Behavior Research Methods

, Volume 46, Issue 4, pp 1007–1012 | Cite as

Simplifying the interaction between cognitive models and task environments with the JSON Network Interface

  • Ryan M. Hope
  • Michael J. Schoelles
  • Wayne D. Gray
Article
  • 307 Downloads

Abstract

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.

Keywords

Cognitive architecture ACT-R EPIC IPC TCP JSON Common Lisp Python 

References

  1. Addyman, C., & French, R. M. (2012). Computational modeling in cognitive science: A manifesto for change. Topics in Cognitive Science, 4, 332–341.PubMedCrossRefGoogle Scholar
  2. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111, 1036–1060. doi:10.1037/0033-295X.111.4.1036 PubMedCrossRefGoogle Scholar
  3. Büttner, P. (2010). “Hello Java!” Linking ACT-R 6 with a Java simulation. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 289–290). Philadelphia: Drexel University.Google Scholar
  4. Byrne, M. D., & Anderson, J. R. (1998). Perception and action. In J. R. Anderson & C. Lebiére (Eds.), The atomic components of thought (pp. 167–200). Hillsdale: Erlbaum.Google Scholar
  5. Crockford, D. (2006). The application/json media type for JavaScript Object Notation (JSON).Google Scholar
  6. Destefano, M. (2010). The mechanics of multitasking: The choreography of perception, action, and cognition over 7.05 orders of magnitude. Troy: Unpublished doctoral dissertation, Rensselaer Polytechnic Institute.Google Scholar
  7. Ehret, B. D., Gray, W. D., & Kirschenbaum, S. S. (2000). Contending with complexity: Developing and using a scaled world in applied cognitive research. Human Factors, 42, 8–23.PubMedCrossRefGoogle Scholar
  8. Gray, W. D. (1995). VCR-as-paradigm: A study and taxonomy of errors in an interactive task. In K. Nordby, P. Helmersen, D. J. Gilmore, & S. A. Arnesen (Eds.), Human–computer interaction: Interact ’95 (pp. 265–270). New York: Chapman & Hall.Google Scholar
  9. Gray, W. D., Kirschenbaum, S. S., & Ehret, B. D. (1997). The précis of Project Nemo, phase 1: Subgoaling and subschemas for submariners. In M. G. Shafto & P. Langley (Eds.), Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society (pp. 283–288). Hillsdale: Erlbaum.Google Scholar
  10. Gray, W. D., & Sabnani, H. (1994). Why you can’t program your VCR, or, predicting errors and performance with production system models of display-based action. In Companion to the CHI 94 Conference on Human Factors in Computing Systems (pp. 79–80). New York: ACM Press.CrossRefGoogle Scholar
  11. Kieras, D. E., & Meyer, D. E. (1997). An overview of the EPIC architecture for cognition and performance with application to human–computer interaction. Human–Computer Interaction, 12, 391–438.CrossRefGoogle Scholar
  12. Ong, R., & Ritter, F. E. (1995). Mechanisms for routinely tying cognitive models to interactive simulations. Osaka: Paper presented at the 6th International Conference on Human–Computer Interaction (HCI International ’95).Google Scholar
  13. Ritter, F. E., Baxter, G. D., Jones, G., & Young, R. M. (2000). Supporting cognitive models as users. ACM Transactions on Computer–Human Interaction, 7, 141–173. doi:10.1145/353485.353486 CrossRefGoogle Scholar
  14. Salvucci, D. D. (2006). Modeling driver behavior in a cognitive architecture. Human Factors, 48, 362–380.PubMedCrossRefGoogle Scholar
  15. Salvucci, D. D. (2009). Rapid prototyping and evaluation of in-vehicle interfaces. ACM Transactions on Computer–Human Interaction, 16, 9. doi:10.1145/1534903.1534906 CrossRefGoogle Scholar
  16. Salvucci, D. D. (2013). Integration and reuse in cognitive skill acquisition. Cognitive Science, 37, 829–860.PubMedCrossRefGoogle Scholar
  17. Schoelles, M. J., & Gray, W. D. (2011). Cognitive modeling as a tool for improving runway safety. Dayton: Paper presented at the 16th International Symposium on Aviation Psychology (ISAP).Google Scholar
  18. Schoelles, M. J., & Gray, W. D. (2012). Simpilot: An exploration of modeling a highly interactive task with delayed feedback in a multitasking environment. Berlin: Paper presented at the 12th International Conference on Cognitive Modeling.Google Scholar

Copyright information

© Psychonomic Society, Inc. 2013

Authors and Affiliations

  • Ryan M. Hope
    • 1
  • Michael J. Schoelles
    • 1
  • Wayne D. Gray
    • 1
  1. 1.Department of Cognitive ScienceRensselaer Polytechnic InstituteTroyUSA

Personalised recommendations