The anthropological and economic history of humanity gives evidence of a progression of cognitive frameworks. There are three cognitive perspectives, in order: living in the present, living in the past, and living in the future. They correspond to three levels of competency with abstract thought: concrete thought only, abstract thought with correlations, and abstract thought with both correlations and causality. This appears to explain the fundamental differences between primitive cultures, traditional cultures, and modern cultures: differences in economics, politics, personality, and anthropological differences in general. So, not only does this theory succinctly explain a wide range of human behavior, but because it does, it appears to be a valid theory and a promising way to decompose abstract thought into its component parts for future cognitive research. These frameworks are discussed along with their implications of exploiting this progression to simplify the problem of developing an AI.
Pigs are more than just a source for delicious meat; they have innate personalities and moods that are affected by their living conditions, and further, they can even be categorized as optimists or pessimists. Research has shown that porcines, just like humans, form judgments by "incorporating aspects of stable personality traits and more transient mood states." Published Wednesday in the journal Biology Letters, the study is titled "Mood and personality interact to determine cognitive biases in pigs" and it tests "the hypothesis that mood and personality interact to influence cognitive bias in the domestic pig." For that purpose, 36 pigs (24 males and 12 females) were divided into two groups and housed in one of two set-ups that were known to affect their moods. Both the housing environments were similar but the more comfortable digs had deeper straw and larger space.
Research on cognitive architectures attempts to develop unified theories of the mind. This paradigm incorporates many ideas from other parts of AI, but it differs enough in its aims and methods that it merits separate treatment. In this paper, we review the notion of cognitive architectures and some recurring themes in their study. Next we examine the substantial progress made by the subfield over the past 40 years, after which we turn to some topics that have received little attention and that pose challenges for the research community.
Historically, AI research has understandably focused on those aspects of cognition that distinguish humans from other animals - in particular, our capacity for complex problem solving. However, with a few notable exceptions, narratives in popular media generally focus on those aspects of human experience that we share with other social animals: attachment, mating and child rearing, violence, group affiliation, and inter-group and inter-individual conflict. Moreover, the stories we tell often focus on the ways in which these processes break down. In this paper, I will argue that current agent architectures don't offer particularly good models of these phenomena, and discuss specific phenomena that I think it would be illuminating to understand at a computational level.