Ryan M Hope

hoper2rpi.edu

Rensselaer Polytechnic Institute

Publications:

Anderson, J. R., Betts, S., Fincham, J. M., Hope, R., & Walsh, M. W. (2020). Reconstructing fine-grained cognition from brain activity. NeuroImage, 221, 116999.

Anderson, J.R., Betts, S., Bothell, D., Hope, R., Lebiere, C. (2019) Learning Rapid and Precise Skills. Psychological Review, 126(5), 727-760.

Process models of cognition, written in architectures such as ACT-R and EPIC, should be able to interact with the same software with which human subjects interact. By eliminating the need to simulate the experiment, this approach would simplify the modeler’s effort, while ensuring that all steps required of the human are also required by the model. In practice, the difficulties of allowing one software system to interact with another present a significant barrier to any modeler who is not also skilled at this type of programming. The barrier increases if the programming language used by the modeling software differs from that used by the experimental software. The JSON Network Interface simplifies this problem for ACT-R modelers, and potentially, modelers using other systems.

Copyright © 2002-2013 ACT-R Research Group

Department of Psychology, Carnegie Mellon University