University of Groningen Cognitive Architectures : Innate or Learned ?

Cognitive architectures are generally considered to be theories of the innate capabilities of the (human) cognitive system. Any knowledge that is not innate is encoded in the architectures memory systems, either by the modeler or learned by the architecture itself. However, in human intelligent behavior few things are innate. An alternative is to acknowledge that learning occurs at different levels of abstraction. A standard model of the mind should therefore span multiple levels of abstraction, encouraging research efforts to establish learning mechanism that connect them. Human babies are born almost completely helpless. Although developmental psychology has a lot to say about what newborns are already capable of, they acquire almost everything they need to know at some stage in life. This is why the human species is so successful: it can adapt to many different circumstances, and is therefore, with the same genome, successful in current day’s society just as well as, say, the time that we were still hunter-gatherers. Piaget (1952) argued that child development goes through a number of stages, starting with a sensorimotor stage, and progressing to more cognitive and abstract stages with age. Even though developmental psychologists still debate the existence of concrete stages, it is clear that learning in an infant is quite different from learning in an adult. Current cognitive architectures, such as ACT-R (Anderson 2007), Soar (Laird, Newell, and Rosenbloom 1987) and PRIMs (Taatgen 2013), have a somewhat more simple view of development. The assumption is that the cognitive architecture represents the innate cognitive capabilities of intelligence, and that everything that is learned is represented as knowledge and skills in the memory systems of the architecture (Laird, Lebiere, and Rosenbloom in press). This may seem a reasonable assumption, but it carries along a big problem. Whenever we want to create a model of a specific task, we have to encode the knowledge to perform that task in terms of knowledge in the memory of the architecture. However, the architecture’s memory is empty, which means that there is a gap between what the architecture can do by itself, and the requirements of the task. The modeler has to fill this gap with ad hoc solutions that are probably not completely justified, but that help to achieve the particular goal of modeling the particular task. However, this approach means that many models, even within the same architecture, are completely incompatible with one another. Therefore, it has the danger that it runs into the same problem that Newell (1973) signaled in psychology in general: that it produces a collection of micro-theories for specific phenomena, but that it fails to achieve a unified theory of cognition. Moreover, certain domains that are relatively late in development, such as language and social cognition, are very hard to model in an architecture where knowledge starts from scratch, because they require large amounts of prerequisite knowledge. If cognitive architectures want to break new grounds, they have to acknowledge this problem, and look for new solutions. Horizontal vs. Multilevel Architectures Newell (1973) identified multiple levels of abstraction to study human cognition, ranging from the level of organelles that operate in the 100 μs time range to social cognition that operates at a time scale of months. What Newell considered the levels that are most interesting for cognition are in between these extremes: the level of deliberate acts (100 ms), operations (1 sec), and unit tasks (10 sec). It is tempting, and consistent with ideas that go back to Turing (1950), to pick a level of abstraction as the base level to model human cognition, consider all levels below that level as implementation, and build up the theory from that level. I call this a horizontal architecture, because it focusses on a single level of abstraction as a starting point both up and down (Figure 1). The developmental literature suggests another approach, in which we take into account that learning takes place at each level of abstraction. I call this a multilevel architecture. The central idea in the multilevel architecture is that there is not a particular level of abstraction that is “special”. Depending on the phenomenon we want to model, we have to select one or more levels of abstraction that are most appropriate. Therefore, the neural level is not the most appropriate to model learning the past tense (Taatgen and Anderson 2002), and object recognition is hard to model at a symbolic level. Multiple Levels of Learning Human learning can takes many different forms. If students are cramming for an exam, trying to force facts into their A Standard Model of Mind: AAAI Technical Report FS-17-05