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An ACT-R model of the Simon Task

This respoitory contains an ACT-R model of the Simon task, together with a set of data that validates the model's assumptions. Both model and experimental results have been published in Stocco et al. (2017).

The model

The model itself borrows from Marsha Lovett's (2005) NJAMOS model of the Stroop task. It also explcitly models the competition between direct and indirect pathways of the basal ganglia as two separate set of rules, "process" and "dont-process" rules.

In turn, this idea is borrowed from my model of Frank's (2004) Probabilistic Stimulus Selection (PSS) Task. The same result could possibily be achieved through other means, but this solution is simple, intutitive, and permits to model competitive dynamics of the BG without changing ACT-R. For an in-depth analysis of why this particular approach is preferrable, see Stocco (2018).

How to run this model

  1. Open a Lisp interpreter and load ACT-R, version 7.x

  2. Load the simon-device.lisp file first. This will load the experimental Simon task, as well as the reward learning system for Process/Don't Process productions, and a number of functions to interface the task with ACT-R and analyze the data.

  3. (Optional) if you want to run simulations, load the simon-simulations.lisp file.

  4. Finally, load the simon-model.lisp task.

  5. Run the model using ACT-R commands, e.g. (run 1000 :real-time t)

References

Stocco, A., Murray, N. L., Yamasaki, B. L., Renno, T. J., Nguyen, J., & Prat, C. S. (2017). Individual differences in the Simon effect are underpinned by differences in the competitive dynamics in the basal ganglia: An experimental verification and a computational model. Cognition, 164, 31-45.

Stocco, A. (2018). A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision‐Making Models. Cognitive Science.