Allocating Mental Effort in Cognitive Tasks: A Model of Motivation in the ACT-R Cognitive Architecture

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 proposed an elegant theory, the Expected Value of Control (EVC), 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 the 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. The motivation mechanism is shown to permit optimal effort allocation and reproduce known phenomena. Finally, the broader implications of our mechanism are discussed. BlurbWe propose to incorporate motivation into ACT-R models as a reward value associated with a goal representation. Multiple simulations show how this mechanism naturally yields flexible control allocation and reproduces a variety of psychological effects.

Automated summary

In this paper, a more detailed and fine-grained motivation framework is proposed, incorporating the principles of Expected Value of Control (EVC) into the ACT-R cognitive architecture. The implementation is tested in two models, showing that the motivation mechanism allows for optimal effort allocation and reproduces known phenomena.