This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

A model of motivation and effort allocation in the ACT-R cognitive architecture

Cher Yang
University of Washington Seattle ~ Psychology
Prof. Andrea Stocco
University of Washington ~ University of Washington

Motivation is the driving force that influences people’s behaviors and interacts with many cognitive functions. Computationally, motivation is represented as a cost-benefit analysis that weighs efforts and rewards in order to choose the optimal actions. Shenhav and colleagues (2013) proposed an elegant theory, the Expected Value of Control, which describes the relationship between cognitive efforts, costs, and rewards. In this paper, we propose a more fine-grained and detailed motivation framework that incorporates the principles of EVC into the ACT-R cognitive architecture. Specifically, motivation is represented as a specific slot in Goal buffer with a corresponding scalar value, M, that is translated into the reward value Rt that is delivered when the goal is reached. This implementation is tested in two models. The first model is a high-level model that reproduces the EVC predictions with abstract actions. The second model is an augmented version of an existing ACT-R model of the Simon task, in which the motivation mechanism is shown to permit optimal effort allocation and reproduce known phenomena. Finally, the broader implications of our mechanism are discussed.



Cognitive Control
Computational Modeling
Cognitive Architecture

There is nothing here yet. Be the first to create a thread.

Cite this as:

Yang, Y., & Stocco, A. (2022, July). A model of motivation and effort allocation in the ACT-R cognitive architecture. Paper presented at Virtual MathPsych/ICCM 2022. Via