Cognitive architectures are computational theories that attempt to cover as much of cognition as possible. Developers of cognitive architectures need to balance functionality of the theory against its explanatory and predictive power, which are often at odds with one another. This chapter discusses this balance with respect to working memory, cognitive performance, perceptual and motor systems, learning and neuroscience. It discusses which solutions six cognitive architectures offer to these areas, and illustrates this with a number of successful models.

For decades, symbolic models of cognition were the dominant computational approaches of cognition. Today they coexist with subsymbolic, statistical, and hybrid models, but they are still the de facto standard for modeling human reasoning processes. This chapter summarizes important aspects of symbolic and hybrid models of cognition, approaching the topic from different perspectives. After some discussion on historical aspects and the theoretical basis of symbolic models of cognition, cognitive architectures as models for intelligent agents are examined. Subsequently, the role of symbolic computational approaches towards processing natural language, representation of human knowledge, and commonsense reasoning are considered. Then the focus is put on the crucial question of learning new representations and theories, before finally looking at hybrid and neural-symbolic systems combining reasoning and learning and bridging between symbolic and subsymbolic elements.

Computer scientists Jonathan Gratch and Stacy Marsella trace the influence of the 1988 publication of the OCC model, noting its impact on computational models of the interplay between emotion and cognition and on practical applications such as those relating to emotion recognition and the generation of emotion-related behaviors. They explain how OCC’s detailing of specific rules for reasoning about emotions invigorated work in affective computing and artificial intelligence more generally by giving computer scientists a clear pathway for modeling emotion processes. The model, they suggest, had both “upstream influences” on work concerning the cognitive antecedents of emotions, and “downstream influences” on work focused on modeling some of the consequences of emotions, such as those concerned with coping and decision-making and their relation to changes in beliefs, desires, and intentions. Finally, at a more sociology of science level, the authors suggest that the OCC model contributed significantly to bringing together the emotion research community in psychology and computer science communities interested in modeling affective phenomena.