A computational account of self-control

Abstract

Self-control is core to human well-being. However, the lack of a well-specified, computationally-tractable framework related to self-control makes it difficult to clarify underlying mechanisms, interpret relevant empirical phenomena, or develop interventions helpful in promoting self-control. To help address this gap we invite consideration of the Comparison with Goal States Model (CGSM) for self-control. It is composed of a synthesis between a learning element and a cybernetic element. The learning element applies an unsupervised Hebbian algorithm that translates statistical regularities in acquired semantic information into distributed representations corresponding to the available options and the relevant goals. The cybernetic element amplifies activations related to available options whose representations are similar to representations of relevant goals and diminishes activations related to available options whose representations are dissimilar to representations of relevant goals. The CGSM successfully explicates observations related to reaction time in food choice, dynamics reflected in mouse-tracking trajectories, and showcases a mechanism by which hyperbolic discount curves in temporal discounting contexts might emerge. We use the CGSM to propose theoretical constraints on the nature of self-control and describe how multiple strategies have the potential to promote self-control.

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