Best path Length On a Semantic Self-Organizing Map (BLOSSOM) is an experimental, unsupervised text-clustering technique with applications in computational linguistics and data mining. This project involves the development and refinement of BLOSSOM as a Measure of Semantic Relatedness and the exploration of its benefits.

CTK is a DTO sponsored project that seeks to support interface design for advanced visualization and interaction techniques. We achieve this using the VIA architecture as a testbed for software applications, performing detailed user analysis (including ProtoMatch eye-data analysis), visual and semantic saliency analyses (Visual Saliency Maps, Measures of Semantic Distance, Information Foraging), developing high-fidelity cognitive models for robust and exhaustive interface testing (simBorgs), and creating methods for assessing dynamic changes in cognitive workload (Cognitive Metrics Profiling).

Configural Memory with Network Reinforcement Learning (CMNRL, pronounced Sea Mineral) is a categorization-based cognitive architecture and an autonomous agent. It is an unsupervised incremental neural network with two main components. The first component, configural memory, is similar to the configural approaches of Gluck & Bower (1988) and Heydemann (1995). Configural approaches have been used to model a wide variety of psychological data (e.g. Pearce, 1994). The second component of CMNRL, Network Reinforcement Learning (NRL) extends traditional reinforcement learning (Sutton & Barto, 1998) by allowing for simultaneous updates of multiple state-action pairs. Just as configural memory, reinforcement learning has been affirmed as a psychologically and biologically plausible mechanism (e.g. Holroyd & Coles, 2002).

Measures of Semantic Relatedness (MSRs) are computational means for calculating the association strength between terms. MSRs have been used to produce models of human web-browsing behavior (Pirolli, 2005), augmented search engine technology (Dumais, 2003), essay-grading algorithms for ETS (Landauer, Foltz, & Laham, 1998), and could be useful for any cognitive models or AI agents that have to deal with text.

http://cwl-projects.cogsci.rpi.edu/msr

Apart from being a game that we all know and love, Tetris is also a relatively complex cognitive task that requires dynamic task processing, advanced perceptual capabilities, and involves both top-down and bottom-up strategies. Given the lack of cognitive models designed for such dynamic tasks, we explore the Tetris environment as a way to get at human perception, learning and memory, categorization, attention, and procedure/strategy selection.

TRACS is a 'Tool for Research on Adaptive Cognitive Strategies'. It is a simple card game developed by Kevin Burns, and can be played online at http://tracsgame.com. (Additional background information is available at http://mentalmodels.mitre.org.) As in the original TRACS studies, we are using the game to assess people's abilities to keep track of the changing odds throughout the course of a game. By equipping our application with eye-tracking and various optional enhancements we are able to run subtle variations and gather a full range of experimental data. Consistent with our "to understand it, build it" approach, we use computational cognitive modeling techniques to test our theories as to the cognitive mechanisms involved.
Our findings have direct implications for research in memory, embodied cognition, and interactive behavior. In addition, our application will be used in combination with the CogWorks MultiWorld for further research on the effects of cognitive workload.

The error-prone task of programming a VCR is representative of a growing number of end-user programmable devices. To help understand this task a display-based model of VCR programming was developed and implemented as a computational cognitive model. The model accurately predicted the vast majority of correct and error recovery keypresses collected from human subjects (Gray, 1995). Further VCR studies were done to compare strategies given additional costs of unnatural interface design.
Currently VCR programming data are being analyzed for memory errors and memory strategies. Computation cognitive models are constantly being developed to match and explain empirical data.

The CogWorks Visualization-Interaction Architecture is a flexible software system designed to facilitate R&D for advanced, dynamic, highly visual interfaces such as those produced by University of Maryland (UMd) and by RPI’s RAIR Lab. Although our configuration uses one PC and one Mac, VIA is platform independent. As all communication between computers occurs over TCP/IP, the key component of VIA, the handler (see Figure) can be written for any platform (Unix, Windows XP, Mac OS 10, etc) and in any language. VIA release 0.1 was used in Sept 2005 with two visualization tools developed by UMd – TreePlus and GraphPlus. The current C# handler can interact with any program that uses the standard C# GUI library (WinForm) and that is written using standard object-oriented design techniques. In the near future we expect to develop handlers for Piccolo™, LispWorks™, and possibly, Java™.