Dramatic advances in the last six months in High Definition Fiber Tracking (HDFT) make it possible to image the fiber connectivity from source to destination mapping hundreds of thousands fiber tracks with sufficient resolution to identify the cable level circuit diagram of the human brain. Brain activity imaging studies using functional Magnetic Resonance Imagining (fMRI) identify differential activation patterns as a function of task and level of practice. These data show subnetworks with communication of high bandwidth vector associations, scalar priority and control signals, and interactions with control and meta cognition. The connectivity and activity data support a triarchic cognitive architecture. Processing is the synergistic interaction of three interlinked cognitive computational systems with differential computation role and evolutionary history. These data provided a detailed diagram to guide reverse engineering of the systems levels of the human brain.

How useful are bio-developmental approaches for understanding how cognitive capabilities are acquired? One bio-developmental hypothesis is that human cognition unfolds with maturation as a massive collection of adaptive cognitive “capabilities” expressing pre-structured genetic programs. But the seeming plasticity of human cognition argues against simple formulations of innately-specified anatomical & functional processing system composed of specialized computational modules. One alternative is an architecture using domain-specific predispositions and general learning mechanisms to construct modules from interactions. This lets them emerge and unfold in a self- organized fashion as part of developmental experience. The result is a more dynamic, complex cognitive architecture explaining such things as the drive for sensorimotor control in infants, which is combines the generation of exploratory movements constrained by the interaction of ability and environment followed by the selection and maintenance of adaptive movement patterns (Schlesinger et al. 2000). Such findings are consistent with a view that ontogenetic processes are co-important (and co-dependent) with gene- based evolutionary processes for behavior and cognition.

We discuss metacognitive modelling as an enhancement to cognitive modelling and computing. Metacognitive control mechanisms should enable AI systems to self-reflect, reason about their actions, and to adapt to new situations. In this respect, we propose implementation details of a knowledge taxonomy and an augmented data mining life cycle which supports a live integration of obtained models.

By way of explaining how a brain works logically, human associative memory is modeled with logical and memory neurons, corresponding to standard digital circuits. The resulting cognitive architecture incorporates basic psychological elements such as short term and long term memory. Novel to the architecture are memory searches using cues chosen pseudorandomly from short term memory. Recalls alternated with sensory images, many tens per second, are analyzed subliminally as an ongoing process, to determine a direction of attention in short term memory.

The SNePS knowledge representation, reasoning, and acting system has several features that facilitate metacognition in SNePS-based agents. The most prominent is the fact that propositions are represented in SNePS as terms rather than as sentences, so that propositions can occur as argu- ments of propositions and other expressions without leaving first-order logic. The SNePS acting subsystem is integrated with the SNePS reasoning subsystem in such a way that: there are acts that affect what an agent believes; there are acts that specify knowledge-contingent acts and lack-of-knowledge acts; there are policies that serve as "daemons," triggering acts when certain propositions are believed or wondered about. The GLAIR agent architecture supports metacognition by specifying a location for the source of self-awareness and of a sense of situatedness in the world. Several SNePS-based agents have taken advantage of these facilities to engage in self-awareness and metacognition.

In this article, I claim that research on cognitive architectures is an important path to the development of general intelligent systems. I contrast this paradigm with other approaches to constructing such systems, and I review the theoretical commitments associated with a cognitive architecture. I illustrate these ideas using a particular architecture -- ICARUS -- by examining its claims about memories, about the representation and organization of knowledge, and about the performance and learning mechanisms that affect memory structures. In closing, I consider ICARUS's relation to other cognitive architectures and discuss some open issues that deserve increased attention.

In this article, I claim that research on cognitive architectures is an important path to the development of general intelligent systems. I contrast this paradigm with other approaches to constructing such systems, and I review the theoretical commitments associated with a cognitive architecture. I illustrate these ideas using a particular architecture -- ICARUS -- by examining its claims about memories, about the representation and organization of knowledge, and about the performance and learning mechanisms that affect memory structures. I also consider the high-level programming language that embodies these commitments, drawing examples from the domain of in-city driving. In closing, I consider ICARUS's relation to other cognitive architectures and discuss some open issues that deserve increased attention.

Despite the popularity of connectionist models in cognitive science, their performance can often be difficult to evaluate. Inspired by the geometric approach to statistical model selection, we introduce a conceptually similar method to examine the global behavior of a connectionist model, by counting the number and types of response patterns it can simulate. The Markov Chain Monte Carlo-based algorithm that we constructed Þnds these patterns efficiently. We demonstrate the approach using two localist network models of speech perception.

Despite the popularity of connectionist models in cognitive science, their performance can often be difficult to evaluate. Inspired by the geometric approach to statistical model selection, we introduce a conceptually similar method to examine the global behavior of a connectionist model, by counting the number and types of response patterns it can simulate. The Markov Chain Monte Carlo-based algorithm that we constructed Þnds these patterns efficiently. We demonstrate the approach using two localist network models of speech perception.

How should we decide among competing explanations of a cognitive process given limited observations? The problem of model selection is at the heart of progress in cognitive science. In this paper, Minimum Description Length (MDL) is introduced as a method for selecting among computational models of cognition. We also show that differential geometry provides an intuitive understanding of what drives model selection in MDL. Finally, adequacy of MDL is demonstrated in two areas of cognitive modeling.