps to bridge this gap by developing methods to use a novel machine-learning technique, Generative Adversarial Networks (GANs) in the generation of complex, yet fully controllable visual stimuli. The methods developed by GANCAT will allow cognition researchers to create large numbers of naturalistic stimuli varying across experimentally relevant properties. As such, GANCAT aligns with the European Commission's plan to achieve Excellence in AI, by encouraging AI uptake and ensuring that AI systems work for the people. GANCAT's research programme combines state-of-the-art deep-learning techniques and behavioural methods for the study of categorisation, psychological similarity, and attention. First, GANCAT compares the categorisation of complex stimuli (histology samples) as supported by real or GAN-generated samples. Second, GANCAT couples convolutional neural networks to derive humanlike judgments of similarity for GAN-generated samples and uses those judgments to identify the mapping between GAN inputs and the generation of samples that vary across psychologically meaningful dimensions. Finally, GANCAT develops methods that allow control over the visual saliency of the features present in GAN-generated stimuli and uses those methods in the development of adaptive learning algorithms that expedite attentional learning. GANCAT does not only help to bridge the existing knowledge gap between the categorisation of simple and complex visual stimuli, but it also puts special effort into sharing its tools with the research community, to persuade cognitive scientists to welcome the complexity of realistic stimuli in their research programmes.