Description: Cognitive science and artificial intelligence (AI) have a long-standing shared history. Early research in AI was inspired by human intelligence and shaped by cognitive scientists (e.g., Elman, 1990; Rumelhart and McClelland, 1986). At the same time, efforts in understanding human learning and processing used methods and data borrowed from AI to build cognitive models that mimicked human cognition (e.g., Anderson, 1975; Tenenbaum et al., 2006; Lieder & Griffiths, 2017; Dupoux, 2018). In the last five years the field of AI has grown rapidly due to the success of large-scale deep learning models in a variety of applications (such as speech recognition and image classification). Interestingly, algorithms and architectures in these models are often loosely inspired by natural forms of cognition (such as convolutional architectures and experience replay; e.g. Hassabis et al., 2017). In turn, the improvement of these algorithms and architectures enabled more advanced models of human cognition that can replicate, and therefore enlighten our understanding of, human behavior (Yamins & DiCarlo, 2016; Fan et al., 2018; Banino et al., 2018; Bourgin et al., 2019). Empirical data from cognitive psychology has also recently played an important role in measuring how current AI systems differ from humans and in identifying their failure modes (e.g., Linzen et al., 2016; Lake et al., 2017; Gopnik, 2017; Nematzadeh et al., 2018; Peterson et al., 2019; Hamrick, 2019).

The recent advancements in AI confirm the success of a multidisciplinary approach inspired by human cognition. However, the large body of literature supporting each field makes it more difficult for researchers to engage in multidisciplinary research without collaborating. Yet, outside domain-specific subfields, there are few forums that enable researchers in AI to actually connect with people from the cognitive sciences and form such collaborations. Our workshop aims to inspire connections between AI and cognitive science across a broad set of topics, including perception, language, reinforcement learning, planning, human-robot interaction, animal cognition, child development, and reasoning.