Analysis of the human connectome data supports the notion of a "Common Model of Cognition" for human and human-like intelligence across domains.

The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains.

Expanding and Repositioning Cognitive Science.

Cognitive science has converged in many ways with cognitive psychology, but while also maintaining a distinctive interdisciplinary nature. Here we further characterize this existing state of the field before proposing how it might be reconceptualized toward a broader and more distinct, and thus more stable, position in the realm of sciences.

Significance of asymptomatic biliary tract disease in heart transplant recipients.

Biliary disease in conjunction with heart transplantation was encountered in 13 of 33 patients: in the past history (three patients), at pretransplant evaluation (nine patients), and appearing de novo after transplantation (one patient). Four patients with asymptomatic cholelithiasis underwent transplantation: biliary complications requiring emergency and/or urgent surgery occurred in all, with two deaths. Potentially complicating factors included (1) untoward effects of steroids on tissue healing and infection and (2) interaction between liver dysfunction and/or external bile loss and cyclosporine metabolism.

R1-soar: an experiment in knowledge-intensive programming in a problem-solving architecture.

This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators.